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ICNRG                                                K. Pentikousis, Ed.
Internet-Draft                                       Huawei Technologies
Intended Status: Informational                                 B. Ohlman
Expires: January 16, 2014                                       Ericsson
                                                               D. Corujo
                                                  Universidade de Aveiro
                                                               G. Boggia
                                                     Politecnico di Bari
                                                                G. Tyson
                                        Queen Mary, University of London
                                                               E. Davies
                                                  Trinity College Dublin
                                                            P. Mahadevan
                                                                    PARC
                                                               S. Spirou
                                                        Intracom Telecom
                                                             A. Molinaro
                                                                   UNIRC
                                                              D. Gellert
                                                            InterDigital
                                                                  S. Eum
                                                                    NICT
                                                           July 15, 2013


           ICN Baseline Scenarios and Evaluation Methodology
                   draft-pentikousis-icn-scenarios-04


Abstract

   This document aims at establishing a common understanding about the
   evaluation of different information-centric networking (ICN)
   approaches so that they can be tested and compared against each other
   while showcasing their own advantages.  Towards this end, we review
   the ICN literature and document scenarios which have been considered
   in previous performance evaluation studies.  We discuss a variety of
   aspects that an ICN solution can address.  This includes general
   aspects, such as, network efficiency, reduced complexity, increased
   scalability and reliability, mobility support, multicast and caching
   performance, real-time communication efficacy, energy consumption
   frugality, and disruption and delay tolerance.  We detail ICN-
   specific aspects as well, such as information security and trust,
   persistence, availability, provenance, and location independence.  We
   then survey the evaluation tools currently available to researchers
   in this area and provide suggestions regarding methodology and
   metrics.  Finally, this document sheds some light on the impact of
   ICN on network security.



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Status of this Memo

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Copyright and License Notice

   Copyright (c) 2013 IETF Trust and the persons identified as the
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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  4
   2.  Toward ICN Baseline Scenarios  . . . . . . . . . . . . . . . .  5
     2.1.  Social Networking  . . . . . . . . . . . . . . . . . . . .  5
     2.2.  Real-time Communication  . . . . . . . . . . . . . . . . .  7
     2.3.  Mobile Networking  . . . . . . . . . . . . . . . . . . . .  8
     2.4.  Infrastructure Sharing . . . . . . . . . . . . . . . . . . 10
     2.5.  Content Dissemination  . . . . . . . . . . . . . . . . . . 11
     2.6.  Vehicular Networking . . . . . . . . . . . . . . . . . . . 13
     2.7.  Multiply Connected Nodes and Economics . . . . . . . . . . 15
     2.8.  Energy Efficiency  . . . . . . . . . . . . . . . . . . . . 20
     2.9.  Delay- and Disruption-Tolerance  . . . . . . . . . . . . . 22
     2.10.  Internet of Things  . . . . . . . . . . . . . . . . . . . 27
     2.11.  Smart City  . . . . . . . . . . . . . . . . . . . . . . . 30
     2.12.  Operation across Multiple Network Paradigms . . . . . . . 31
     2.13.  Summary . . . . . . . . . . . . . . . . . . . . . . . . . 32
   3.  Evaluation Methodology . . . . . . . . . . . . . . . . . . . . 33
     3.1.  ICN Simulators and Testbeds  . . . . . . . . . . . . . . . 34
       3.1.1.  CCN and NDN  . . . . . . . . . . . . . . . . . . . . . 34
       3.1.2.  PSI  . . . . . . . . . . . . . . . . . . . . . . . . . 36
       3.1.3.  NetInf . . . . . . . . . . . . . . . . . . . . . . . . 36
       3.1.4.  COMET  . . . . . . . . . . . . . . . . . . . . . . . . 37
       3.1.5.  Large-scale Testing  . . . . . . . . . . . . . . . . . 37
     3.2.  Topology Selection . . . . . . . . . . . . . . . . . . . . 38
     3.3.  Traffic Load . . . . . . . . . . . . . . . . . . . . . . . 39
     3.4.  Choosing Relevant Metrics  . . . . . . . . . . . . . . . . 40
       3.4.1.  Traffic Metrics  . . . . . . . . . . . . . . . . . . . 43
       3.4.2.  System Metrics . . . . . . . . . . . . . . . . . . . . 44
     3.5.  Resource Equivalence and Tradeoffs . . . . . . . . . . . . 46
     3.6.  Technology Evolution Assumptions . . . . . . . . . . . . . 46
   4.  Security Considerations  . . . . . . . . . . . . . . . . . . . 46
     4.1. Authentication  . . . . . . . . . . . . . . . . . . . . . . 47
     4.2. Authorization, Access Control and Statistics  . . . . . . . 48
     4.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 49
     4.4. Changes to the Network Security Threat Model  . . . . . . . 49
   5.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 50
   6.  Acknowledgments  . . . . . . . . . . . . . . . . . . . . . . . 50
   7.  Informative References . . . . . . . . . . . . . . . . . . . . 51
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 62











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1.  Introduction

   Information-centric networking (ICN) marks a fundamental shift in
   communications and networking.  In contrast with the omnipresent and
   very successful host-centric paradigm, which is based on perpetual
   connectivity and the end-to-end principle, ICN changes the focal
   point of the network architecture from the end host to "named
   information" (or content, or data).  In this paradigm, connectivity
   may well be intermittent.  End-host and in-network storage can be
   capitalized upon transparently, as bits in the network and on storage
   devices have exactly the same value.  Mobility and multiaccess are
   the norm.  Anycast, multicast, and broadcast are natively supported,
   and energy efficiency is a design consideration from the very
   beginning.

   Although interest in ICN is growing rapidly, ongoing work on
   different architectures, such as, for example, NetInf [NetInf], CCN
   and NDN [CCN], the publish-subscribe Internet (PSI) architecture
   [PSI], and the data-oriented architecture [DONA] is far from being
   completed.  The development phase that ICN is going through and the
   plethora of approaches to tackle the hardest problems make this a
   very active and growing research area but, on the downside, it also
   makes it more difficult to compare different proposals on an equal
   footing.  This document aims to address this by establishing a common
   understanding about potential experimental setups where different ICN
   approaches can be tested and compared against each other while
   showcasing their advantages.

   Ahlgren et al. [SoA] note that describing ICN architectures is akin
   to shooting a moving target.  We find that comparing these different
   approaches is often even more tricky.  in particular, we observe that
   a variety of performance evaluation scenarios has been devised,
   typically with good reason, in order to highlight the advantages of
   each ICN architecture.  That is, there is no single scenario, use
   case, or reference topology which is employed as a benchmark
   consistently across the ICN literature.  This should be expected to
   some degree at this early stage of ICN development.  Nevertheless,
   this document shows that certain baseline scenarios seem to emerge in
   which ICN architectures could showcase their superiority over current
   systems, in general, and against each other, in particular.

   The remainder of this document is organized as follows.  In Section 2
   we review the peer-reviewed ICN literature and select prominent
   evaluation study cases as a foundation for the baseline scenarios to
   be considered by the IRTF Information-Centric Networking Research
   Group (ICNRG) in its future work.  The list of scenarios has evolved
   since the first draft version of this document based on the input
   from the research group and the corresponding text contributions.



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   Section 3 presents currently available simulation tools and
   experimental testbeds that can be used in evaluating ICN, and
   outlines the key elements that should be considered in an ICN
   evaluation.  Finally, Section 4 discusses the impact of ICN on
   network security.


2.  Toward ICN Baseline Scenarios

   This section presents a number of scenarios grouped into several
   categories.  Note that certain evaluation scenarios span across these
   categories, so the boundaries between them should not be considered
   rigid and inflexible.  There are two goals for this section. First,
   to provide a set of use cases and applications that highlight
   opportunities for testing different ICN proposals.  Second, to
   identify key attributes of a common set of techniques that can be
   instrumental in evaluating ICN.

   The overall aim is that each scenario is described at a sufficient
   level of detail so that it can serve as the base for comparative
   evaluations of different approaches.  This will need to include
   reference configurations, topologies, specifications of traffic mixes
   and traffic loads.  These specifications (or configurations) should
   preferably come as sets that describe extremes as well as "typical"
   usage scenarios.


2.1.  Social Networking

   Social networking applications have proliferated over the past decade
   based on overlay content dissemination systems that require large
   infrastructure investments to rollout and maintain.  Content
   dissemination is at the heart of the ICN paradigm and, therefore, we
   would expect that they are a "natural fit" for showcasing the
   superiority of ICN over traditional client-server TCP/IP-based
   systems.

   Mathieu et al. [ICN-SN], for instance, illustrate how an Internet
   Service Provider (ISP) can capitalize on CCN to deploy a short-
   message service akin to Twitter at a fraction of the complexity of
   today's systems.  Their key observation is that such a service can be
   seen as a combination of multicast delivery and caching.  That is, a
   single user addresses a large number of recipients, some of which
   receive the new message immediately as they are online at that
   instant, while others receive the message whenever they connect to
   the network.

   Along similar lines, Kim et al. [VPC] present an ICN-based social



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   networking platform in which a user shares content with her/his
   family and friends without the need for a centralized content server;
   see also subsection 2.4, below, and [JBDMM+12].  Based on the CCN
   naming scheme, [VPC] takes a user name to represent a set of devices
   that belong to the person.  Other users in this in-network,
   serverless social sharing scenario can access the user's content not
   via a device name/address but with the user's name.  In [VPC],
   signature verification does not require any centralized
   authentication server.  Kim and Lee [VPC2] present a proof-of-concept
   evaluation in which users with ordinary smartphones can browse a list
   of members or content using a name, and download the content selected
   from the list.

   In short, in both ICN-based social networking application scenarios
   there is no need for a classic client-server architecture (let alone
   a cloud-based infrastructure) to intermediate between content
   providers and consumers in a hub-and-spoke fashion.

   Earlier work by Arianfar et al. [ANO10] considers a similar pull-
   based content retrieval scenario using a different architecture,
   pointing to significant performance advantages.  Although the authors
   consider a  network topology (redrawn in Fig. 1 for convenience) that
   has certain interesting characteristics, they do not explicitly
   address social networking in their evaluation scenario.  Nonetheless,
   similarities are easy to spot: "followers" (such as C0, C1, ..., and
   Cz in Fig. 1) obtain content put "on the network" (I1, ..., Im, and
   B1, B2) by a single user (e.g. Px) relying solely on network
   primitives.

   \--/
   |C0|
   /--\     +--+     +--+     +--+               +--+
       *=== |I0| === |I1| ... |In|               |P0|
   \--/     +--+     +--+     +--+               +--+
   |C1|                           \             / o
   /--\                            +--+     +--+  o
    o                              |B1| === |B2|  o
    o              o o o o         +--+     +--+  o
    o                             /             \ o
    o       +--+     +--+     +--+                +--+
    o  *=== |Ik| === |Il| ... |Im|                |Px|
   \--/     +--+     +--+     +--+                +--+
   |Cz|
   /--\

   Figure 1.  Dumbbell with linear daisy chains.

   In summary, the social networking scenario aims to exercise each ICN



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   architecture in terms of network efficiency, multicast support,
   caching performance and its reliance on centralized mechanisms (or
   lack thereof).


2.2.  Real-time Communication

   Real-time audio and video (A/V) communications include an array of
   services ranging from one-to-one voice calls to multi-party multi-
   media conferences with support ranging from whiteboards to augmented
   reality.  Real-time communications have been studied and deployed in
   the context of packet- and circuit-switched networks for decades.
   The stringent quality of service requirements that this type of
   communication imposes on network infrastructure are well known.
   Since one could argue that network primitives which are excellent for
   information dissemination are not well-suited for conversational
   services, ICN evaluation studies should consider real-time
   communication scenarios in detail.

   Notably,  Jacobson et al. [VoCCN] presented an early evaluation where
   the performance of a VoIP (voice over IP) call using an information-
   centric approach was compared with that of an off-the-shelf VoIP
   implementation using RTP/UTP.  The results indicated that despite the
   extra cost of adding security support in the ICN approach,
   performance was virtually identical in the two cases evaluated in
   their testbed.  However, the experimental setup presented is quite
   rudimentary, while the evaluation considered a single voice call
   only.  Xuan and Yan [NDNpb] revisit the same scenario but are
   primarily interested in reducing the overhead that may arise in one-
   to-one communication employing an ICN architecture.  Both studies
   illustrate that quality telephony services are feasible with at least
   one ICN approach.  That said, future ICN evaluations should employ
   standardized call arrival patterns, for example, following well-
   established methodologies from the quality of service/experience
   (QoS/QoE) evaluation toolbox and would need to consider more
   comprehensive metrics.

   Given the wide-spread deployment of real-time A/V communications, an
   evaluation of an ICN system should demonstrate capabilities beyond
   feasibility.  For example, with respect to multimedia conferencing,
   Zhu et al. [ACT] describe the design of a distributed audio
   conference tool based on NDN.  Their system includes ICN-based
   conference discovery, discovery of speakers and voice data
   distribution.  The reported evaluation results point to gains in
   scalability and security.  Moreover, Chen et al. [G-COPSS] explore
   the feasibility of implementing a Massively Multiplayer Online Role
   Playing Game (MMORPG) based on CCNx and show that stringent temporal
   requirements can be met, while scalability is significantly improved



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   when compared to a host-centric (IP-based) client-server system.
   This type of work points to benefits for both the data and control
   path of a modern network infrastructure.

   Real-time communication also brings up the issue of named data
   granularity for dynamically generated content.  For instance, today
   in many cases A/V data is generated in real-time and is distributed
   immediately.  One possibility is to apply a single name to the entire
   content, but this could result in significant distribution delays.
   Alternatively, distributing the content in smaller "chunks" which are
   named individually may be a better option with respect to real-time
   distribution but raises naming scalability concerns.

   We observe that, all in all, the ICN research community has hitherto
   only scratched the surface of this area with respect to illustrating
   the benefits of adopting an information-centric approach as opposed
   to a host-centric one, and more work is recommended in this
   direction.

   In short, scenarios in this category should illustrate not only
   feasibility but reduced complexity, increased scalability,
   reliability, and capacity to meet stringent QoS/QoE requirements when
   compared to established host-centric solutions.  Accordingly, the
   primary aim of this scenario is to exercise each ICN architecture in
   terms of its ability to satisfy real-time QoS requirements and
   improved user experience.


2.3.  Mobile Networking

   IP mobility management relies on anchors to provide ubiquitous
   connectivity to end-hosts as well as moving networks.  This is a
   natural choice for a host-centric paradigm that requires end-to-end
   connectivity and a continuous network presence for hosts [SCES].  An
   implicit assumption in host-centric mobility management is therefore
   that the mobile node aims to connect to a particular peer, as well as
   to maintain global reachability and service continuity [EEMN].
   However, with ICN new ideas about mobility management should come to
   the fore capitalizing on the different nature of the paradigm.  For
   example, one could exploit the ability of nodes to better express
   their intended use of the network, i.e., the retrieval of a small
   subset of the global data corpus as discussed in [MOBSURV].

   Dannewitz et al. [N-Scen], illustrate a scenario where a multiaccess
   end-host can retrieve email securely using a combination of cellular
   and wireless local area network (WLAN) connectivity.  This scenario
   borrows elements from previous work, e.g., [DTI], and develops them
   further with respect to multiaccess.  Unfortunately, Dannewitz et al.



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   [N-Scen] do not present any results demonstrating that an ICN
   approach is, indeed, better.  That said, the scenario is interesting
   as it considers content specific to a single user (i.e., her mailbox)
   and does point to reduced complexity.  It is also compatible with
   recent work in the Distributed Mobility Management (DMM) Working
   Group within the IETF.  Finally, Xylomenos et al. [PSIMob] as well as
   [EEMN] argue that an information-centric architecture can avoid the
   complexity of having to manage tunnels to maintain end-to-end
   connectivity as is the case with mobile anchor-based protocols such
   as Mobile IP (and its variants).  Similar considerations hold for a
   vehicular (networking) environment, as we discuss in subsection 2.6
   below.

   Overall, mobile networking scenarios have not been developed in
   detail, let alone evaluated at a large scale.  Further, the majority
   of scenarios discussed so far have related to information consumer,
   rather than source, mobility.  We expect that in the coming period
   more papers will address this topic.  Earlier work [mNetInf] argues
   that for mobile and multiaccess networking scenarios we need to go
   beyond the current mobility management mechanisms in order to
   capitalize on the core ICN features.  They present a testbed setup
   (redrawn in Fig. 2) which can serve as the basis for other ICN
   evaluations.  In this scenario, node "C0" has multiple network
   interfaces that can access local domains N0 and N1 simultaneously
   allowing C0 to retrieve objects from which ever server (I2 or I3) can
   supply them without necessarily needing to access the servers in the
   core network "C" (P1 and P2).  Lindgren [Lin11] explores this
   scenario further for an urban setting.  He uses simulation and
   reports sizable gains in terms of reduction of object retrieval times
   and core network capacity use.

   +------------+      +-----------+
   | Network N0 |      | Network C |
   |            |      |           |
   | +--+       | ==== |    +--+   |
   | |I2|       |      |    |P1|   |
   | +--+       |      |    +--+   |
   |     \--/   |      |           |
   +-----|C0|---+      |           |
   |     /--\   |      |           |
   | +--+       |      |           |
   | |I3|       |      |      +--+ |
   | +--+       | ==== |      |P2| |
   |            |      |      +--+ |
   | Network N1 |      |           |
   +------------+      +-----------+

   Figure 2.  Overlapping wireless multiaccess.



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   The benefits from capitalizing on the broadcast nature of wireless
   access technologies has yet to be explored to its full potential in
   the ICN literature, including quantifying possible gains in terms of
   energy efficiency [AMR13].  Obviously, ICN architectures must avoid
   broadcast storms.  Early work in this area considers distributed
   packet suppression techniques which exploit delayed transmissions and
   overhearing; examples can be found in [MPZ10] and [OLG10] for ICN-
   based mobile ad-hoc networks (MANETs), and in [WAKVWZ12] and [ACM12]
   for vehicular scenarios.

   One would expect that mobile networking scenarios will be naturally
   coupled with those discussed in the previous sections, as more users
   access social networking and multimedia applications through mobile
   devices.  Further, the constraints of real-time A/V applications
   create interesting challenges in handling mobility, particularly in
   terms of maintaining service continuity.  This scenario therefore
   spans across most of the others considered in this document with the
   likely need for some level of integration, particularly considering
   the well-documented increases in mobile traffic.  Mobility is further
   considered in subsection 2.9 and the economic consequences of nodes
   having multiple network interfaces is explored in subsection 2.7.

   To summarize, mobile networking scenarios should aim to provide
   service continuity for those applications that require it, decrease
   complexity and control signaling for the network infrastructure, as
   well as increase wireless capacity utilization by taking advantage of
   the broadcast nature of the medium.  Beyond this, mobile networking
   scenarios should form a cross-scenario platform that can highlight
   how other scenarios can still maintain their respective performance
   metrics during periods of high mobility.


2.4.  Infrastructure Sharing

   A key idea in ICN is that the network should secure information
   objects per se, not the communications channel that they are
   delivered over.  This means that hosts attached to an information-
   centric network can share resources on an unprecedented scale,
   especially when compared to what is possible in an IP network.  All
   devices with network access and storage capacity can contribute their
   resources increasing the value of an information-centric network
   (perhaps) much faster than Metcalfe's law.

   For example, Jacobson et al. [JBDMM+12] argue that in ICN the "where
   and how" of obtaining information are new degrees of freedom.  They
   illustrate this with a scenario involving a photo sharing application
   which takes advantage of whichever access network connectivity is
   available at the moment (WLAN, Bluetooth, and even SMS) without



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   requiring a centralized infrastructure to synchronize between
   numerous devices.  It is important to highlight that since the focus
   of communication changes, keep-alives in this scenario are simply
   unnecessary, as devices participating in the testbed network
   contribute resources in order to maintain user content consistency,
   not link state information as is the case in the host-centric
   paradigm.  This means that the notion of "infrastructure" may be
   completely different in the future.

   Muscariello et al. [MCG11], for instance, presented early work on an
   analytical framework that attempts to capture the storage/bandwidth
   tradeoffs that ICN enables and can be used as foundation for a
   network planning tool.  In addition, Chai et al. [CHPP12] explore the
   benefits of ubiquitous caching throughout an information-centric
   network and argue that "caching less can actually achieve more."
   These papers also sit alongside a variety of other studies that look
   at various scenarios such as caching HTTP-like traffic [L9] and
   BitTorrent-like traffic [TKMEMT12].  We observe that much more work
   is needed in order to understand how to make optimal use of all
   resources available in an information-centric network.  In real-world
   deployments, policy and commercial considerations are also likely to
   affect the use of particular resources and more work is expected in
   this direction as well (see also subsection 2.7).

   In conclusion, scenarios in this category, would cover the
   communication-computation-storage tradeoffs that an ICN deployment
   must consider.  This would exercise features relating to network
   planning, perhaps capitalizing on user-provided resources, as well as
   operational and economical aspects to illustrate the superiority of
   ICN over other approaches.  An obvious baseline to compare against in
   this regard is existing federations of IP-based Content Distribution
   Networks (CDNs).


2.5.  Content Dissemination

   Content dissemination has attracted more attention than other aspects
   of ICN, perhaps due to a misunderstanding of what the first "C" in
   CCN stands for.  Scenarios in this category abound in the literature,
   including stored and streaming A/V distribution, file distribution,
   mirroring and bulk transfers, versioned content services (c.f.,
   Subversion-type revision control), as well as traffic aggregation.

   Decentralized content dissemination with on-the-fly aggregation of
   information sources was envisaged in [N-Scen], where information
   objects can be dynamically assembled based on hierarchically
   structured subcomponents.  For example, a video stream could be
   associated with different audio streams and subtitle sets, which can



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   all be obtained from different sources.  Using the topology depicted
   in Fig. 1 as an example, an application at C1 may end up obtaining,
   say, the video content from I1, but the user-selected subtitles from
   Px.  Semantics and content negotiation, on behalf of the user, were
   also considered, e.g., for the case of popular tunes which may be
   available in different encoding formats.  Effectively this scenario
   has the information consumer issuing independent requests for content
   based on information identifiers, and stitching the pieces together
   irrespective of "where" or "how" they were obtained.

   A case in point for content dissemination are vehicular ad-hoc
   networks (VANETs), as an ICN approach may address their needs for
   information dissemination between vehicles better than today's
   solutions, as discussed in the following subsection.  The critical
   part of information dissemination in a VANET scenario revolves around
   "where" and "when".  For instance, one may be interested in traffic
   conditions 2 km ahead while having no interest in similar information
   about the area around the path origin.  VANET scenarios may provide
   fertile ground for showcasing the ICN advantage with respect to
   content dissemination especially when compared with current host-
   centric approaches.  That said, information integrity and filtering
   are challenges that must be addressed.  As mentioned earlier, content
   dissemination scenarios in VANETs have a particular affinity to the
   mobility scenarios discussed earlier.

   Content dissemination scenarios, in general, have a large overlap
   with those described in the previous sections and are explored in
   several papers, such as [DONA] [PSI] [PSIMob] [NetInf] [CCN]
   [JBDMM+12] [ANO10], just to name a few.  In addition, Chai et al.
   [CURLING] present a hop-by-hop hierarchical content resolution
   approach, which employs receiver-driven multicast over multiple
   domains, advocating another content dissemination approach.  Yet,
   largely, work in this area did not address the issue of access
   authorization in detail.  Often, the distributed content is mostly
   assumed to be freely accessible by any consumer.  Distribution of
   paid-for or otherwise restricted content on a public ICN network
   requires more attention in the future.  Fotiou et al. [FMP12]
   consider a scheme to this effect but it still requires access to an
   authorization server to verify the user's status after the
   (encrypted) content has been obtained.  This may effectively negate
   the advantage of obtaining the content from any node, especially in a
   disruption-prone or mobile network.

   In summary, scenarios in this category aim to exercise primarily
   scalability, cost and performance attributes of content
   dissemination.  Particularly, they should highlight the ability of an
   ICN to scale to billions of objects, while not exceeding the cost of
   existing content dissemination solutions (i.e., CDNs) and, ideally,



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   increasing performance.  These should be shown in a holistic manner,
   improving content dissemination for both information consumers and
   publishers of all sizes.  We expect that in particular for content
   dissemination, both extreme as well as typical scenarios can be
   specified drawing data from current CDN deployments.


2.6.  Vehicular Networking

   Users "on wheels" are interested in road safety, traffic efficiency,
   and infotainment applications that can be supported through vehicle-
   to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless
   communications.  These applications exhibit unique features in terms
   of traffic generation patterns, delivery requirements, spatial and
   temporal scope, which pose great challenges to traditional networking
   solutions.  VANETs, by their nature, are characterized by challenges
   such as fast-changing topology, intermittent connectivity, high node
   mobility, but also by the opportunity to combine information from
   different sources as each vehicle does not care about "who" delivers
   the named data objects.

   ICN is an attractive candidate solution for vehicular networking, as
   it has several advantages.  First, ICN fits well to the nature of
   typical vehicular applications that are geography- and time-dependent
   (e.g., road traveler information, accident warning, point-of-interest
   advertisements) and usually target vehicles in a given area,
   regardless of their identity or IP address.  These applications are
   likely to benefit from in-network and decentralized data caching and
   replication mechanisms.  Second, content caching is particularly
   beneficial for intermittent on-the-road connectivity and can speed up
   data retrieval through content replication in several nodes.  Caching
   can usually be implemented at relatively low cost in vehicles as the
   energy demands of the ICN device are likely to be a negligible
   fraction of the total vehicle energy consumption, thus allowing for
   sophisticated processing, continuous communication and adequate
   storage in the vehicle.  Finally, ICN natively supports asynchronous
   data exchange between end-nodes.  By using (and redistributing)
   cached named information objects, a mobile node can serve as a link
   between disconnected areas.  In short, ICN can enable communication
   even under intermittent network connectivity, which is typical of
   vehicular environments with sparse roadside infrastructure and fast
   moving nodes.

   The advantages of ICN in vehicular networks were preliminarily
   discussed in [BK10] and [DMND], and additionally investigated in
   [WWKVZ12] [WAKVWZ12] [AKH11] [TL12] [ACM12] [CRoWN].  For example,
   Bai and Krishnamachari [BK10] take advantage of the localized and
   dynamic nature of a VANET to explore how a road congestion



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   notification application can be implemented.  Wang et al. [DMND]
   consider data collection where Road-Side Units (RSUs) collect
   information from vehicles by broadcasting NDN-like INTEREST packets.
   The proposed architecture is evaluated using simulation in a grid
   topology and is compared against a host-centric alternative based on
   Mobile IP.  See Fig. 3 for an indicative example of an urban VANET
   topology.  Their results indicate high efficiency for ICN even at
   high speeds.  That said, the authors point out that as this work is a
   preliminary exploration of ICN in vehicular environments, many issues
   remain to be evaluated, such as system scalability to large numbers
   of vehicles and the impact of vehicles forwarding Interests and
   relaying data for other vehicles.

      + - - _- - -_- - - -_- - _- - - +
      |    /_\   /_\     /_\  /_\     |
      |    o o   o o     o o  o o     |
      |    +-------+     +-------+ _  |
      |    |       |     |       |/_\ |
      |  _ |       |     |       |o o |
      | /_\|       |    |       |     |
      | o o+--_----+\===/+--_----+    |
      |      /_\    |RSU|  /_\        |
      |      o o    /===\  o o        |
      |    +-------+     +-------+ _  |
      |    |       |     |       |/_\ |
      | _  |       |     |       |o o |
      |/_\ |       |     |       |    |
      |o o +_-----_+     +_-----_+    |
      |    /_\   /_\     /_\   /_\    |
      +_ _ o_o_ _o_o_ _ _o_o_ _o_o_ _ +

   Figure 3.  Urban grid VANET topology.

   As mentioned in the previous section, due to the short communication
   duration between a vehicle and the RSU, and the typically short time
   of sustained connectivity between vehicles, VANETs may be a good
   showcase for the ICN advantages with respect to content
   dissemination.  Wang et al. [WWKVZ12], for instance, analyze the
   advantages of hierarchical naming for vehicular traffic information
   dissemination.  Arnould et al. [AKH11] apply ICN principles to safety
   information dissemination between vehicles with multiple radio
   interfaces.  In [TL12], TalebiFard and Leung use network coding
   techniques to improve content dissemination over multiple ICN paths.
   Amadeo et al. [ACM12][[CRoWN] propose an application-independent ICN
   framework for content retrieval and distribution where the role of
   provider can be played equivalently by both vehicles and RSUs.  ICN
   forwarding is extended through path-state information carried in
   Interest and Data packets, stored in a new data structure kept by



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   vehicular nodes, and exploited also to cope with node mobility.

   Typical scenarios for testing content distribution in VANETs may be
   highways with vehicles moving in straight lines, with or without RSUs
   along the road, as shown in Fig. 4.  With a NDN approach in mind, for
   example, RSUs may send Interests to collect data from vehicles
   [DMND], or vehicles may send Interests to collect data from other
   peers [WAKVWZ12] or from RSUs [ACM12].  Fig. 2 applies to content
   dissemination in VANET scenarios as well, where C0 represents a
   vehicle which can obtain named information objects via multiple
   wireless peers and/or RSUs (I2 and I3 in the figure).  Grid
   topologies such as the one illustrated in Fig. 3 should be considered
   in urban scenarios with RSUs at the crossroads, or co-located with
   traffic lights as in [CRoWN].

        \__/                    \__/
        |RU|                    |RU|
      ================================
           _     _     _     _
          /_\   /_\   /_\   /_\
      _ _ o_o_ _o_o_ _o_o_ _o_o_ _ _ _
           _     _     _     _
          /_\   /_\   /_\   /_\
          o o   o o   o o   o o
      ================================

   Figure 4.  Highway VANET topology.

   To summarize, VANET scenarios aim to exercise ICN deployment from
   various perspectives, including scalability, caching, transport, and
   mobility issues.  There is a need for further investigation in (i)
   challenging scenarios (e.g., disconnected segments);  (ii) scenarios
   involving both consumer and provider mobility;  (iii) smart caching
   techniques which take into consideration node mobility patterns,
   spatial and temporal relevance, content popularity, and social
   relationships between users/vehicles;  (iv) identification of new
   applications (beyond data dissemination and traffic monitoring) that
   could benefit from the adoption of an ICN paradigm in vehicular
   networks (e.g., mobile cloud, social networking).


2.7.  Multiply Connected Nodes and Economics

   The evolution of, in particular, wireless networking technologies has
   resulted in a convergence of the bandwidth and capabilities of
   various different types of networks.  Today a leading edge mobile
   telephone or tablet computer will typically be able to access a Wi-Fi
   access point, a 4G cellular network and the latest generation of



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   Bluetooth local networking.  Until recently a node would usually have
   a clear favorite network technology appropriate to any given
   environment.   The choice would, for example, be primarily determined
   by the available bandwidth with cost as a secondary determinant.
   Furthermore, it is normally the case that a device only uses one of
   the technologies at a time for any particular application.

   It seems likely that this situation will change so that nodes are
   able to use all of the available technologies in parallel.  This will
   be further encouraged by the development of new capabilities in
   cellular networks including Small Cell Networks (SCN) and
   Heterogeneous Networks (HetNet).  Consequently, mobile devices will
   have similar choices to wired nodes attached to multiple service
   providers allowing "multi-homing" via the various different
   infrastructure networks as well as potential direct access to other
   mobile nodes via Bluetooth or a more capable form of ad hoc Wi-Fi.

   Infrastructure networks are generally under the control of separate
   economic entities that may have different policies about the
   information of an ICN deployed within their network caches.  As ICN
   shifts the focus from nodes to information objects, the interaction
   between networks will likely evolve to capitalize on data location
   independence, efficient and scalable in-network named object
   availability and access via multiple paths.  These interactions
   become critical in evaluating the technical and economic impact of
   ICN architectural choices, as noted in [ArgICN].  Beyond simply
   adding diversity in deployment options, these networks have the
   potential to alter the incentives among existing, and future, we may
   add, network players, as noted in [EconICN].

   Moreover, such networks enable more numerous inter-network
   relationships where exchange of information may be conditioned on a
   set of multilateral policies.  For example, shared SCNs are emerging
   as a cost-effective way to address coverage of complex environments
   such as sports stadiums, large office buildings, malls, etc. [OptSC]
   [FEMTO].  Such networks are likely to be a complex mix of different
   cellular and WLAN access technologies (such as HSPA, LTE, and Wi-Fi)
   as well as ownership models.  It is reasonable to assume that access
   to content generated in such networks may depend on contextual
   information such as the subscription type, timing, and location of
   both the owner and requester of the content.  The availability of
   such contextual information across diverse networks can lead to
   network inefficiencies unless data management can benefit from an
   information-centric approach.  The "Event with Large Crowds"
   demonstrator created by the SAIL project investigated this kind of
   scenario; more details are available in [SAIL-B3].

   Jacobson et al. [CCN] include interactions between networks in their



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   overall system design, and mention both "an edge-driven, bottom-up
   incentive structure" and techniques based on evolutions of existing
   mechanisms both for ICN router discovery by the end-user and for
   interconnecting between autonomous systems (AS).  For example, a BGP
   extension for domain-level content prefix advertisement can be used
   to enable efficient interconnection between AS's.  Liu et al. [MLDHT]
   proposed to address the "suffix-hole" issue found in prefix-based
   name aggregation through the use of a combination of Bloom-filter
   based aggregation and multi-level DHT.

   Name aggregation has been discussed for a flat naming design as well
   in [NCOA], which also notes that based on estimations in [DONA] flat
   naming may not require aggregation.  This is a point that calls for
   further study.  Scenarios evaluating name aggregation, or lack
   thereof, should take into account the amount of state (e.g. size of
   routing tables) maintained in edge routers as well as network
   efficiency (e.g. amount of traffic generated).

                 +---------------+
     +---------->| Popular Video |
     |           +---------------+
     |             ^           ^
     |             |           |
     |           +-+-+ $0/MB +-+-+
     |           | A +-------+ B |
     |           ++--+       +-+-+
     |            | ^         ^ |
     |      $8/MB | |         | | $10/MB
     |            v |         | v
   +-+-+  $0/MB  +--+---------+--+
   | D +---------+       C       |
   +---+         +---------------+

   Figure 5.  Relationships and transit costs between networks A to D.

   DiBenedetto et al. [RP-NDN] study policy knobs made available by NDN
   to network operators.  New policies, which are not feasible in the
   current Internet are described, including a "cache sharing peers"
   policy, where two peers have an incentive to share content cached in,
   but not originating from, their respective network.  The simple
   example used in the investigation considers several networks and
   associated transit costs, as shown in Fig. 5. (based on Fig. 1 of
   [RP-NDN]).  Agyapong and Sirbu [EconICN] further establish that ICN
   approaches should incorporate features that foster (new) business
   relationships.  For example, publishers should be able to indicate
   their willingness to partake in the caching market, proper reporting
   should be enabled to avoid fraud, and content should be made
   cacheable as much as possible to increase cache hit ratios.



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   Ahlgren et al. [SAIL-B3] enable network interactions in the NetInf
   architecture using a name resolution service at domain edge routers,
   and a BGP-like routing system in the NetInf Default Free Zone.
   Business models and incentives are studied in [SAIL-A7] and [SAIL-
   A8], including scenarios where the access network provider (or a
   virtual CDN) guarantees QoS to end users using ICN.  Fig. 6
   illustrates a typical scenario topology from this work which involves
   an interconnectivity provider.

   +----------+     +-----------------+     +------+
   | Content  |     | Access Network/ |     | End  |
   | Provider +---->|  ICN Provider   +---->| User |
   +----------+     +-+-------------+-+     +------+
                      |             |
                      |             |
                      v             v
   +-------------------+     +----------------+       +------+
   | Interconnectivity |     | Access Network |       | End  |
   |     Provider      +---->|     Provider   +------>| User |
   +-------------------+     +----------------+       +------+

   Figure 6.  Setup and operating costs of network entities

   Jokela et al. [LIPSIN] propose a two-layer approach where additional
   rendezvous systems and topology formation functions are placed
   logically above multiple networks and enable advertising and routing
   content between them.  Visala et al. [LANES] further describe an ICN
   architecture based on similar principles, which, notably, relies on a
   hierarchical DHT-based rendezvous interconnect.  Rajahalme et al.
   [PSIRP1] describe a rendezvous system using both a BGP-like routing
   protocol at the edge and a DHT-based overlay at the core.  Their
   evaluation model is centered around policy-compliant path stretch,
   latency introduced by overlay routing, caching efficacy, and overlay
   routing node load distribution.

   Rajahalme et al. [ICCP] point out that ICN architectural changes may
   conflict with the current tier-based peering model.  For example,
   changes leading to shorter paths between ISPs are likely to meet
   resistance from Tier-1 ISPs.  Rajahalme [IDMcast] shows how
   incentives can help shape the design of specific ICN aspects, and in
   [IDArch] he presents a modeling approach to exploit these incentives.
    This includes a network model which describes the relationship
   between Autonomous Systems based on data inferred from the current
   Internet, a traffic model taking into account business factors for
   each AS, and a routing model integrating the valley-free model and
   policy-compliance.  A typical scenario topology is illustrated in
   Fig. 7, which is redrawn here based on Fig. 1 of [ICCP].  Note that
   it relates well with the topology illustrated in Fig. 1 of this



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   document.

                        o-----o
                  +-----+  J  +-----+
                  |     o--*--o     |
                  |        *        |
               o--+--o     *     o--+--o
               |  H  +-----------+  I  |
               o-*-*-o     *     o-*-*-o
                 * *       *       * *
            ****** ******* * ******* *******
            *            * * *             *
         o--*--o        o*-*-*o         o--*--o
         |  E  +--------+  F  +---------+  G  +
         o-*-*-o        o-----o         o-*-*-o
           * *                            * *
      ****** *******                 ****** ******
      *            *                 *           *
   o--*--o      o--*--o           o--*--o     o--*--o
   |  A  |      |  B  +-----------+  C  |     |  D  |
   o-----o      o--+--o           o--+--o     o----+o
                   |                 |         ^^  | route
             data  |            data |    data ||  | to
                   |                 |         ||  | data
               o---v--o          o---v--o     o++--v-o
               | User |          | User |     | Data |
               o------o          o------o     o------o

   Legend:
   *****  Transit link
   +---+  Peering link
   +--->  Data delivery or route to data

   Figure 7.  Tier-based set of interconnections between AS A to J.

   To sum up, the evaluation of ICN architectures across multiple
   network types should include a combination of technical and economic
   aspects, capturing their various interactions.  These scenarios aim
   to illustrate scalability, efficiency and manageability, as well as
   traditional and novel network policies.   Moreover, scenarios in this
   category should specifically address how different actors have proper
   incentives, not only in a pure ICN realm, but also during the
   migration phase towards this final state.








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2.8.  Energy Efficiency

   Advantage can be taken of some prominent ICN features to
   significantly reduce the energy footprint of future communication
   networks.  A simple example of a potential energy-saving operation is
   caching.  If a data object can be retrieved from within a network,
   rather than from a distant origin server, clearly, significant
   amounts of energy expenditure can be saved by avoiding several
   further hops.  Alternatively, approaches that aim to simplify
   routers, such as [PURSUIT], could also reduce energy consumption by
   pushing routing decisions into more energy-efficient data centers.

   We elaborate on the energy efficiency potential of ICN based on three
   categories of ICN characteristics.  Namely, we point out that a) ICN
   does not rely solely on end-to-end communication, b) ICN enables
   ubiquitous caching, and c) ICN brings awareness of user requests (as
   well as their corresponding responses) at the network layer thus
   permitting network elements to better schedule their transmission
   patterns.

   First, ICN does not mandate perpetual end-to-end communication, which
   introduces a whole range of energy consumption inefficiencies due to
   the extensive signaling, especially in the case of mobile and
   wirelessly connected devices.  This opens up new opportunities for
   accommodating sporadically connected nodes and could be one of the
   keys to an order of magnitude decrease in energy consumption.  For
   example, web applications often need to maintain state at both ends
   of a connection in order to verify that the authenticated peer is up
   and running.  This introduces keep-alive timers and polling behavior
   with a high toll on energy consumption.  Pentikousis [EEMN] discusses
   several related scenarios and explains why the current host-centric
   paradigm, which employs end-to-end always-on connections, introduces
   built-in energy inefficiencies argueing that patches to make
   currently deployed protocols energy-aware cannot provide for an order
   of magnitude increase in energy efficiency.

   Second, ICN network elements come with built-in caching capabilities,
   which is often referred to as ubiquitous caching.  Pushing data
   objects to caches closer to end user devices, for example, could
   significantly reduce the amount of transit traffic in the core
   network, thereby reducing the energy used for data transport.  Guan
   et al. [EECCN] study the energy efficiency of CCNx (based on their
   proposed energy model) and compare it with conventional content
   dissemination systems such as CDNs and P2P.  Their model is based on
   the analysis of the topological structure and the average hop-length
   from all consumers to the nearest cache location.  Their results show
   that ICN can be more energy efficient in delivering popular content.
   In particular, they also note that different network element design



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   choices (e.g. the optical bypass approach) can be more energy-
   efficient in delivering infrequently accessed content.

   Lee et al. [EECD] investigate the energy efficiency of various
   network devices deployed in access, metro, and core networks for both
   CDNs and ICN.  They use trace-based simulations to show that an ICN
   approach can substantially improve the network energy efficiency for
   content dissemination mainly due to the reduction in the number of
   hops required to obtain a data object, which can be served by
   intermediate nodes in ICN.  They also emphasize that the impact of
   cache placement (in incremental deployment scenarios) and
   local/cooperative content replacement strategies need to be carefully
   investigated in order to better quantify the energy efficiencies
   arising from adopting an ICN paradigm.

   Third, as mentioned earlier, energy efficiency can be tackled by
   different ICN approaches in ways that it cannot in a host-centric
   paradigm.  We already mentioned that in ICN, perpetual (always-on)
   connectivity is not necessary, therefore mechanisms that capitalize
   on powering down network interfaces are easier to accommodate.  Since
   all ICN elements are aware of the user request and its corresponding
   data response, due to the nature of name-based routing, they can
   employ power consumption optimization processes for determining their
   transmission schedule.  For example, network coding [NCICN] or
   adaptive video streaming [COAST] can be used in individual ICN
   elements so that redundant transmissions, possibly passing through
   intermediary networks, could be significantly reduced, thereby saving
   energy by avoiding to carry redundant traffic.

   Alternatively, approaches that aim to simplify routers could also
   reduce energy consumption by pushing routing decisions to a more
   energy-efficient entity.  Along these lines, Ko et al. [ICNDC] design
   a data center network architecture based on ICN principles and
   decouple the router control-plane and data-plane functionalities.
   Thus, data forwarding is performed by simplified network entities
   while the complicated routing computation is carried out in more
   energy-efficient data centers.

   To summarize, energy efficiency has been discussed in ICN evaluation
   studies but most published work is preliminary in nature.  Thus, we
   suggest that more work is needed in this front.  Evaluating energy
   efficiency does not require the definition of new scenarios or
   baseline topologies, but does require the establishment of clear
   guidelines so that different ICN approaches can be compared not only
   in terms of scalability, for example, but also in terms to power
   consumption.





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2.9.  Delay- and Disruption-Tolerance

   Delay- and Disruption-Tolerant Networking (DTN) [DTN] [TBB13]
   originated as a means to extend the Internet to interplanetary
   communications.  However, it was subsequently found to be an
   appropriate architecture for many terrestrial situations as well.
   Typically, this was where delays were greater than protocols such as
   TCP could handle, and where disruptions to communications were the
   norm rather than occasional annoyances, e.g., where an end-to-end
   path does not necessarily exist when communication is initiated.  DTN
   has now been applied to many situations, including opportunistic
   content sharing, handling infrastructural issues during emergency
   situations (e.g., earthquakes) and providing connectivity to remote
   rural areas without existing Internet provision and little or no
   communications or power infrastructure.

   The DTN architecture [RFC4838] is based on a "store, carry and
   forward" paradigm that has been applied extensively to situations
   where data is carried between network nodes by a "data mule", which
   carries bundles of data stored in some convenient storage medium
   (e.g., a USB memory stick).  With the advent of sensor and peer-to-
   peer (P2P) networks between mobile nodes, DTN is becoming a more
   commonplace type of networking than originally envisioned.  Since ICN
   also does not rely on the familiar end-to-end communications
   paradigm, there are, thus, clear synergies [DTN].  It could therefore
   be argued that many of the key principles embodied within DTN also
   exist in ICN, as we explain next.

   First, both approaches rely on in-network storage.  In the case of
   DTN, bundles are stored temporarily on devices on a hop-by-hop basis.
    In the case of ICN, information objects are also cached on devices
   in a similar fashion.  As such, both paradigms must provision storage
   within the network.

   Second, both approaches espouse late binding of names to locations
   due to the potentially large interval between request and response
   generation.  In the case of DTN, it is often impossible to predict
   the exact location (in a disconnected topology) where a node will be
   found.  Similarly, in the case of ICN, it is also often impossible to
   predict where an information object might be found.  As such, the
   binding of a request/bundle to a destination (or routing locator)
   must be performed as late as possible.

   Third, both approaches treat data as a long-lived component that can
   exist in the network for extended periods of time.  In the case of
   DTN, bundles are carried by nodes until appropriate next hops are
   discovered.  In the case of ICN, information objects are typically
   cached until storage is exhausted.  As such, both paradigms require a



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   direct shift in the way applications interact with the network.

   Through these similarities, it becomes possible to identify many DTN
   principles that are already in existence within ICN architectures.
   For example, ICN nodes will often retain publications locally, making
   them accessible later on, much as DTN bundles are handled.
   Consequently, these synergies suggest strong potential for marrying
   the two technologies.  This, for instance, could include building new
   integrated Information-Centric Delay Tolerant Network (ICDTN)
   protocols or, alternatively, building ICN schemes over existing DTN
   protocols (or vice versa).

   The above similarities suggest that integration of the two principles
   would be certainly feasible.  Beyond this, there are also a number of
   direct benefits identifiable.  Through caching and replication, ICN
   offers strong information resilience, whilst, through store-and-
   forward, DTN offers strong connectivity resilience.  As such, both
   architectures could benefit greatly from each other.  Initial steps
   have already been taken in the DTN community to integrate ICN
   principles, e.g., the Bundle Protocol Query Block [BPQ] has been
   added to the DTN Bundle Protocol [RFC5050].  Whilst, similarly,
   initial steps have also been taken in the ICN community, such as
   [SLINKY].  In fact, the SAIL project has recently developed a
   prototype implementation of NetInf running over the DTN Bundle
   Protocol.

   For the purpose of evaluating the use of ICNs in a DTN setting, two
   key scenarios are identified in this document (note the rest of this
   section uses the term ICDTN).  These are both prominent use cases
   that are currently active in both the ICN and DTN communities.  The
   first is opportunistic content sharing, whilst the second is the use
   of ad hoc networks during disaster recovery (e.g., earthquakes).
   These are discussed in the context of a simulation-based evaluation;
   due to the scale and mobility of DTN-like setups, this is the primary
   method of evaluation used.  Within the DTN community, the majority of
   simulations are performed using the Opportunistic Network Environment
   (ONE) simulator [ONE], which is referred to in this document.  Before
   exploring the two scenarios, the key shared components of their
   simulation are discussed.  This is separated into the two primary
   inputs that are required: the environment and the workload.

   In the case of both scenarios, the environment can be abstractly
   modeled by a time series of active connections between device pairs.
   Unlike other scenarios in this document, an ICDTN scenario does not
   depend on (relatively) static topologies but, rather, a set of time-
   varying disconnected topologies.  In opportunistic networks, these
   topologies are actually products of the mobility of users.  For
   example, if two users walk past each other, an opportunistic link can



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   be created.  There are two methods used to generate these mobility
   patterns and, in turn, the time series of topologies.  The first is
   synthetic, whereby a (mathematical) model of user behavior is created
   in an agent-based fashion, e.g., random waypoint, Gauss-Markov.  The
   second is trace-driven, whereby the mobility of real users is
   recorded and used.  In both cases, the output is a sequence of time-
   stamped "contacts", i.e. a period of time in which two devices can
   communicate.  An important factor missing from typical mobility
   traces, however, is the capacity of these contacts: how much data can
   be transferred?  In both approaches to modeling mobility, links are
   usually configured as Bluetooth or WiFi (ONE easily allows this,
   although lower layer considerations are ignored, e.g., interference).
    This is motivated by the predominance of these technologies on
   mobile phones.

   The workload in an ICDTN is modeled much like the workload within the
   other scenarios.  It involves object creation/placement and object
   retrieval.  Object creation/placement can either be done statically
   at the beginning of the simulations or, alternatively, dynamically
   based on a model of user behavior.  In both cases, the latter is
   focused on, as it models far better the characteristics of the
   scenarios.

   Once the environment and workload has been configured, the next step
   is to decide the key metrics for the study.  Unlike traditional ICN,
   the quality of service expectations are typically far lower in an
   ICDTN, thereby moving away from metrics such as throughput.  At a
   high-level, it is of clear interest to evaluate different ICN
   approaches with respect to both their delay- and disruption-
   tolerance, i.e., how effective is the approach when used in an
   environment subject to significant delay and/or disruption; and to
   their active support for operations in a DTN environment.

   The two most prominent metrics considered in a host-centric DTN are
   delivery probability and delivery delay.  The former relates to the
   probability that a sent message will be received within a certain
   delay bound, whilst the latter captures the average length of time it
   takes for nodes to receive the message.  These metrics are similarly
   important in an ICDTN, although they are slightly different due to
   the request-response nature of ICN.  Therefore, the two most
   prominent evaluative metrics are:

    o Satisfaction Probability: The probability by which an information
      request (e.g., Interest) will be satisfied (i.e., how often a Data
      response will be received).

    o Satisfaction Delay: The length of time it takes an information
      request to be satisfied.



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   Note that the key difference between the host-centric and
   information-centric metrics is the need for a round-trip rather than
   a one-way communication.  Beyond this, depending on the focus of the
   work, other elements that may be investigated include name
   resolution, routing and forwarding in disconnected parts of the
   network; support for unidirectional links; number of round trips
   needed to complete a data transfer; long-term content availability
   (or resilience); efficiency in the face of disruption, and so on.  It
   is also important to weigh these performance metrics against the
   necessary overheads. In the case of an ICDTN, this is generally
   measured by the number of message replicas required to access content
   (note that routing in a DTN is often replication-based, which leads
   to many copies of the same message).

   The first key baseline scenario in this context is opportunistic
   content sharing.  This occurs when mobile nodes create opportunistic
   links between each other to share content of interest.  For example,
   this might occur on an underground train, in which people pass news
   items between their mobile phones.  Equally, content generated on the
   phones (e.g., tweets [TWIMIGHT]) could be stored for later forwarding
   (or even forwarded amongst interested passengers on the train).  Such
   networks are often termed pocket-switched networks, as they are
   independently formed between the user devices.  Here, the evaluative
   scenario of ICDTN microblogging is proposed.  As previously
   discussed, the construction of such an evaluative scenario requires a
   formalization of its environment and workload.  Luckily, there exist
   a number of datasets that offer exactly this information required for
   microblogging.

   In terms of the environment (i.e., mobility patterns), the Haggle
   project produced contact traces based on conference attendees using
   Bluetooth.  These traces are best targeted at application scenarios
   in which a small group of (50-100) people are in a relatively
   confined space.  In contrast, larger scale traces are also available,
   most notably MIT's Reality Mining project.  These are better suited
   for cases where longer-term movement patterns are of interest.

   The second input, workload, relates to the creation and consumption
   of microblogs (e.g. tweets).  This can be effectively captured
   because subscriptions conveniently formalize who consumes what.  For
   bespoke purposes, specific data can be directly collected from
   Twitter for trace-driven simulations.  Several Twitter datasets are
   already available to the community containing a variety of data,
   ranging from Tweets to follower graphs.  Sources include:
      http://www.tweetarchivist.com/
      http://twapperkeeper.com/
      http://www.infochimps.com/collections/twitter-census
      http://socialcomputing.asu.edu/datasets/Twitter



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   These datasets can therefore be used to extract information
   production, placement and consumption.

   The second key baseline scenario in this context relates to the use
   of ICDTNs in emergency scenarios.  In these situations it is typical
   for infrastructure to be damaged or destroyed, leading to the
   collapse of traditional forms of communications (e.g., cellular
   telephone networks).  This has been seen in the recent North India
   flooding, as well as the 2011 Tohoku earthquake and tsunami.  Power
   problems often exacerbate the issue, with communication problems
   lasting for days.  Therefore, in order to address this, DTNs have
   been used due to their high levels of resilience and independence
   from fixed infrastructure.  The most prominent use of DTNs in
   disaster areas would be the dissemination of information, e.g.,
   warnings and evacuation maps.  Here, we focus on the dissemination of
   standard broadcast information that should be received by all
   parties.

   For the environmental setup, there are no commonly used mobility
   traces for disaster zones, unlike in the previous evaluative
   scenario.  This is clearly due to the difficultly (near
   impossibility) of acquiring them in a real setting.  That said,
   various synthetic models are available.  The Post Disaster Mobility
   Model [MODEL1] models civilians and emergency responders after a
   disaster has occurred, with people attempting to reach evacuation
   points (this has also been implemented in ONE).  [MODEL2] focuses on
   emergency responders, featuring the removal of nodes from the
   disaster zone, as well as things like obstacles (e.g. collapsed
   buildings).  [MODEL3] also looks at emergency responders, but focuses
   on patterns associated with common procedures.  For example, command
   and control centers are typically set up with emergency responders
   periodically returning.  Clearly, the mobility of emergency
   responders is particularly important in this setting because they
   usually are the ones who will "carry" information into the disaster
   zone.  It is recommended that one of these emergency-specific models
   are used during any evaluations, due to the inaccuracy of alternate
   models used for "normal" behavior.

   The workload input in this scenario is far simpler than for the
   previous scenario.  In emergency cases, the dissemination of
   individual pieces of information to all parties is the norm.  This is
   often embodied using things like the Common Alert Protocol (CAP).  As
   such, small objects (e.g. 512KB to 2MB) are usually generated
   containing text and images; note that the ONE simulator offers
   utilities to easily generate these.  These messages are also always
   generated by central authorities, therefore making the placement
   problem easier (they would be centrally generated and given to
   emergency responders to disseminate as they pass through the disaster



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   zone).  The key variable is therefore the generation rate, which is
   synonymous with the rate that microblogs are written in the previous
   scenario.  This will largely be based on the type of disaster
   occurring, however, hourly updates would be an appropriate
   configuration.  Higher rates can also be tested, based on the rate at
   which situations change (lands slides, for example, can exhibit
   highly dynamic properties).

   To summarize, this section has highlighted the applicability of ICN
   principles to existing DTN scenarios. Two evaluative setups have been
   described in detail, namely, mobile opportunistic content sharing
   (microblogging) and emergency information dissemination.


2.10.  Internet of Things

   Advances in electronics miniaturization combined with low-power
   wireless access technologies (e.g., ZigBee, NFC, Bluetooth and
   others) have enabled the coupling of interconnected digital services
   with everyday objects.  As devices with sensors and actuators connect
   into the network, they become "smart objects" and form the foundation
   for the so-called Internet of Things (IoT).  IoT is expected to
   increase significantly the amount of content carried by the network
   due to machine-to-machine (M2M) communication as well as novel user
   interaction possibilities.

   Yet, the full potential of IoT does not lie in simple remote access
   to smart object data.  Instead, it is the intersection of Internet
   services with the physical world that will bring about the most
   dramatic changes.  Burke [IoTEx], for instance, makes a very good
   case for creating everyday experiences using interconnected things
   through participatory sensing applications.  In this case, inherent
   ICN capabilities for data discovery, caching, and trusted
   communication are leveraged to obtain sensor information and enable
   content exchange between mobile users, repositories, and
   applications.

   Kutscher and Farrell [IWMT] discuss the benefits that ICN can provide
   in these environments in terms of naming, caching, and optimized
   transport.  The Named Information URI scheme (ni) [RFC6920], for
   instance, could be used for globally unique smart object
   identification, although an actual implementation report is not
   currently available.  Access to information generated by smart
   objects can be of varied nature and often vital for the correct
   operation of large systems.  As such, supporting timestamping,
   security, scalability, and flexibility need to be taken into account.

   Ghodsi et al. [NCOA] examine hierarchical and self-certifying naming



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   schemes and point out that ensuring reliable and secure content
   naming and retrieval may pose stringent requirements (e.g., the
   necessity for employing PKI), which can be too demanding for low-
   powered nodes, such as sensors.  That said, earlier work by Heidemann
   et al. [nWSN] shows that, for dense sensor network deployments,
   disassociating sensor naming from network topology and using named
   content at the lowest level of communication in combination with in-
   network processing of sensor data is feasible in practice and can be
   more efficient than employing a host-centric binding between node
   locator and the content existing therein.

   Burke et al. [NDNl] describe the implementation of a lighting control
   building automation system where the security, naming and device
   discovery NDN mechanisms are leveraged to provide configuration,
   installation and management of residential and industrial lighting
   control systems.  The goal is an inherently resilient system, where
   even smartphones can be used for control. Naming reflects fixtures
   with evolved identification and node reaching capabilities thus
   simplifying bootstrapping, discovery, and user interaction with
   nodes.  The authors report that this ICN-based system requires less
   maintenance and troubleshooting than typical IP-based alternatives.

   Biswas et al. [CIBUS] visualize ICN as a contextualized information-
   centric bus (CIBUS) over which diverse sets of service producers and
   consumers co-exist with different requirements.  ICN is leveraged to
   unify different platforms to serve consumer-producer interaction in
   both infrastructure and ad hoc settings.  Ravindran et al. [Homenet],
   show the application of this idea in the context of a home network,
   where consumers (residents) require policy-driven interactions with
   diverse services such as climate control, surveillance systems, and
   entertainment systems.  Name-based protocols are developed to enable
   zero-configuration node and service discovery, contextual service
   publishing and subscription, policy-based routing and forwarding with
   name-based firewall, and hoc device-to-device communication.

   IoT exposes ICN concepts to a stringent set of requirements which are
   exacerbated by the amount of nodes, as well as by the type and volume
   of information that must be handled.  A way to address this is
   proposed in [IoTScope], which tackles the problem of mapping named
   information to an object, diverting from the currently typical
   centralized discovery of services and leveraging the intrinsic ICN
   scalability capabilities for naming.  It extends the base [PURSUIT]
   design with hierarchically-based scopes, facilitating lookup, access,
   and modifications of only the part of the object information that the
   user is interested in.  Another important aspect is how to
   efficiently address resolution and location of the information
   objects, particularly when large numbers of nodes are connected, as
   in IoT deployments.  In [ICN-DHT], Katsaros et al. propose a



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   Distributed Hash Table (DHT) which is compared with DONA [DONA].
   Their results show how topological routing information has a positive
   impact on resolution, at the expense of memory and processing
   overhead.

   The use of ICN mechanisms is IoT scenarios faces the most dynamic and
   heterogeneous type of challenges, when taking into consideration the
   requirements and objectives of such integration.  The disparity in
   technologies (not only in access technologies, but also in terms of
   end-node diversity such as sensors, actuators and their
   characteristics) as well as in the information that is generated and
   consumed in such scenarios, will undoubtedly bring about many of the
   considerations presented in the previous sections.  For instance, IoT
   shares similarities with the constraints and requirements applicable
   to vehicular networking.  Here, a central problem is the deployment
   of mechanisms that can use opportunistic connectivity in unreliable
   networking environments (similarly to the vehicular networking and
   DTN scenarios).

   However, one important concern in IoT scenarios, also motivated by
   this strongly heterogeneous environment, is how content dissemination
   will be affected by the different semantics of the disparate
   information and content being shared.  In fact, this is already a
   difficult problem that goes beyond the scope of ICN [SEMANT].  With
   the ability of the network nodes to cache forwarded information to
   improve future requests, a challenge arises regarding whether the ICN
   fabric should be involved in any kind of procedure (e.g., tagging)
   that facilitates the relationship or the interpretation of the
   different sources of information.

   Another issue lies with the need for having energy-efficiency
   mechanisms related to the networking capabilities of IoT
   infrastructures.  Often, the devices in IoT deployments have limited
   battery capabilities, and thus need low power consumption schemes
   working at multiple levels.  In principle, energy efficiency gains
   should be observed from the inherent in-network caching capability.
   However, this might not be the most usual case in IoT scenarios,
   where the information (particularly from sensors, or controlling
   actuators) is more akin to real-time traffic, thus reducing the scale
   of potential savings due to ubiquitous in-network caching.

   ICN approaches, therefore, should be evaluated with respect to their
   capacity to handle the content produced and consumed by extremely
   large numbers of diverse devices.  IoT scenarios aim to exercise ICN
   deployment from different aspects, including ICN node design
   requirements, efficient naming, transport, and caching of time-
   restricted data.  Scalability is particularly important in this
   regard as the successful deployment of IoT principles could expand



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   both device and content numbers dramatically beyond all current
   expectations.


2.11.  Smart City

   The rapid increase in urbanization sets the stage for the most
   compelling and challenging environments for networking.  By 2050 the
   global population will reach nine billion people, 75% of which will
   dwell in urban areas.  In order to cope with this influx, many cities
   around the world have started their transformation toward the Smart
   City vision.  Smart cities will be based on the following innovation
   axes: smart mobility, smart environment, smart people, smart living,
   and smart governance.  In development terms, the core goal of a smart
   city is to become a business-competitive and attractive environment,
   while serving citizen well being [CPG].

   In a smart city, ICT plays a leading role and acts as the glue
   bringing together all actors, services, resources (and their
   interrelationships), that the urban environment is willing to host
   and provide [MVM].  ICN appears particularly suitable for these
   scenarios.  Domains of interest include intelligent transportation
   systems, energy networks, health care, A/V communications, peer-to-
   peer and collaborative platforms for citizens, social inclusion,
   active participation in public life, e-government, safety and
   security, sensor networks.  Clearly, this scenario has close ties to
   the vision of IoT, discussed in the previous subsection, as well as
   to vehicular networking.

   Nevertheless, the road to build a real information-centric digital
   ecosystem will be long and more coordinated effort is required to
   drive innovation in this domain.  We argue that smart city needs and
   ICN technologies can trigger a virtuous innovation cycle toward
   future ICT platforms.  Recent concrete ICN-based contributions have
   been formulated for home energy management [iHEMS], geo-localized
   services [ACC], smart city services [IB], and traffic information
   dissemination in vehicular scenarios [WAKVWZ12].  Some of the
   proposed ICN-based solutions are implemented in real testbeds while
   others are evaluated through simulation.

   Zhang et al. [iHEMS] propose a secure publish-subscribe architecture
   for handling the communication requirements of Home Energy Management
   Systems (HEMS).  The objective is to safely and effectively collect
   measurement and status information from household elements, aggregate
   and analyze the data, and ultimately enable intelligent control
   decisions for actuation.  They consider a simple experimental test-
   bed for their proof-of-concept evaluation, exploiting open source
   code for the ICN implementation, and emulating some node



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   functionality in order to facilitate system operation.

   A different scenario is considered in [ACC], where DHTs are employed
   for distributed, scalable, and geographically-aware service lookup in
   a smart city.  Also in this case, the ICN application is validated by
   considering a small-scale testbed: a small number of nodes are
   realized with simple embedded PCs or specific hardware boards (e.g.,
   for some sensor nodes); other nodes realizing the network connecting
   the principal actors of the tests are emulated with workstations.
   The proposal in [IB] draws from a smart city scenario (mainly
   oriented towards waste collection management) comprising sensors and
   moving vehicles, as well as a cloud computing system that supports
   data retrieval and storage operations.  The main aspects of this
   proposal are analyzed via simulation using open source code which is
   publicly available.  Some software applications are designed on real
   systems (e.g., PCs and smartphones).

   To sum up, smart city scenarios aim to exercise several ICN aspects
   in an urban environment.  In particular, they can be useful to (i)
   analyze the capacity of using ICN for managing extremely large data
   sets; (ii) study ICN performance in terms of scalability in
   distributed services; (iii) verify the feasibility of ICN in a very
   complex application like vehicular communication systems; and (iv)
   examine the possible drawbacks related to privacy and security issues
   in complex networked environments.


2.12.  Operation across Multiple Network Paradigms

   Today the overwhelming majority of networks are integrated with the
   well-connected Internet with IP at the "waist" of the technology
   hourglass.  However there is a large amount of ongoing research into
   alternative paradigms that can cope with conditions other than the
   standard set assumed by the Internet.  Perhaps the most advanced of
   these is Delay- and Disruption-Tolerant Networking (DTN).  DTN is
   considered as one of the scenarios for the deployment in
   subection 2.9 but here we consider how ICN can operate in an
   integrated network that has essentially disjoint "domains" (a highly-
   overloaded term!) or regions that use different network paradigms and
   technologies, but with gateways that allow interoperation.

   ICN operates in terms of named data objects so that requests and
   deliveries of information objects can be independent of the
   networking paradigm.  Some researchers have contemplated some form of
   ICN becoming the new waist of the hourglass as the basis of a future
   reincarnation of the Internet, e.g., [ArgICN], but there are a large
   number of problems to resolve, including authorization and access
   control (see subsection 4.2) and transactional operation for



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   applications such as banking, before some form of ICN can be
   considered as ready to take over from IP as the dominant networking
   technology.  In the meantime, ICN architectures will operate in
   conjunction with existing network technologies as an overlay or in
   cooperation with the lower layers of the "native" technology.

   It seems likely that as the reach of the "Internet" is extended,
   other technologies such as DTN will be needed to handle scenarios
   such as space communications where inherent delays are too large for
   TCP/IP to cope with effectively.  Thus, demonstrating that ICN
   architectures can work effectively in and across the boundaries of
   different networking technologies will be important.  The NetInf
   architecture in particular targets the inter-domain scenario by the
   use of a convergence layer architecture [SAIL-B3] and PSIRP/PURSUIT
   is envisaged as a candidate for an IP replacement.

   The key items for evaluation over and above the satisfactory
   operation of the architecture in each constituent domain will be to
   ensure that requests and responses can be carried across the network
   boundaries with adequate performance and do not cause malfunctions in
   applications or infrastructure because of the differing
   characteristics of the gatewayed domains.


2.13.  Summary

   We conclude Section 2 with a brief summary of the evaluation aspects
   we have seen across a range of scenarios.

   The scalability of different mechanisms in an ICN architecture stands
   out as an important concern (cf. subsections 2.1-2, 2.5-7, 2.10-11,)
   as does network, resource and energy efficiency (cf. subsections 2.1,
   2.3-4, 2.7-8).  Operational aspects such as network planing,
   manageability, reduced complexity and overhead (cf. subsection 2.2-4,
   2.7, 2.10) should not be neglected especially as ICN architectures
   are evaluated with respect to their potential for deployment in the
   real world.  Accordingly, further research in economic aspects as
   well as in the communication, computation, and storage tradeoffs
   entailed in each ICN architecture is needed.

   With respect to purely technical requirements, support for multicast,
   mobility, and caching lie at the core of many scenarios (cf.
   subsections 2.1, 2.3, 2.5-6).  ICN must also be able to cope when the
   Internet expands to incorporate additional network paradigms (cf.
   subsection 2.12).  We have also seen that being able to address
   stringent QoS requirements and increase reliability and resilience
   should also be evaluated following well-established methods (cf.
   subsections 2.2, 2.9-10).



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   Finally, we note that new applications that significantly improve the
   end user experience and forge a migration path from today's host-
   centric paradigm could be the key to a sustained and increasing
   deployment of the ICN paradigm in the real world (cf. subsections
   2.2-3, 2.6, 2.10-11).


3.  Evaluation Methodology

   As we have seen in the previous section, different ICN approaches
   have been evaluated in the peer-reviewed literature using a mixture
   of theoretical analysis, simulation and emulation techniques, and
   empirical (testbed) measurements.  These are all popular methods for
   evaluating network protocols, architectures, and services in the
   networking community.  Typically, researchers follow a specific
   methodology based on the goal of their experiment, e.g., whether they
   want to evaluate scalability, quantify resource utilization, analyze
   economic incentives, and so on, as we have discussed earlier.  In
   addition, though, we observe that ease and convenience of setting up
   and running experiments can sometimes be a factor in published
   evaluations.

   It is worth pointing out that for well-established protocols, such as
   TCP, performance evaluation using actual network deployments has the
   benefit of realistic workloads and reflects the environment where the
   service or protocol will be deployed.  However, results obtained in
   this environment are often difficult to replicate independently.
   Beyond this, the difficulty of deploying future Internet
   architectures and then engaging sufficient users to make such
   evaluation realistic is often prohibitive.

   Moreover, for ICN in particular, it is not yet clear what qualifies
   as a "realistic workload".  As such, trace-based analysis of ICN is
   in its infancy, and more work is needed towards defining
   characteristic workloads for ICN evaluation studies.  Accordingly,
   the experimental process itself as well as the evaluation methodology
   are being actively researched for ICN architectures.   Numerous
   factors affect the experimental results, including the topology
   selected; the background traffic that an application is being
   subjected to; network conditions such as available link capacities,
   link delays, and loss-rate characteristics throughout the selected
   topology; failure and disruption patterns; node mobility; as well as
   other aspects such as the diversity of devices used, and so on, as we
   explain in the remainder of this section.

   Apart from the technical evaluation of the functionality of an ICN
   architecture, its future success will be largely driven by its
   deployability and economic viability.  Thus evaluations should also



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   include an assessment of incremental deployability in the existing
   network environment together with a view of how the technical
   functions will incentivize deployers to invest in the capabilities
   that allow the architecture to spread across the network.

   In this section, we present various techniques and considerations for
   evaluating different ICN architectures.  We do not intend to develop
   a complete methodology or a benchmarking tool.  Instead, this
   document proposes key guidelines alongside suggested data sets and
   high-level approaches that we expect to be of interest to ICNRG and
   the ICN community as a whole.  Through this, researchers and
   practitioners alike would be able to compare and contrast different
   ICN designs against each other, and identify the respective strengths
   and weaknesses.


3.1.  ICN Simulators and Testbeds

   Since ICN is still an emerging area, the community is still in the
   process of developing effective evaluation environments, including
   simulators, emulators, and testbeds.  To date, none of the available
   evaluation methodologies can be seen as the one and only community
   reference evaluation tool.  Furthermore, no single environment
   supports all well-known ICN approaches.  Simulators and emulators
   should be able to capture, faithfully, all features and operations of
   the respective ICN architecture(s).  It is also essential that these
   tools and environments come with adequate logging facilities so that
   one can use them for in-depth analysis as well as debugging.
   Additional requirements include the ability to support mid- to large-
   scale experiments, the ability to quickly and correctly set various
   configurations and parameters, as well as to support the playback of
   traffic traces captured on a real testbed or network. Obviously, this
   does not even begin to touch upon the need for strong validation of
   any evaluated implementations.

   The rest of this subsection summarizes the ICN simulators and
   testbeds currently available to the community.


3.1.1.  CCN and NDN

   The CCN project has open-sourced a software reference implementation
   of the architecture and protocol called CCNx (www.ccnx.org).  CCNx is
   available for deployment on various operating systems and includes C
   and Java libraries that can be used to build CCN applications. CCN-
   lite (www.ccn-lite.net) is a lightweight implementation of the CCN
   protocol, supports most of the key features of CCNx, and is
   interoperable with CCNx.  The core CCNx logic has been implemented in



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   about 1000 lines of code and is ideal for classroom work and course
   projects as well as for quickly experimenting with CCNx extensions.

   ndnSIM [ndnSIM] is a module that can be plugged into the ns-3
   simulator and supports the core features of CCN.  One can use ndnSIM
   to experiment with various CCN applications and services as well as
   components developed for CCN such as routing protocols, caching and
   forwarding strategies.  The code for ns-3 and ndnSIM is openly
   available to the community and can be used as the basis for
   implementing ICN protocols or applications.  For more details see
   http://www.nsnam.org and http://www.ndnsim.net.

   ccnSim [ccnSim] is another CCN-specific simulator that was specially
   designed to handle forwarding of a large number of CCN-chunks.
   ccnSim is written in C++ for the OMNeT++ simulation framework
   (www.omnetpp.org).  Interested readers could consider also the
   Content Centric Networking Packet Level Simulator [CCNPL].  Finally,
   CCN-Joker [CGB12] is an application-layer platform that can be used
   to build a CCN overlay.  CCN-Joker emulates in user-space all basic
   aspects of a CCN node (e.g., handling of Interest and Data packets,
   cache sizing, replacement policies), including both flow and
   congestion control.  The code is open source and is suitable for both
   emulation-based analyses and real experiments.

   An example of a testbed that supports CCN is the Open Network Lab
   (see https://onl.wustl.edu/).  The ONL testbed currently comprises 18
   extensible gigabit routers and over 100 computers representing
   clients and is freely available to the public for running CCN
   experiments.  Nodes in ONL are preloaded with CCNx software.  ONL
   provides a graphical user interface for easy configuration and
   testbed set up as per the experiment requirements, and also serves as
   a control mechanism, allowing access to various control variables and
   traffic counters.  It is also possible to run and evaluate CCN over
   popular testbeds such as PlanetLab (www.planet-lab.org), Emulab
   (www.emulab.net), and Deter (www.isi.deterlab.net) by directly
   running the CCNx open-source code on PlanetLab and Deter nodes,
   respectively.

   NEPI, the Network Experimentation Programming Interface,
   (http://nepi.inria.fr) is a tool developed for controlling and
   managing large-scale network experiments.  NEPI provides an
   experiment description language to design network experiments,
   describing topology, applications, and a controller to automatically
   deploy those experiments on target experimentation environments, such
   as PlanetLab. The controller is also capable of collecting result and
   log files during the experiment execution.  NEPI also allows to
   specify node selection filters while designing the experiment,
   thereby supporting automatic discovery and provisioning of testbed



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   nodes during experiment deployment, without the user having to hand-
   pick them.  It is simple and efficient to use NEPI to evaluate CCNx
   on large-scale testbeds such as PlanetLab.


3.1.2.  PSI

   The PSIRP project has open-sourced its Blackhawk publish-subscribe
   (Pub/Sub) implementation for FreeBSD; more details are available
   online at http://www.psirp.org/downloads.html.  Despite being limited
   to one operating system, the code base also provides a virtual image
   to allow its deployment on other environments through virtualization.
    The code distribution features a kernel module, a file system and
   scope daemon, as well as a set of tools, test applications and
   scripts.  This work was extended as part of the PURSUIT project,
   resulting in the development of the Blackadder prototype for Linux
   and FreeBSD.  It currently runs on a testbed across Europe, America
   (MIT) and Japan (NICT).  All sites are connected via OpenVPN, which
   exports a virtual Ethernet device to all machines in the testbed.  In
   total, 40 machines in a graph topology containing one Topology
   Manager and one Rendezvous node that handle all publish/subscribe and
   topology formation requests are interconnected [PTA13].

   Moreover, the ICN simulation environment [VBYR12] allows the
   simulation of new techniques for topology management following the
   Publish-Subscribe paradigm and the PSIRP approach. The simulator is
   based on the OMNET++ simulator and the INET/MANET frameworks. It is
   currently publicly available at
   http://sourceforge.net/projects/icnsim.  A design characteristic of
   this platform is the separation between the network and topology
   management policies.  An interface is used to provide this
   functionality and policies can be imported and applied in the network
   as topology manager applications running on top of this interface.


3.1.3.  NetInf

   The EU FP7 4WARD and SAIL projects have made a set of open-source
   implementations available; see http://www.netinf.org/open-source for
   more details.  Of note, two software packages are available.  The
   first one is a set of tools for NetInf implementing different aspects
   of the protocol (e.g., NetInf URI format, HTTP and UDP convergence
   layer) using different programming languages.  The Java
   implementation provides a local caching proxy and client.  The second
   one, is a OpenNetInf prototype from the 4WARD project.  Besides a
   rich set of NetInf mechanisms implemented, it also provides a browser
   plug-in and video streaming software.




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   The SAIL project developed a hybrid host-centric and information-
   centric network architecture called the Global Information Network
   (GIN).  The prototype code for this can be downloaded from
   http://gin.ngnet.it.


3.1.4.  COMET

   The EU FP7 COMET project developed a simulator, called Icarus, which
   implements ProbCache [PCP12], centrality-based in-network caching
   [CHPP12] and the hash-route-based algorithms detailed in [SPP13].
   The simulator is built in Python and makes use of the Fast Network
   Simulator Setup tool [SCP13] to configure the related parameters of
   the simulation. The simulator is available from:
   https://github.com/lorenzosaino/icarus/


3.1.5.  Large-scale Testing

   An important consideration in the evaluation of any kind of future
   Internet mechanism, lies in the characteristics of that evaluation
   itself.  Often, central to the assessment of the features provided by
   a novel mechanism, lies the consideration of how it improves over
   already existing technologies, and by "how much."  With the
   disruptive nature of clean-slate approaches generating new and
   different technological requirements, it is complex to provide
   meaningful results for a network layer framework, in comparison with
   what is deployed in the current Internet.  Thus, despite the
   availability of ICN implementations and simulators, the need for
   large-scale environments supporting experimental evaluation of novel
   research is of prime importance to the advancement of ICN deployment.

   In this regard, initiatives such as the Future Internet Research and
   Experimentation Initiative (www.ict-fire.eu), enable researchers to
   test new protocols and architectures in real conditions over
   production networks (e.g., through virtualization and software-
   defined networking mechanisms), simplifying the validation of future
   evolutions and reducing the gap between research and deployment.
   Similarly, Future Internet Design (www.nets-find.net) is a long-term
   initiative along the same direction in the US.  GENI (www.geni.net)
   also offers experimentation infrastructure as does PlanetLab
   (www.planet-lab.org), which likely offers the largest testbed
   available today.  Those wishing to perform smaller, more controlled
   experiments can also consider the Emulab testbed (www.emulab.net),
   which allows various topologies to be configured.

   Finally, the National Institute of Information and Communications
   Technology (NICT) builds and operates the high-performance testbed



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   JGN-X (see http://www.jgn.nict.go.jp/english/index.html), which has
   cutting-edge network functions and technologies including those
   currently in development.  JGN-X aims to establish new-generation
   network technology and accelerate the R&D in areas such as network
   virtualization and advanced operations of virtualized layers.  JGN-X
   is used for collaboration among developers in order to foster the
   establishment and expansion of new-generation network technology.


3.2.  Topology Selection

   Section 2 introduced several topologies that have been used in ICN
   studies so far but, to date and to the best of our understanding,
   there is no single topology that can be used to easily evaluate all
   aspects of the ICN paradigm.  There is rough consensus that the
   classic dumbbell topology cannot serve well future evaluations of ICN
   approaches.  Therefore, one should consider a range of topologies,
   each of which would stress different aspects, as outlined earlier in
   this document.  Current Internet traces are also available to assist
   in this, e.g. see http://www.caida.org/data/active/internet-topology-
   data-kit and
   http://www.cs.washington.edu/research/networking/rocketfuel.

   Depending on what is the focus of the evaluation, intra-domain
   topologies alone may be appropriate.  However, those interested, for
   example, in quantifying transit costs will require inter-domain
   traces (note that the above CAIDA traces offer this).  Scalability is
   an important aspect in such an evaluation.  For instance, CAIDA's
   ITDK traces record millions of routers across thousands of domains.

   Beyond these "real-world" traces there is a wide range of synthetic
   topologies, such as the Barabasi-Albert model [BA99] and the Watts-
   Strogatz small-world topology [WS98].  These synthetic traces allow
   experiments to be performed whilst controlling various key parameters
   (e.g. degree).  Through this, different aspects can be investigated,
   such as inspecting resilience properties.  For some lines of ICN
   research, this may be more appropriate as, practically speaking,
   there are no assurances that a future ICN will share the same
   topology with today's networks.

   Besides defining the evaluation topology as a graph G = (V,E), where
   V is the set of vertices (nodes) and E is the set of edges (links),
   one should also clearly define and list the respective matrices that
   correspond to the network, storage and computation capacities
   available at each node as well as the delay characteristics of each
   link, so that the results obtained can be easily replicated in other
   studies.  Recent work by Hussain and Chen [Montage], although
   currently addressing host-centric networks, could also be leveraged



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   and be extended by the ICN community.  Measurement information can
   also be taken from existing platforms such as iPlane
   (http://iplane.cs.washington.edu), which can be used to provide
   configuration parameters such as access link capacity and delay.
   Alternatively, synthetic models such as [KPLKTS09] can be used to
   configure such topologies.

   Finally, the dynamic aspects of a topology, such as node and content
   mobility, disruption patterns, packet loss rates as well as link and
   node failure rates, to name a few, should also be carefully
   considered.  As mentioned in subsection 2.9, for example, contact
   traces from the DTN community could also be used in ICN evaluations.


3.3.  Traffic Load

   As we are still lacking ICN-specific traffic workloads we can
   currently only extrapolate from today's workloads.  In this
   subsection we provide a first draft of a set of common guidelines, in
   the form of what we will refer to as a content catalog for different
   scenarios.  This catalog, which is based on previously published
   work, could be used to evaluate different ICN proposals, for example,
   on routing, congestion control, and performance, and can be
   considered as other kinds of ICN contributions emerge.

   We take scenarios from today's Web, file sharing (BitTorrent-like)
   and User Generated Content (UGC) platforms (e.g., YouTube), as well
   as Video on Demand (VoD) services.  Publicly available traces for
   these include those available from web sites such as
   http://mikel.tlm.unavarra.es/~mikel/bt_pam2004,
   http://multiprobe.ewi.tudelft.nl/multiprobe.html,
   http://an.kaist.ac.kr/traces/IMC2007.html, and
   http://traces.cs.umass.edu/index.php/Network/Network.

   The content catalog for each type of traffic can be characterized by
   a specific set of parameters: the cardinality of the estimated
   content catalog, the average size of the exchanged contents (either
   chunks or entire named information objects), and the statistical
   distribution that best reflect the popularity of objects and their
   request frequency.  Table I summarizes such as content catalog.  With
   this shared point of reference, the use of the same set of parameters
   (depending on the scenario of interest) among researchers will be
   eased, and different proposals could be compared on a common base.

   Several previous studies have stated that Zipf's law is the discrete
   distribution that best represents the request frequency in a number
   of application scenarios, ranging from typical Web access to VoD
   services.  The key aspect of this distribution is that the frequency



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   of a content request is inversely proportional to the rank of the
   content itself, i.e., the smaller the rank, the higher the request
   frequency.  If we denote with M the content catalog cardinality and
   with 1 <= i <= M the rank of the i-th most popular content, we can
   express the probability of requesting the content with rank "i" as:

   P(X=i) = ( 1/i^(alpha) ) / C, with C = SUM(1 / j^(alpha)), alpha > 0

   where the sum is obtained considering all values of j, 1 <= j <= M.

   Table I.  Content Catalog

   Traffic | Catalog |  Mean Object Size  |  Popularity Distribution
    Load   |  Size   |  [L4][L5][L7][L8]  |  [L3][L5][L6][L11][L12]
           | [L1][L2]|  [L9][L10]         |
           | [L3][L5]|                    |
   ==================================================================
   Web     |  10^12  | Chunk: 1-10 kB     | Zipf with
           |         |                    | 0.64 <= alpha <= 0.83
   ------------------------------------------------------------------
   File    | 5x10^6  | Chunk: 250-4096 kB | Zipf with
   sharing |         | Object: ~800 MB    | 0.75 <= alpha<= 0.82
   ------------------------------------------------------------------
   UGC     |  10^8   | Object: ~10 MB     | Zipf, alpha >= 2
   ------------------------------------------------------------------
   VoD     |  10^4   | Object: ~100 MB    | Zipf, 0.65 <= alpha <= 1
   ==================================================================

   * UGC = User Generated Content ** VoD = Video on Demand

   Further, a variation of the Zipf distribution, termed the Mandelbrot-
   Zipf distribution, has been suggested by [SH06] to better model
   environments where nodes can locally store previously requested
   content.  For example, it was observed that peer-to-peer file sharing
   applications typically exhibited a 'fetch-at-most-once' style of
   behavior.  This is because peers tend to persistently store the files
   they download, a behavior that may also be prevalent in ICN.


3.4.  Choosing Relevant Metrics

   ICN is a networking concept that spun out of the desire to align the
   operation model of a network with the model of its typical use.  For
   TCP/IP networks, this means a fundamental change in data access and
   transport mechanisms from a host-to-host model to a user-to-
   information model.  The premise is that the effort invested in
   changing models will be offset, or even surpassed, by the potential
   of a "better" network.  However, such a claim can be validated only



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   if it is quantified.

   Quantification of network performance requires a set of standard
   metrics.  These metrics should be broad enough so that they can be
   applied equally to host-centric and information-centric (or other)
   networks.  This will allow reasoning about a certain ICN approach in
   relation to an earlier version of the same approach, to a different
   ICN approach, and to the incumbent host-centric approach.  It will
   therefore be less difficult to gauge optimization and research
   direction.  On the other hand, metrics should be targeted to network
   performance primarily and should avoid unnecessary expansion into the
   physical and application layers.  Similarly, at this point, it is
   more important to capture as metrics only the main figures of merit
   and to leave more esoteric and less frequent cases for the future.

   To arrive at a set of relevant metrics, it would be beneficial to
   look at the metrics considered in previously published evaluations
   for several ICN approaches, such as CCN [CCN] [VoCCN] [NDNProj];
   NetInf [4WARD6.1] [4WARD6.3] [SAIL-B2] [SAIL-B3]; PURSUIT [PRST4.5],
   COMET [CMT-D5.2] [CMT-D6.2]; Connect [MCG11] [RealCCN]; and
   CONVERGENCE [ICN-Web] [ICN-Scal] [ICN-Tran].  The metrics used in
   these studies fall into two categories: metrics for the approach as a
   whole, and metrics for individual components (resolution, routing,
   etc.).  Metrics for the entire approach are further subdivided into
   traffic and system metrics.

   It is important to note that sometimes ICN approaches do not name or
   define metrics consistently.  This is a major problem when trying to
   find metrics that allow comparison between approaches.  For the
   purposes of exposition, in what follows we have tried to smooth
   differences by pitting similarly defined metrics under the same name.
   Also, due to space constraints, we have chosen to report here only
   the most common metrics between approaches.  For more details the
   reader should consult the references provided in this document for
   each approach.

   Traffic metrics in existing ICN approaches are summarized in Table
   II. These metrics capture mainly the perspective of the end user,
   i.e., the consumer, provider, or owner of the content or service.
   Depending on the level where these metrics are measured, we have made
   the distinction into user, application and network-level traffic
   metrics. So, for example, network-level metrics are mostly focused on
   packet characteristics, whereas user-level metrics can cover elements
   of human perception.  The approaches do not make this distinction
   explicitly, but we can see from the table that CCN and NetInf have
   used metrics from all levels, PURSUIT and COMET have focused on
   lower-level metrics, and Connect and CONVERGENCE prefer higher-level
   metrics.  Throughput and download time seem to be the most popular



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   metrics altogether.

   Table II.  Traffic metrics used in ICN evaluation studies

                   User   |    Application    |        Network
               ======================================================
                 Download | Goodput | Startup | Throughput |  Packet
                   time   |         | latency |            |  delay
   ==================================================================
   CCN         |    x     |    x    |         |      x     |    x
   ------------------------------------------------------------------
   NetInf      |    x     |         |    x    |      x     |    x
   ------------------------------------------------------------------
   PURSUIT     |          |         |    x    |      x     |    x
   ------------------------------------------------------------------
   COMET       |          |         |    x    |      x     |
   ------------------------------------------------------------------
   Connect     |    x     |         |         |            |
   ------------------------------------------------------------------
   CONVERGENCE |    x     |    x    |         |            |
   ==================================================================

   While traffic metrics are more important for the end user, the owner
   or operator of the networking infrastructure is normally more
   interested in system metrics, which can reveal the efficiency of an
   approach.  Although different ICN approaches have used system metrics
   in their respective evaluation studies, the situation is not as
   coherent as with the traffic metrics.  The most common system metrics
   used are: protocol overhead, total traffic, transit traffic, cost
   savings, router cost, and router energy consumption.

   Besides traffic and systems metrics that aim to evaluate an approach
   as a whole, all of the surveyed approaches also evaluate the
   performance of individual components.  Name resolution, request/data
   routing, and data caching are the most typical components, so Table
   III presents the popular metrics for each of those components.  FIB
   size and path length, i.e., the routing component metrics, are almost
   ubiquitous between different studies, perhaps due to the networking
   background of the involved researchers.  That might be also the
   reason for the sometimes decreased focus on traffic and system
   metrics, in favor of component metrics.  It can certainly be argued
   that traffic and system metrics are affected by component metrics,
   however no approach has made the relationship clear.  With this in
   mind, and also taking into account that traffic and system metrics
   are readily useful to end users and network operators, we will
   restrict ourselves to those in the following sections.





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   Table III.  Component metrics in current ICN evaluations

                      Resolution      |    Routing    |    Cache
               ======================================================
                 Resolution | Request | FIB  |  Path  | Size |  Hit
                    time    |  rate   | size | length |      | ratio
   ==================================================================
   CCN         |     x      |         |  x   |   x    |   x  |   x
   ------------------------------------------------------------------
   NetInf      |     x      |    x    |      |   x    |      |   x
   ------------------------------------------------------------------
   PURSUIT     |            |         |  x   |   x    |      |
   ------------------------------------------------------------------
   COMET       |     x      |    x    |  x   |   x    |      |   x
   ------------------------------------------------------------------
   CONVERGENCE |            |    x    |  x   |        |   x  |
   ==================================================================

   Before proceeding, we should note that we would like our metrics to
   be applicable to host-centric networks as well.  Standard metrics
   already exist for IP networks and it would certainly be beneficial to
   take them into account.  It is encouraging that many of the metrics
   used by existing ICN approaches can also be used on IP networks and
   that all of the approaches have tried on occasion to draw the
   parallels.


3.4.1.  Traffic Metrics

   At their core, host-centric and information-centric networking
   function as data transport services.  Information of interest to a
   user resides in one or more storage points connected to the network
   and, on the user's request, the network transports this information
   to the user for consumption.  We could therefore quantify the data
   transport performance of the network in terms of well-established
   Quality of Service (QoS) metrics.

   The IETF has been working for more than a decade on devising metrics
   and methods for measuring the performance of IP networks.  The work
   has been carried out largely within the IPPM WG, guided by a relevant
   framework [RFC2330].  IPPM metrics include delay, delay variation,
   loss, reordering, and duplication.  While the IPPM work is certainly
   based on packet-switched IP networks, it is conceivable that it can
   be modified and extended to cover ICN networks as well.  However,
   more study is necessary to turn this claim into a certainty.  Many
   experts have toiled for a long time on devising and refining the IPPM
   metrics and methods, so it would be an advantage to use IPPM on
   measuring ICN performance.  In addition, IPPM works already for host-



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   centric networks, so comparison with information-centric networks
   would entail only the ICN extension of the IPPM framework.  Finally,
   an important benefit of measuring the transport performance of a
   network at its output, using QoS metrics such as IPPM, is that it can
   be done mostly without any dependence on particular applications.

   Another option for measuring transport performance would be to use
   QoS metrics, not at the output of the network like with IPPM, but at
   the input to the application.  So for an application like live video
   streaming, the relevant metrics would be startup latency, playout lag
   and playout continuity.  The benefit of this approach is that it
   abstracts away all details of the underlying transport network, so it
   can be readily applied to compare networks of different architecture
   (host-centric, information-centric, or other).  As implied earlier,
   the drawback of this approach is its dependence on the application,
   so it is likely that different (types of) applications will require
   different metrics.  It might be possible to identify standard metrics
   for each type of application, but the situation is not as clear as
   with IPPM metrics and further investigation is necessary.

   At a higher level of abstraction, we could measure the network's
   transport performance at the application output.  This entails
   measuring the quality of the transported and reconstructed
   information as perceived by the user during consumption.  In such an
   instance we would use Quality of Experience (QoE) metrics, which are
   by definition dependent on the application.  For example, the
   standardized methods for obtaining a Mean Opinion Score (MOS) for
   VoIP (e.g., ITU-T P.800) is quite different from those for IPTV
   (e.g., PEVQ).  These methods are notoriously hard to implement, as
   they involve real users in a controlled environment.  Such
   constraints can be relaxed or dropped by using methods that model
   human perception under certain environments, but these methods are
   typically intrusive.  The most important drawback of measuring
   network performance at the output of the application is that only one
   part of each measurement is related to network performance.  The rest
   is related to application performance, e.g., video coding, or even
   device capabilities, both of which are irrelevant to our purposes
   here and are generally hard to separate.  We therefore see the use of
   QoE metrics in measuring ICN performance as a poor choice at this
   stage.


3.4.2.  System Metrics

   Overall system metrics that need to be considered include
   reliability, scalability, energy efficiency, and delay/disconnection
   tolerance.  In deployments where ICN is addressing specific
   scenarios, relevant system metrics could be derived from current



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   experience.  For example, in IoT scenarios, which were discussed
   earlier in subsection 2.10, it is reasonable to consider the current
   generation of sensor nodes, sources of information, and even
   measurement gateways (e.g., for smart metering at homes) or
   smartphones.  In this case, ICN operation ought to be evaluated with
   respect not only to overall scalability and network efficiency, but
   also the impact on the nodes themselves.  Karnouskos et al. [KVHHM11]
   provide a comprehensive set of sensor and IoT-related requirements,
   for example, which include aspects such as resource utilization,
   service life-cycle management and device management.

   Additionally, various specific metrics are critical in constrained
   environments, such as CPU processing requirements, signaling
   overhead, and memory allocation for caching procedures in addition to
   power consumption and battery lifetime.  For gateway nodes, which are
   typically a point of service to a large number of nodes and have to
   satisfy the information requests from remote entities, we need to
   consider scalability-related metrics, such as frequency of requests
   received and processing of successfully satisfied information
   requests.

   Finally, given the in-network caching functionality in ICN, metrics
   for the efficiency and performance of in-network caching have to be
   defined, which can quantify the performance of in-network caching
   algorithms.  A first step on this direction has been made in [L9].
   The paper proposes a formula that approximates the proportion of time
   that a data object is stored in a network cache.  The model takes as
   input the rate of requests for a given content, named the Content of
   Interest (CoI), and the rate of requests for all other data objects
   that go through the given network element (router) and move the CoI
   down in the (LRU) cache.  The formula takes also into account the
   size of the cache of this router.

   The output of the model essentially reflects the probability that the
   CoI will be found in a given cache.  The initial study [L9] is
   applied to the CCN/NDN framework, where contents get cached at every
   node they traverse, while efforts are underway to assess the accuracy
   of the model for other caching strategies.  The formula according to
   which the probability or proportion is calculated is given by:

   pi = [mu/(mu+lambda)]^N,

   where lambda is the request rate for CoI, mu is the request rate for
   contents that move CoI down the cache, and N is the size of the cache
   (in slots).

   The formula can be used to assess the caching performance of the
   system and can also potentially be used to identify the gain of the



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   system due to caching.  This can then be used to compare against
   gains by other factors, e.g., addition of extra bandwidth in the
   network.


3.5.  Resource Equivalence and Tradeoffs

   As we have seen above, every information-centric network is built
   from a set of resources, which include link capacities, different
   types of memory structures and repositories used for storing named
   information objects and chunks temporarily (i.e. caching) or
   persistently, as well as name resolution and other lookup services.
   Complexity and processing needs in terms of forwarding decisions,
   management (e.g., need for manual configuration, explicit garbage
   collection, and so on), and routing (i.e., amount of state needed,
   need for manual configuration of routing tables, support for
   mobility, etc.) set the stage for a range of engineering tradeoffs.

   In order to be able to compare different ICN approaches it would be
   beneficial to to define equivalence in terms of different resources
   which today are considered incomparable.  For example, would
   provisioning an additional 5 Mb/s link capacity lead to better
   performance than adding 100 GB of in-network storage?  Within this
   context one would consider resource equivalence (and the associated
   tradeoffs) for example for cache hit ratios per GB of cache,
   forwarding decision times, CPU cycles per forwarding decision, and so
   on.


3.6.  Technology Evolution Assumptions

   TBD


4.  Security Considerations

   The introduction of an information-centric networking architecture
   and the corresponding communication paradigm changes many aspects of
   network security.  Additional evaluation will be required to ensure
   relevant security requirements are appropriately met by the
   implementation of the chosen architecture in the scenarios presented
   in Section 2.

   The various ICN architectures that are currently proposed have
   concentrated on authentication of delivered content to ensure content
   integrity.  However the approaches are primarily applicable to freely
   accessible content that does not require access authorization,
   although they will generally support delivery of encrypted content.



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   The introduction of widespread caching mechanisms may also provide
   additional attack surfaces.  The caching architecture to be used also
   needs to be evaluated to ensure that it meets the requirements of the
   usage scenarios.

   In practice, the work on security in the various ICN research
   projects has been heavily concentrated on authentication of content.
   In general, work on authorization, access control, privacy and
   security threats due to the expanded role of in-network caches has
   been quite limited.  A roadmap for improving the security model in
   NetInf can be found in [RAA09].  In the rest of this section we
   briefly consider the issues at hand and provide pointers to the work
   that has been done on the security aspects of the architectures
   proposed.


4.1. Authentication

   For fully secure content distribution, content access requires that
   the receiver needs to be able to reliably assess validity,
   provenance, and relevance.  Validity relates to whether the received
   data object is a complete, uncorrupted copy of what was originally
   published.  Provenance means that the receiver can identify the
   publisher, and, if so, whether it and the source of any cached
   version of the document can be adequately trusted.  Relevance
   ascertains that the content obtained answers the question that the
   receiver asked.

   All ICN architectures considered in this document primarily target
   the validity requirement using strong cryptographic means to tie the
   content request name to the content.  Provenance and relevance are
   directly targeted to varying extents:  There is a tussle or trade-off
   between simplicity and efficiency of access and level of assurance of
   all these traits.   For example, maintaining provenance information
   can become extremely costly, particularly when considering (historic)
   relationships between multiple objects.  Architectural decisions have
   therefore been taken in each case as to whether the assessment is
   carried out by the ICN or left to the application.

   An additional consideration for authentication is whether a name
   should be irrevocably and immutably tied to a static piece of
   preexisting content or whether the name can be used to refer to
   dynamically or subsequently generated content.  Schemes that only
   target immutable content can be less resource hungry as they can use
   digest functions rather than public key cryptography for generating
   and checking signatures.  However, this can increase the load on
   applications as they are required to manage many names, rather than
   using a single name for an item of evolving content that changes over



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   time (e.g. a piece of data containing an age reference).

   NetInf, for instance, uses the Named Information (ni) URI scheme
   [RFC6920] to identify content.  This allows NetInf to assure validity
   without any additional information but gives no assurance on
   provenance or relevance.  A "search" request allows an application to
   identify relevant content.  Applications may choose to structure
   content to allow provenance assurance but this will typically require
   additional network access.  NetInf validity authentication is
   consequently efficient in a network environment with intermittent
   connectivity as it does not force additional network accesses and
   allows the application to decide on provenance validation if
   required.  NetInf primarily targets static content, but an extension
   would allow dynamic content to be handled.  The immutable case only
   uses digest functions.

   DONA [DONA] and CCN [CCN] [SJ09] integrate most of the data needed to
   verify provenance into all content retrievals but need to be able to
   retrieve additional information (typically a security certificate) in
   order to complete the provenance authentication.  Whether the
   application has any control of this extra retrieval will depend on
   the implementation.  CCN is explicitly designed to handle dynamic
   content allowing names to be pre-allocated and attached to
   subsequently generated content.  DONA offers variants for dynamic and
   immutable content.

   PSI allows the authentication mechanism to be chosen so that it can,
   in theory, adopt the authentication strategy of any other other
   architectures [PRST2.4].  In the light of such choices, however,
   interoperability (if required by the chosen deployment) needs to be
   taken care of through dedicated solutions.


4.2. Authorization, Access Control and Statistics

   A potentially major concern for all ICN architectures considered here
   is that they do not provide any inbuilt support for an authorization
   framework or for statistics monitoring.  Once content has been
   published and cached in servers, routers or end points not controlled
   by the publisher, the publisher has no way to enforce access control,
   determine which users have accessed the content or revoke its
   publication.  In fact, in some cases, it is even difficult for the
   publishers themselves to perform access control, where requests do
   not necessarily contain host/user identifier information.

   Access could be limited by encrypting the content but the necessity
   of distributing keys out-of-band appears to negate the advantages of
   in-network caching.  This also creates significant challenges when



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   attempting to manage and restrict key access. An authorization
   delegation scheme has been proposed in [FMP12] but this requires
   access to a server controlled by the publisher to obtain an access
   token making it essentially just an out-of-band key distribution
   system.

   Evaluating the impact of the absence of these features will be
   essential for any scenario where an ICN architecture might be
   deployed.  It may have a seriously negative impact on the
   applicability of ICN in commercial environments unless a solution can
   be found.


4.3. Privacy

   Another area where the architectures have not been significantly
   analyzed is privacy.  Caching implies a trade-off between network
   efficiency and privacy.  The activity of users is significantly more
   exposed to the scrutiny of cache owners with whom they may not have
   any relationship.

   Although in many ICN architectures, the source of a request is not
   explicitly identified, an attacker may be able to obtain considerable
   information if s/he can monitor transactions on the cache and obtain
   details of the objects accessed, the topological direction of
   requests and information about the timing of transactions.   The
   persistence of data in the cache can make life easier for an attacker
   by giving a longer timescale for analysis.

   The impact of CCN on privacy has been investigated in [Lau10].  The
   analysis in this master's thesis is to a large degree applicable to
   all of ICN architectures because it is mostly focused on the common
   caching aspect.  The privacy risks of named data networking are also
   highlighted in [LLRSBK12].  Further work on privacy in ICNs can be
   found in [CDKU12].


4.4. Changes to the Network Security Threat Model

   The architectural differences of the various ICN models as compared
   to TCP/IP have consequences for network security.  There is limited
   consideration of the threat models and potential mitigation in the
   various documents describing the architectures.  The changed threat
   model is also discussed in [Lau10] and [CDKU12].  Some of the key
   aspects are:

    o Caching implies a tradeoff between network efficiency and user
      privacy as discussed in subsection 4.3.



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    o More powerful routers upgraded to handle persistent caching
      increase the network's attack surface.  This is particularly the
      case in systems (e.g., CCN) that may need to perform cryptographic
      checks on content that is being cached.  For example, not doing
      this could lead routers to disseminate invalid content.

    o ICN makes it difficult to identify the origin of a request as
      mentioned in subsection 4.3, slowing down the process of blocking
      requests and requiring alternative mechanisms to differentiate
      legitimate requests from inappropriate ones, as access control
      lists (ACLs) will probably be of little value for ICN requests.

    o Denial-of-service (DoS) attacks may require more effort on ICN
      than in TCP/IP, but they are still feasible.  One reason for this
      is that it is difficult for the attacker to force repeated
      requests for the same content onto a single node. Information-
      centric networks naturally spread content so that after the
      initial few requests, subsequent requests will generally be
      satisfied by alternative sources, blunting the impact of a DoS
      attack.  That said, there are many ways around this, e.g.,
      generating random suffix identifiers that always result in cache
      misses.

    o Per-request state in routers can be abused for DoS attacks.

    o Caches can be misused in the following ways:

       + Attackers can use caches as storage to make their own content
         available.

       + The efficiency of caches can be decreased by attackers with the
         goal of DoS attacks.

       + Content can be extracted by any attacker connected to the
         cache, putting users' privacy at risk.

   Appropriate mitigation of these threats will need to be considered in
   each scenario.


5.  IANA Considerations

   This document presents no IANA considerations.


6.  Acknowledgments

   This document has benefited from pointers to the growing ICN



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   literature, suggestions, comments and proposed text provided by the
   following members of the IRTF Information-Centric Networking Research
   Group (ICNRG), listed in alphabetical order: Marica Amadeo, Hitoshi
   Asaeda, Claudia Campolo, Luigi Alfredo Grieco, Myeong-Wuk Jang, Ren
   Jing, Will Liu, Ioannis Psaras, Dirk Trossen, Jianping Wang, Yuanzhe
   Xuan, and Xinwen Zhang.


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Authors' Addresses


   Kostas Pentikousis (editor)
   Huawei Technologies
   Carnotstrasse 4
   10587 Berlin
   Germany

   Email: k.pentikousis@huawei.com




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   Borje Ohlman
   Ericsson Research
   S-16480 Stockholm
   Sweden

   Email: Borje.Ohlman@ericsson.com


   Daniel Corujo
   Instituto de Telecomunicacoes
   Campus Universitario de Santiago
   P-3810-193 Aveiro
   Portugal

   Email: dcorujo@av.it.pt


   Gennaro Boggia
   Dep. of Electrical and Information Engineering
   Politecnico di Bari
   Via Orabona 4
   70125 Bari
   Italy

   Email: g.boggia@poliba.it


   Gareth Tyson
   School and Electronic Engineering and Computer Science
   Queen Mary, University of London
   United Kingdom

   Email: gareth.tyson@eecs.qmul.ac.uk


   Elwyn Davies
   Trinity College Dublin/Folly Consulting Ltd
   Dublin, 2
   Ireland

   Email: davieseb@scss.tcd.ie


   Priya Mahadevan
   Palo Alto Research Center
   3333 Coyote Hill Rd
   Palo Alto, CA 94304
   USA



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   Email: Priya.Mahadevan@parc.com


   Spiros Spirou
   Intracom Telecom
   19.7 km Markopoulou Avenue
   19002 Peania, Athens
   Greece

   Email: spis@intracom.com


   Antonella Molinaro
   Dep. of Information, Infrastructures, and Sustainable
   Energy Engineering
   Universita' Mediterranea di Reggio Calabria
   Via Graziella 1
   89100 Reggio Calabria
   Italy

   Email: antonella.molinaro@unirc.it


   Dorothy Gellert
   InterDigital Communications, LLC
   781 Third Avenue
   King Of Prussia, PA  19406-1409
   USA

   Email:  dorothy.gellert@interdigital.com


   Suyong Eum
   National Institute of Information and Communications Technology
   4-2-1, Nukui Kitamachi, Koganei
   Tokyo  184-8795
   Japan

   Phone: +81-42-327-6582
   Email: suyong@nict.go.jp











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