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Versions: (draft-pentikousis-icn-scenarios) 00 01 02 03 04 05 RFC 7945

ICNRG                                                K. Pentikousis, Ed.
Internet-Draft                                                      EICT
Intended Status: Informational                                 B. Ohlman
Expires: January 7, 2016                                        Ericsson
                                                               E. Davies
                                                  Trinity College Dublin
                                                               S. Spirou
                                                        Intracom Telecom
                                                               G. Boggia
                                                     Politecnico di Bari
                                                            July 6, 2015

         Information-centric Networking: Evaluation Methodology


   This document surveys the evaluation tools currently available to
   researchers in the information-centric networking (ICN) area and
   provides suggestions regarding methodology and metrics.  Finally,
   this document sheds some light on the impact of ICN on network

Status of this Memo

   This Internet-Draft is submitted to IETF in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF), its areas, and its working groups.  Note that
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   material or to cite them other than as "work in progress."

   The list of current Internet-Drafts can be accessed at

   The list of Internet-Draft Shadow Directories can be accessed at

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Copyright and License Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
   document authors. All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document. Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.

Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  2
   2.  Evaluation Methodology . . . . . . . . . . . . . . . . . . . .  4
     2.1.  ICN Simulators and Testbeds  . . . . . . . . . . . . . . .  4
       2.1.1.  CCN and NDN  . . . . . . . . . . . . . . . . . . . . .  4
       2.1.2.  Publish/Subscribe Internet Architecture  . . . . . . .  6
       2.1.3.  NetInf . . . . . . . . . . . . . . . . . . . . . . . .  6
       2.1.4.  COMET  . . . . . . . . . . . . . . . . . . . . . . . .  7
       2.1.5.  Experimental Facility Testing  . . . . . . . . . . . .  7
     2.2.  Topology Selection . . . . . . . . . . . . . . . . . . . .  8
     2.3.  Traffic Load . . . . . . . . . . . . . . . . . . . . . . .  9
     2.4.  Choosing Relevant Metrics  . . . . . . . . . . . . . . . . 13
       2.4.1.  Traffic Metrics  . . . . . . . . . . . . . . . . . . . 16
       2.4.2.  System Metrics . . . . . . . . . . . . . . . . . . . . 17
     2.5.  Resource Equivalence and Tradeoffs . . . . . . . . . . . . 18
   3.  ICN Security Aspects . . . . . . . . . . . . . . . . . . . . . 19
     3.1. Authentication  . . . . . . . . . . . . . . . . . . . . . . 20
     3.2. Authorization, Access Control and Statistics  . . . . . . . 21
     3.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 21
     3.4. Changes to the Network Security Threat Model  . . . . . . . 22
   4.  Security Considerations  . . . . . . . . . . . . . . . . . . . 23
   5.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 23
   6.  Acknowledgments  . . . . . . . . . . . . . . . . . . . . . . . 23
   7.  Informative References . . . . . . . . . . . . . . . . . . . . 24
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 30

1.  Introduction

   As discussed in [RFC7476], the development phase that information-
   centric networking (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

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   difficult to compare different proposals on an equal footing.  To
   date, 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.  Typically, researchers follow a specific methodology
   based on the goal of their experiment, e.g., whether they want to
   evaluate scalability, quantify resource utilization, or analyze
   economic incentives.  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; the
   diversity of devices used, as we explain in the remainder of this

   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 ICN evaluations should
   assess 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.

   This document incorporates input from ICNRG participants and their
   corresponding text contributions, has been reviewed by several ICNRG
   active participants (see section 6), and represents the consensus of
   the research group.  That said, note that this document does not
   constitute an IETF standard; see also [RFC5743].

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   The remainder of this document is organized as follows. Section 2
   presents various techniques and considerations for evaluating
   different ICN architectures.  Section 3 discusses the impact of ICN
   on network security.

2.  Evaluation Methodology

   This document proposes key guidelines alongside suggested data sets
   and high-level approaches that we expect to be of interest to 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, as well as against the state of the art in host-
   centric solutions, and identify the respective strengths and

2.1.  ICN Simulators and Testbeds

   Since ICN is an emerging area, the community is 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); any limitations should be openly documented.  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.

2.1.1.  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

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   interoperable with CCNx. The core CCNx logic has been implemented in
   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 [CCNj] 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 a 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,

   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,

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   thereby supporting automatic discovery and provisioning of testbed
   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.

2.1.2.  Publish/Subscribe Internet Architecture

   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 [IEICE].

   Moreover, the ICN simulation environment [ICN-Sim] 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.

2.1.3.  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. The SAIL project developed a

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

2.1.4.  COMET

   The EU FP7 COMET project developed a simulator, called Icarus, which
   implements ProbCache [PROBCACHE], centrality-based in-network caching
   [CL4M] and the hash-route-based algorithms detailed in [HASHROUTE].
   The simulator is built in Python and makes use of the Fast Network
   Simulator Setup tool [FNSS] to configure the related parameters of
   the simulation. The simulator is available from:

2.1.5.  Experimental Facility 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.

   Over the last decade several experimental facilities have become
   available to the ICN community. Most of them provide a base platform
   for experimentation using real network links and virtualized
   computing resources. GENI (www.geni.net), for example, 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.

   The Asia Future Internet Forum (http://www.asiafi.net) has also
   developed a testbed used for ICN experiments [AFI]. This testbed
   consists of multiple servers located in Asia and other locations.
   Each testbed server (or VM) utilizes a Linux kernel-based container
   (LXC) for node virtualization.  This testbed enables testbed users to
   run applications and protocols for ICN in two experimentation modes
   using two different container designs: (1) application-level

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   experimentation using a "common container" and (2) network-level
   experimentation using a "user container."  A common container is
   shared by all testbed users, and a user container is assigned to one
   testbed user.  A common container possesses a global IP address to
   connect with each other or outside networks, while each user
   container uses a private IP address (e.g., and a user
   space is a closed networking environment. Yet, a user can login to
   his/her user containers using SSH with his/her certificate, or access
   them from PCs connected to the Internet using SSH tunnel.  This
   testbed also implements an "on-filesystem cache" to allocate caching
   data on a UNIX filesystem.  The on-filesystem cache system
   accommodates two kinds of caches: "individual cache" and "shared
   cache." Individual cache is accessible for one dedicated router for
   the individual user, while shared cache is accessible for a set of
   routers in the same group to avoid duplicated caching in the
   neighborhood for cooperative caching.

2.2.  Topology Selection

   [RFC7476] 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. Current Internet traces
   are also available to assist in this, e.g. see the CAIDA Macroscopic
   Internet Topology Data Kit
   (http://www.caida.org/data/active/internet-topology-data-kit) and

   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 consideration in this choice of this with CAIDA's ITDK
   traces recording millions of routers across thousands of domains.
   Beyond these traces there is a wide range of synthetic topologies,
   such as the Barabasi-Albert model [BA] and the Watts-Strogatz small-
   world topology [WATTS].  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 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.

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   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
   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 [DELAY] 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 [RFC7476], for example, contact traces
   from the DTN community could also be used in ICN evaluations.

2.3.  Traffic Load

   In this subsection we provide 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.  As we are
   still lacking ICN-specific traffic workloads we can currently only
   extrapolate from today's workloads.  A significant challenge then
   relates to the identification of the applications contributing to the
   observed traffic (e.g., Web, P2P), as well as to the exact amount of
   traffic they contribute to the overall traffic mixture.  Efforts in
   this direction can take heed from today's traffic mix comprising 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://multiprobe.ewi.tudelft.nl/multiprobe.html,
   http://an.kaist.ac.kr/traces/IMC2007.html, and

   Taking a more systematic approach, and with the purpose of modeling
   the traffic load, we can resort to measurement studies that
   investigate the composition of Internet traffic, such as [1][2].  In
   [1] a large scale measurement study was performed, with the purpose
   of studying the traffic crossing inter-domain links.  The results

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   indicate the dominance of Web traffic, amounting to 52% over all
   measured traffic. However, Deep Packet Inspection (DPI) techniques
   reveal that 25-40% of all HTTP traffic actually carries video
   traffic.  Results from DPI techniques also reveal the difficulty in
   correctly identifying the application type in the case of P2P
   traffic: mapping observed port numbers to well known applications
   shows P2P traffic constituting only 0.85% of overall traffic, while
   DPI raises this percentage to 18.32% [1]. Relevant studies on a large
   ISP show the percentage of P2P traffic ranging from 17 to 19% of
   overall traffic [2].  Table I provides an overview of these figures.
   The "other" traffic type denotes traffic that cannot be classified in
   any of the first three application categories, and consists of
   unclassified traffic and traffic heavily fragmented into several
   applications (e.g., 0.17% DNS traffic).

   Table I. Traffic Type Percent of Total Traffic [1, 2]

   Traffic Type | Ratio
   Web          | 31-39%
   P2P          | 17-19%
   Video        | 13-21%
   Other        | 29-31%

   The content catalog for each type of traffic can be characterized by
   a specific set of parameters:

   i. The cardinality of the estimated content catalog.

   ii. The size of the exchanged contents (either chunks or entire named
   information objects).

   iii. The popularity of objects expressed in their request frequency.
   In most application types, the popularity distribution follows some
   power law, indicating that a small number of information items
   triggers a major portion of the entire set of requests.  The exact
   shape of the power law popularity distribution directly impacts the
   performance of the underlying protocols.  For instance, highly skewed
   popularity distributions (e.g., a Zipf-like distribution with a high
   slope value) favor the deployment of caching schemes, since caching a
   very small set of information items can dramatically increase the
   cache hit ratio.

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   Several studies in the past years have stated that Zipf's law is the
   discrete distribution that best represents the request frequency in a
   number of application scenarios, ranging from the Web to video on
   demand (VoD) services.  The key aspect of this distribution is that
   the frequency 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.

   Further, a variation of the Zipf distribution, termed the Mandelbrot-
   Zipf distribution, has been suggested by [P2PMod] 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.

   Popularity can also be characterized in terms of:

   a. The temporal dynamics of popularity, i.e., how requests are
   distributed in time. The popularity distribution expresses the number
   of requests submitted for each information item participating into a
   certain workload.  However, they do not describe how these requests
   are distributed in time.  This aspect is of primary importance when
   considering the performance of caching schemes since the ordering of
   the requests obviously affects the contents of a cache.  For example,
   with a Least Frequently Used (LFU) cache replacement policy, if all
   requests for a certain item are submitted close in time, the item is
   unlikely to be evicted from the cache, even by a (globally) more
   popular item whose requests are more evenly distributed in time.  The
   temporal ordering of requests gains even more importance when
   considering workloads consisting of various applications, all
   competing for the same cache space.

   b. The spatial locality of popularity i.e., how requests are
   distributed throughout a network. The importance of spatial locality
   relates to the ability to avoid redundant traffic in the network.  If
   requests are highly localized in some area of the entire network,
   then similar requests can be more efficiently served with mechanisms
   such as caching and/or multicast i.e., the concentration of similar
   requests in a limited area of the network allows increasing the
   perceived cache hit ratios at caches in the area and/or the traffic

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   savings from the use of multicast.

   Table II provides an overview of distributions that can be used to
   model each of the identified traffic types i.e., Web, Video (based on
   YouTube measurements) and P2P (based on BitTorrent measurements).
   These distributions are the outcome of a series of modeling efforts
   based on measurements of real traffic
   workloads[3][4][5][6][7][8][9][10][11][12][13].  A tool for the
   creation of synthetic workloads following these models, and also
   allowing the generation of different traffic mixes, can be found at
   [14].  A thorough description of the tool is provided in [15].

   Table II. Overview of traffic types models

       |  Object Size   |  Temporal Locality   | Popularity Distribution
   Web | Concatenation  | Ordering via LRU     | Zipf: p(i)=K/i^a
       | of Lognormal   | stack model [5]      | i: popularity rank
       | (body) and     |                      | N: total items
       | Pareto (tail)  | Exact timing via     | K: 1/Sum(1/i^a)
       | [7,8]          | exponential          | a: distribution slope
       |                | distribution [6]     | values 0.64-0.84 [3][4]
   VoD | Duration/size: | No analytical models | Weibull: k=0.513,
       | Concatenated   |                      | lambda=6010
       | normal, most   | Random distribution  |
       | videos         | across total         | Gamma: k=0.372,
       | ~330 kb/s [13] | duration             | theta=23910 [12]
   P2P | Wide variation | Mean arrival rate of | Mandelbrot-Zipf [9]:
       | on torrent     | 0.9454 torrents/hour | p(i)=K/((i+q)/a)
       | sizes [9].     | Peers in a swarm     | q: plateau factor,
       | No analytical  | arrive as            | 5 to 100.
       | models exist:  | l(t)= l0*e^(-t/tau)  | Flatter head than in
       | Sample a real  | l0: initial arrival  | Zipf-like distribution
       | BitTorrent [11]| rate (87.74 average) | (where q=0)
       | or use fixed   | tau: object          |
       | value          | popularity           |
       |                | (1.16 average)* [10] |

   * Random ordering of swarm births (first request). For each swarm
     calculate a different tau. Based on average tau and object
     popularity. Exponential decay rule for subsequent requests.

   Table III summarizes the content catalog.  With this shared point of
   reference, the use of the same set of parameters (depending on the

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   scenario of interest) among researchers will be eased, and different
   proposals could be compared on a common base.

   Table III. 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

2.4.  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 to change the mechanisms of data access
   and transport 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 if it is

   Quantification of network performance requires a set of standard
   metrics. These metrics should be broad enough so 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 another ICN approach or
   to the incumbent host-centric approach. It will therefore be less
   difficult to gauge optimization and research direction.  On the other
   hand, the metrics should be targeted to network performance only 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.

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   To arrive at a set of relevant metrics, it would be beneficial to
   look at the metrics used in existing ICN approaches, such as CCN
   [CCN] [VoCCN] [NDNP], NetInf [4WARD6.1] [4WARD6.3] [SAIL-B2] [SAIL-
   B3], PURSUIT [PRST4.5], COMET [CMT-D5.2] [CMT-D6.2], Connect [SHARE]
   [RealCCN], and CONVERGENCE [ICN-Web] [ICN-Scal] [ICN-Tran].  The
   metrics used in these approaches 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 the various 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 for each approach.

   Traffic metrics in existing ICN approaches are summarized in Table
   IV.  These are metrics for evaluating an approach mainly from 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 don't 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 metrics altogether.

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   Table IV. Traffic metrics used in ICN evaluations

                   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.  The various ICN approaches have used system metrics, but
   unfortunately 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 the 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, as
   summarized in Table V.  FIB size and path length, i.e., the routing
   component metrics, are almost ubiquitous among approaches, 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 V. Component metrics in existing ICN approaches

                      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'd 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

2.4.1.  Traffic 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-
   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 it's output, using QoS metrics such as IPPM, is that it
   can be done mostly without any dependence to applications.

   Another option for measuring transport performance would be to use

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   Quality of Service 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 between
   networks of different concepts (host-centric, information-centric, or
   other).  As implied earlier, the drawback of the 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.

2.4.2.  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
   experience.  For example, in IoT scenarios, which were discussed
   earlier in [RFC7476], 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.

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   [SensReqs] 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 also 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 gateways,
   which typically act as 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 and
   processing of successfully satisfied information requests.

   Finally, given the in-network caching functionality of ICNs, metrics
   for the efficiency and performance of in-network caching have to be
   defined.  Such metrics will need to guide researchers and operators
   regarding 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 content stays
   in a network cache.  The model takes as input the rate of requests
   for a given content (the Content of Interest) and the rate of
   requests for all other contents 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.  An initial study [L9] is applied
   to the CCN/NDN framework, where contents get cached at every node
   they traverse.  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
   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.

2.5.  Resource Equivalence and Tradeoffs

   As we have seen above, every ICN network is built from a set of
   resources, which include link capacities, different types of memory
   structures and repositories used for storing named information

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   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 be able 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

3.  ICN Security Aspects

   The introduction of an information-centric networking architecture
   and the corresponding communication paradigm changes many aspects of
   network security.  These will affect all scenarios described in
   [RFC7476].  Additional evaluation will be required to ensure relevant
   security requirements are appropriately met by the implementation of
   the chosen architecture in the various scenarios.

   The ICN architectures currently proposed have concentrated on
   authentication of delivered content to ensure the integrity of the
   content.  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.

   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.
   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
   [NETINFSC].  In the rest of this section we briefly consider the
   issues and provide pointers to the work that has been done on the
   security aspects of the architectures proposed.

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3.1. Authentication

   For fully secure content distribution, content access requires that
   the receiver needs to be able to reliably assess:

      validity:   is it a complete, uncorrupted copy of what was
                  originally published;

      provenance: can the receiver identify the publisher, and, if so,
                  whether it and the source of any cached version of the
                  document can be adequately trusted; and

      relevance:  is the content an answer to 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 because they are required to manage many names, rather
   than using a single name for an item of evolving content that changes
   over time (e.g. a piece of data containing an age reference).

   NetInf 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 and 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

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   dynamic content to be handled.  The immutable case only uses digest

   DONA [DONA] and CCN [CCN], [SECCONT] 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.

   PURSUIT [PSTSEC] appears to allow implementers to choose the
   authentication mechanism so that it can, in theory, emulate the
   authentication strategy of any of the other architectures. It is not
   clear whether different choices would lead to lack of

3.2. 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
   attempting to manage and restrict key access. An authorization
   delegation scheme has been proposed [ACDICN] 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.

3.3. Privacy

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   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 [CCNSEC] and
   the analysis is applicable to all ICN architectures because it is
   mostly focused on the common caching aspect.  The privacy risks of
   named data networking are also highlighted in [CCNPRIV].  Further
   work on privacy in ICNs can be found in [CONPRV].

3.4. 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.[CCNSEC] and [CONPRV]
   also consider the changed threat model.  Some of the key aspects are:

    o Caching implies a tradeoff between network efficiency and user
      privacy as discussed in Section 3.3.

    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 ICNs makes it difficult to identify the origin of a request as
      mentioned in Section 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 on 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; ICNs naturally
      spread content so that after the initial few requests, subsequent

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      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

       + 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.

4.  Security Considerations

   This document does not impact the security of the Internet.

5.  IANA Considerations

   This document presents no IANA considerations.

6.  Acknowledgments

   Konstantinos Katsaros contributed the updated text of Section 2.3
   along with an extensive set of references.

   Priya Mahadevan, Daniel Corujo and Gareth Tyson contributed to an
   earlier version of this document.

   This document has benefited from reviews, pointers to the growing ICN
   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, Suyong Eum, Dorothy Gellert, Luigi Alfredo
   Grieco, Myeong-Wuk Jang, Ren Jing, Will Liu, Antonella Molinaro,
   Ioannis Psaras, Dirk Trossen, Jianping Wang, Yuanzhe Xuan, and Xinwen

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7.  Informative References

   [RFC6920]  Farrell, S., Kutscher, D., Dannewitz, C., Ohlman, B.,
              Keranen, A., and P. Hallam-Baker, "Naming Things with
              Hashes", RFC 6920, April 2013.

   [RFC7476]  Pentikousis, K., Ohlman, B., Corujo, D., Boggia, G.,
              Tyson, G., Davies, E., Molinaro, A., and S. Eum,
              "Information-Centric Networking: Baseline Scenarios ", RFC
              7476, March 2015.

   [ndnSIM]   Afanasyev, A. et al., ndnSIM: NDN simulator for NS-3 NDN
              Technical Report NDN-0005, Revision 2, October 2012.

   [ccnSim]   Rossini, G. and D. Rossi, "Large scale simulation of CCN
              networks", Proc. Algotel 2012 , La Grande Motte, France,
              May 2012.

   [CCNPL]    Muscariello, L., "Content centric networking packet level
              simulator", available online at

   [CCNj]     Cianci, I. et al. "CCN - Java Opensource Kit EmulatoR for
              Wireless Ad Hoc Networks", Proc. 7th ACM Int. Conf. on
              Future Internet Technologies, Seoul, Korea, Sept., 2012.

   [IEICE]    G. Parisis, D. Trossen, and H. Asaeda, "A Node Design and
              a Framework for Development and Experimentation for an
              Information-Centric Network", IEICE Trans. Commun., vol.
              E96-B, no. 7, pp.1650-1660, July 2013.

   [ICN-Sim]  N. Vastardis et al., "Simulation Tools Enabling Research
              on Information-centric Networks", Proc. ICC FutureNet
              Workshop. IEEE, 2012.

   [PROBCACHE] I. Psaras, W. Chai, G. Pavlou, "Probabilistic In-Network
              Caching for Information-Centric Networks", Proc. SIGCOMM
              ICN Workshop. ACM, 2012.

   [CL4M]     Chai, W. K. et al., "Cache 'Less for More' in Information-
              centric Networks", Proc. Networking.  IFIP, 2012.

   [HASHROUTE] L. Saino, I. Psaras, G. Pavlou, "Hash-routing Schemes for
              Information-Centric Networking", Proc. SIGCOMM ICN
              Workshop. ACM, 2013.

   [FNSS]     L. Saino, C. Cocora and G. Pavlou, "A Toolchain for
              Simplifying Network Simulation Setup", Proc. SIMUTOOLS.

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              ACM, 2013.

   [BA]       Barabasi, A. and R. Albert, "Emergence of scaling in
              random networks", Science, vol. 286, no. 5439, pp. 509-
              512, 1999.

   [WATTS]    Watts, D. J. and S. H. Strogatz, "Collective dynamics of
              small-world networks", Nature, vol. 393, no. 6684, pp. 40-
              "10, 1998.

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Authors' Addresses

   Kostas Pentikousis (editor)
   EICT GmbH
   Torgauer Strasse 12-15
   10829 Berlin

   Email: k.pentikousis@eict.de

   Borje Ohlman
   Ericsson Research
   S-16480 Stockholm

   Email: Borje.Ohlman@ericsson.com

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

   Email: davieseb@scss.tcd.ie

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

   Email: spis@intracom.com

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

   Email: g.boggia@poliba.it

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