< draft-mcbride-edge-data-discovery-overview-01.txt   draft-mcbride-edge-data-discovery-overview-02.txt >
T2TRG M. McBride COINRG M. McBride
Internet-Draft Huawei Internet-Draft Futurewei
Intended status: Standards Track D. Kutscher Intended status: Standards Track D. Kutscher
Expires: September 11, 2019 Emden University Expires: January 8, 2020 Emden University
E. Schooler E. Schooler
Intel Intel
CJ. Bernardos CJ. Bernardos
UC3M UC3M
March 10, 2019 July 07, 2019
Overview of Edge Data Discovery Edge Data Discovery for COIN
draft-mcbride-edge-data-discovery-overview-01 draft-mcbride-edge-data-discovery-overview-02
Abstract Abstract
This document describes the problem of distributed data discovery in This document describes the problem of distributed data discovery in
edge computing. Increasing numbers of IoT devices and sensors are edge computing, and in particular for computing-in-the-network
generating a torrent of data that originates at the very edges of the (COIN), which may require both the marshalling of data at the outset
network and that flows upstream, if it flows at all. Sometimes that of a computation and the persistence of the resultant data after the
data must be processed or transformed (transcoded, subsampled, computation. Although the data might originate at the network edge,
compressed, analyzed, annotated, combined, aggregated, etc.) on edge as more and more distributed data is created, processed, and stored,
equipment, particularly in places where multiple high bandwidth it becomes increasingly dispersed throughout the network. There
streams converge and where resources are limited. Support for edge needs to be a standard way to find it. New and existing protocols
data analysis is critical to make local, low-latency decisions (e.g., will need to be developed to support distributed data discovery at
regarding predictive maintenance, the dispatch of emergency services, the network edge and beyond.
identity, authorization, etc.). In addition, (transformed) data may
be cached, copied and/or stored at multiple locations in the network
on route to its final destination. Although the data might originate
at the edge, for example in factories, automobiles, video cameras,
wind farms, etc., as more and more distributed data is created,
processed and stored, it becomes increasingly dispersed throughout
the network. There needs to be a standard way to find it. New and
existing protocols will need to be identified/developed/enhanced for
distributed data discovery at the network edge and beyond.
Status of This Memo Status of This Memo
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provisions of BCP 78 and BCP 79. provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on September 11, 2019. This Internet-Draft will expire on January 8, 2020.
Copyright Notice Copyright Notice
Copyright (c) 2019 IETF Trust and the persons identified as the Copyright (c) 2019 IETF Trust and the persons identified as the
document authors. All rights reserved. document authors. All rights reserved.
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the Trust Legal Provisions and are provided without warranty as the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License. described in the Simplified BSD License.
Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Edge Data . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1. Edge Data . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Background . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. Background . . . . . . . . . . . . . . . . . . . . . . . 3
1.3. Requirements Language . . . . . . . . . . . . . . . . . . 4 1.3. Requirements Language . . . . . . . . . . . . . . . . . . 4
1.4. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4 1.4. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. The Edge Data Discovery Problem Scope . . . . . . . . . . . . 5 2. Edge Data Discovery Problem Scope . . . . . . . . . . . . . . 4
2.1. A Cloud-Edge Continuum . . . . . . . . . . . . . . . . . 5 2.1. A Cloud-Edge Continuum . . . . . . . . . . . . . . . . . 5
2.2. Types of Edge Data . . . . . . . . . . . . . . . . . . . 6 2.2. Types of Edge Data . . . . . . . . . . . . . . . . . . . 6
3. Scenarios for Discovering Edge Data Resources . . . . . . . . 8 3. Scenarios Requiring Discovery of Edge Data Resources . . . . 7
4. Edge Data Discovery . . . . . . . . . . . . . . . . . . . . . 8 4. Edge Data Discovery . . . . . . . . . . . . . . . . . . . . . 7
4.1. Types of Discovery . . . . . . . . . . . . . . . . . . . 9 4.1. Types of Discovery . . . . . . . . . . . . . . . . . . . 7
4.2. Naming the Data . . . . . . . . . . . . . . . . . . . . . 9 4.2. Naming the Data . . . . . . . . . . . . . . . . . . . . . 8
5. Use Cases of edge data discovery . . . . . . . . . . . . . . 10 5. Use Cases of edge data discovery . . . . . . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10 7. Security Considerations . . . . . . . . . . . . . . . . . . . 12
8. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 10 8. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 12
9. Normative References . . . . . . . . . . . . . . . . . . . . 11 9. Normative References . . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction 1. Introduction
Edge computing is an architectural shift that migrates Cloud Edge computing is an architectural shift that migrates Cloud
functionality (compute, storage, networking, control, data functionality (compute, storage, networking, control, data
management, etc.) out of the back-end data center to be more management, etc.) out of the back-end data center to be more
proximate to the IoT data being generated and analyzed at the edges proximate to the IoT data being generated and analyzed at the edges
of the network. Edge computing provides local compute, storage and of the network. Edge computing provides local compute, storage and
connectivity services, often required for latency- and bandwidth- connectivity services, often required for latency- and bandwidth-
sensitive applications. Thus, Edge Computing plays a key role in sensitive applications. Thus, Edge Computing plays a key role in
verticals such as Energy, Manufacturing, Automotive, Video Analytics, verticals such as Energy, Manufacturing, Automotive, Video
Retail, Gaming, Healthcare, Mining, Buildings and Smart Cities. Surveillance, Retail, Gaming, Healthcare, Mining, Buildings and Smart
Cities.
1.1. Edge Data 1.1. Edge Data
Edge computing is motivated at least in part by the sheer volume of Edge computing is motivated at least in part by the sheer volume of
data that is being created by IoT devices (sensors, cameras, lights, data that is being created by endpoint devices (sensors, cameras,
vehicles, drones, wearables, etc.) at the very network edge and that lights, vehicles, drones, wearables, etc.) at the very network edge
flows upstream, in a direction for which the network was not and that flows upstream, in a direction for which the network was not
originally provisioned. In fact, in dense IoT deployments (e.g., originally designed. In fact, in dense IoT deployments (e.g., many
many video cameras are streaming high definition video), where video cameras are streaming high definition video), where multiple
multiple data flows collect or converge at edge nodes, data is likely data flows collect or converge at edge nodes, data is likely to need
to need transformation (transcoded, subsampled, compressed, analyzed, transformation (transcoded, subsampled, compressed, analyzed,
annotated, combined, aggregated, etc.) to fit over the next hop link, annotated, combined, aggregated, etc.) to fit over the next hop link,
or even to fit in memory or storage. Note also that the act of or even to fit in memory or storage. Note also that the act of
performing compute on the data creates yet another new data stream! performing compute on the data creates yet another new data stream!
Preservation of the original data streams are needed sometimes but
not always.
In addition, data may be cached, copied and/or stored at multiple In addition, data may be cached, copied and/or stored at multiple
locations in the network on route to its final destination. With an locations in the network on route to its final destination. With an
increasing percentage of devices connecting to the Internet being increasing percentage of devices connecting to the Internet being
mobile, support for in-the-network caching and replication is mobile, support for in-the-network caching and replication is
critical for continuous data availability, not to mention efficient critical for continuous data availability, not to mention efficient
network and battery usage for endpoint devices. network and battery usage for endpoint devices.
Additionally, as mobile devices' memory/storage fill up, in an edge Additionally, as mobile devices' memory/storage fill up, in an edge
context they may have the ability to offload their data to other context they may have the ability to offload their data to other
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world (outside of or scattered across managed local data centers), world (outside of or scattered across managed local data centers),
increasingly isolated in separate local edge clouds or data silos. increasingly isolated in separate local edge clouds or data silos.
Thus there needs to be a standard way to find it. New and existing Thus there needs to be a standard way to find it. New and existing
protocols will need to be identified/developed/enhanced for these protocols will need to be identified/developed/enhanced for these
purposes. Being able to discover distributed data at the edge or in purposes. Being able to discover distributed data at the edge or in
the middle of the network - will be an important component of Edge the middle of the network - will be an important component of Edge
computing. computing.
1.2. Background 1.2. Background
An IETF T2T RG Edge discussion was held and a comparative study on Several IETF T2T RG Edge Computing discussions have been held over
the definition of Edge computing was presented in multiple sessions the last couple years, a comparative study on the definition of Edge
in T2T RG in 2018. An IETF BEC (beyond edge computing) effort has computing was presented in multiple sessions in T2T RG in 2018 and an
been evaluating potential gaps in existing edge computing Edge Computing I-D was submitted early 2019. An IETF BEC (beyond
architectures. Edge Data Discovery is one potential gap that needs edge computing) effort has been evaluating potential gaps in existing
evaluation and a solution. edge computing architectures. Edge Data Discovery is one potential
gap that was identified and that needs evaluation and a solution.
Businesses, such as industrial companies, are starting to understand The newly proposed COIN RG highlights the need for computations in
how valuable the data is that they've kept in silos. Once this data the network to be able to marshal potentially distributed input data
is made accessible on edge computing platforms, they may be able to and to handle resultant output data, i.e., its placement, storage
monetize the value of the data. But this will happen only if data and/or possible migration strategy.
can be discovered and searched among heterogeneous equipment in a
standard way. Discovering the data, that its most useful to a given
market segment, will be extremely useful in building business
revenues. Having a mechanism to provide this granular discovery is
the problem that needs solving either with existing, or new,
protocols.
1.3. Requirements Language 1.3. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119]. document are to be interpreted as described in RFC 2119 [RFC2119].
1.4. Terminology 1.4. Terminology
o Edge: The edge encompasses all entities not in the back-end cloud. o Edge: The edge encompasses all entities not in the back-end cloud.
The device edge is the boundary between digital and physical The device edge represents the very leaves of the network and
entities in the last mile network. Sensors, gateways, compute encompasses the entities found in the last mile network. Sensors,
nodes are included. The infrastructure edge includes equipment on gateways, compute nodes are included. Because the things that
the network operator side of the last mile network including cell populate the IoT can be both physical and/or cyber, in some
towers, edge data centers, cable headends, etc. See Figure 1 for solutions, particularly in software-defined or digital-twin
other possible tiers of edge clouds between the device edge and contexts, the device edge can include logical (vs physical)
the back-end cloud data center. entities. The infrastructure edge includes equipment on the
network operator side of the last mile network including cell
towers, edge data centers, cable headends, POPs, etc. See
Figure 1 for other possible tiers of edge clouds between the
device edge and the back-end cloud data center.
o Edge Computing: Distributed computation that is performed near the o Edge Computing: Distributed computation that is performed near the
edge, where nearness is determined by the system requirements. network edge, where nearness is determined by the system
This includes high performance compute, storage and network requirements. This includes high performance compute, storage and
equipment on either the device or infrastructure edge. network equipment on either the device or infrastructure edge.
o Edge Data Discovery: The process of finding required data from o Edge Data Discovery: The process of finding required data from
edge entities, i.e., from databases, files systems, device memory edge entities, i.e., from databases, files systems, device memory
that might be physically distributed in the network, and that might be physically distributed in the network, and
consolidating it or providing access to it logically as if it were consolidating it by providing access to it logically as if it were
a single unified source, perhaps through its namespace, that can a single unified source, perhaps through its namespace, that can
be evaluated or searched. be evaluated or searched.
o NDN: Named Data Networking. NDN routes data by name (vs address), o ICN: Information Centric Networking. An ICN-enabled network
caches content natively in the network, and employs data-centric routes data by name (vs address), caches content natively in the
security. Data discovery may require that data be associated with network, and employs data-centric security. Data discovery may
a name or names, a series of descriptive attributes, and/or a require that data be associated with a name or names, a series of
unique identifier. descriptive attributes, and/or a unique identifier.
2. The Edge Data Discovery Problem Scope 2. Edge Data Discovery Problem Scope
Our focus is on how to define and scope the edge data discovery Our focus is on how to define and scope the edge data discovery
problem. This requires some discussion of the evolving definition of problem. This requires some discussion of the evolving definition of
the edge and in turn what is meant by edge data. the edge as part of a cloud-to-edge continuum and in turn what is
meant by edge data as well as the meta-data surrounding the edge
data.
2.1. A Cloud-Edge Continuum 2.1. A Cloud-Edge Continuum
Although Edge Computing data typically originates at edge devices, Although Edge Computing data typically originates at edge devices,
there is nothing that precludes edge data from being created anywhere there is nothing that precludes edge data from being created anywhere
in the cloud-to-edge computing continuum (Figure 1). New edge data in the cloud-to-edge computing continuum (Figure 1). New edge data
may result as a byproduct of computation being performed on the data may result as a byproduct of computation being performed on the data
stream anywhere along its path in the network. For example, stream anywhere along its path in the network. For
infrastructure edges may create new edge data when multiple data example,infrastructure edges may create new edge data when multiple
streams converge upon this aggregation point and require data streams converge upon this aggregation point and require
transformation to fit within the available resources. Edge data also transformation (e.g., to fit within the available resources, to
may be sent to the back-end cloud as needed. Discovering data which smooth raw measurements to eliminate high-frequency noise, to
has be sent to the cloud is out of scope of this document, the obfuscate data for privacy).
assumption being that the cloud boundary is one that does not expose
or publish the availability of its data. An assumption is that all data will have associated policies
(default, inherited or configured) that describe access control
permissions. Consequently, the discoverability of data will be a
function of who or what has requested access. In other words, the
discoverable view into the available data will be limited to those
who are authorized. Discovering edge data that is exclusively
private is out of scope of this document, the assumption being that
there will be some edge clouds that do not expose or publish the
availability of their data. Although edge data may be sent to the
back-end cloud as needed, there is nothing that precludes it from
being discoverable if the cloud offers it as public.
Initially our focus is on discovery of edge data that resides at the
Device Edge and the Infrastructure Edge.
+-------------------------------+ +-------------------------------+
| Back-end Cloud Data Center | | Back-end Cloud Data Center |
+-------------------------------+ +-------------------------------+
*** Cloud *** Cloud
* * Interconnect * * Interconnect
*** ***
+-------------------------------+ +-------------------------------+
| Core Data Center | | Core Data Center |
+-------------------------------+ +-------------------------------+
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+-------------------------------+ +-------------------------------+
*** Access *** Access
* * Network * * Network
*** ***
+-------------------------------+ +-------------------------------+
| Device Edge | | Device Edge |
+-------------------------------+ +-------------------------------+
Figure 1: Cloud-to-edge computing continuum Figure 1: Cloud-to-edge computing continuum
Initially our focus is on discovery of edge data that resides at the
Device Edge and the Infrastructure Edge.
2.2. Types of Edge Data 2.2. Types of Edge Data
Besides sensor and measurement data accumulating throughout the edge Besides classically constrained IoT device sensor and measurement
computing infrastructure, edge data may also take the form of data accumulating throughout the edge computing infrastructure, edge
streaming data (from a camera), meta data (about the data), control data may also take the form of higher frequency and higher volume
data (regarding an event that was triggered), and/or an executable streaming data (from a continuous sensor or from a camera), meta data
that embodies a function, service, or any other piece of code or (about the data), control data (regarding an event that was
algorithm. Edge data also could be created after multiple streams triggered), and/or an executable that embodies a function, service,
converge at the edge node and are processed, transformed, or or any other piece of code or algorithm. Edge data also could be
aggregated together in some manner. created after multiple streams converge at an edge node and are
processed, transformed, or aggregated together in some manner.
SFC Data and meta-data discovery
Service function chaining (SFC) allows the instantiation of an
ordered set of service functions and subsequent "steering" of traffic
through them. Service functions provide a specific treatment of
received packets, therefore they need to be known so they can be used
in a given service composition via SFC. So far, how the SFs are
discovered and composed has been out of the scope of discussions in
IETF. While there are some mechanisms that can be used and/or
extended to provide this functionality, work needs to be done. An
example of this can be found in [I-D.bernardos-sfc-discovery].
In an SFC environment deployed at the edge, the discovery protocol
may also need to make available the following meta-data information
per SF:
o Service Function Type, identifying the category of SF provided.
o SFC-aware: Yes/No. Indicates if the SF is SFC-aware.
o Route Distinguisher (RD): IP address indicating the location of
the SF(I).
o Pricing/costs details.
o Migration capabilities of the SF: whether a given function can be
moved to another provider (potentially including information about
compatible providers topologically close).
o Mobility of the device hosting the SF, with e.g. the following
sub-options:
Level: no, low, high; or a corresponding scale (e.g., 1 to 10).
Current geographical area (e.g., GPS coordinates, post code).
Target moving area (e.g., GPS coordinates, post code).
o Power source of the device hosting the SF, with e.g. the following
sub-options:
Battery: Yes/No. If Yes, the following sub-options could be
defined:
Capacity of the battery (e.g., mmWh).
Charge status (e.g., %).
Lifetime (e.g., minutes).
Discovery of resources in an NFV environment: virtualized resources
do not need to be limited to those available in traditional data
centers, where the infrastructure is stable, static, typically
homogeneous and managed by a single admin entity. Computational
capabilities are becoming more and more ubiquitous, with terminal
devices getting extremely powerful, as well as other types of devices
that are close to the end users at the edge (e.g., vehicular onboard
devices for infotainment, micro data centers deployed at the edge,
etc.). It is envisioned that these devices would be able to offer
storage, computing and networking resources to nearby network
infrastructure, devices and things (the fog paradigm). These
resources can be used to host functions, for example to offload/
complement other resources available at traditional data centers, but
also to reduce the end-to- end latency or to provide access to
specialized information (e.g., context available at the edge) or
hardware. Similar to the discovery of functions, while there are
mechanisms that can be reused/extended, there is no complete solution
yet defined. An example of work in this area is
[I-D.bernardos-intarea-vim-discovery]."
3. Scenarios for Discovering Edge Data Resources Regardless of edge data type, a key problem in the Cloud-Edge
continuum is that data is often kept in silos. Meaning, data is
often sequestored within the Edge where it was created. A goal of
this discussion is to consider the prospect that different types of
edge data will be made accessible across disparate edges, for example
to enable richer multi-modal analytics. But this will happen only if
data can be described, searched and discovered across heterogeneous
edges in a standard way. Having a mechanism to enable granular edge
data discovery is the problem that needs solving either with existing
or new protocols. The mechanisms shouldn't care to which flavor
cloud or edge the request for data discovery is made.
Mainly two types of situations need to be covered: 3. Scenarios Requiring Discovery of Edge Data Resources
1. A set of data resources appears (e.g., a mobile node hosting data 1. A set of data resources appears (e.g., a mobile node hosting data
joins a network) and they want to be discovered by an existing joins a network) and they want to be discovered by an existing
but possibly virtualized and/or ephemeral data directory but possibly virtualized and/or ephemeral data directory
infrastructure. infrastructure.
2. A device wants to discover data resources available at or near 2. A device wants to discover data resources available at or near
its current location. As some of these resources may be mobile, its current location. As some of these resources may be mobile,
the available set of edge data may vary over time. the available set of edge data may vary over time.
3. A device wants to discover to where best in the edge
infrastructure to opportunistically upload its data, for example
if a mobile device wants to offload its data to the
infrastructure (for greater data availability, battery savings,
etc.).
4. Edge Data Discovery 4. Edge Data Discovery
How can we discover data on the edge and make use of it? There are How can we discover data on the edge and make use of it? There are
proprietary implementations that collect data from various databases proprietary implementations that collect data from various databases
and consolidate it for evaluation. We need a standard protocol set and consolidate it for evaluation. We need a standard protocol set
for doing this data discovery, on the device or infrastructure edge, for doing this data discovery, on the device or infrastructure edge,
in order to meet the requirements of many use cases. We will have in order to meet the requirements of many use cases. We will have
terabytes of data on the edge and need a way to identify its terabytes of data on the edge and need a way to identify its
existence and find the desired data. A user requires the need to existence and find the desired data. A user requires the need to
search for specific data in a data set and evaluate it using their search for specific data in a data set and evaluate it using their
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to discovery one another. to discovery one another.
Discovery of link state and traffic engineering data/services by Discovery of link state and traffic engineering data/services by
external devices. BGP-LS is one solution. external devices. BGP-LS is one solution.
The question is if one or more of these protocols might be a suitable The question is if one or more of these protocols might be a suitable
contender to extend to support edge data discovery? contender to extend to support edge data discovery?
4.2. Naming the Data 4.2. Naming the Data
Named Data Networking (NDN) is one of five research projects funded Information-Centric Networking (ICN) RFC 7927 [RFC7927] is a class of
by the U.S. National Science Foundation under its Future Internet architectures and protocols that provide "access to named data" as a
Architecture Program. NDN has its roots in an earlier project, first-order network service. Instead of host-to-host communication
Content-Centric Networking (CCN), which Van Jacobson started at Xerox as in IP networks, ICNs often use location-independent names to
PARC around the time of his Google talk, to turn his architecture identify data objects, and the network provides the services of
vision into a running prototype (see also his CoNEXT 2009 paper and processing (answering) requests for named data with the objective to
especially Jacobsons ACM Queue interview). The motivation is the finally deliver the requested data objects to a requesting consumer.
mis-match of todays Internet architecture and its usage. Today we
build, support, and use Internet applications and services on top of Such an approach has profound effects on various aspects of a
an extremely capable architecture not designed to support them. What networking system, including security (by enabling object-based
if we had an architecture designed to support them? Specifically, security on a message/packet level), forwarding behavior (name-based
todays IP packets can name only endpoints of conversations (IP forwarding, caching), but also on more operational aspects such as
addresses) at the network layer. What if we generalize this layer to bootstrapping, discovery etc.
name any information (or content), not just endpoints? We make it
easier to develop, manage, secure, and use our networks. NDN can be The CCNx and NDN (https://named-data.net) variants of ICN are based
applied to edge data discovery to make it much easier to extract data on a request/response abstraction where consumers (hosts, application
and meta-data by naming it. If data was named we would be able to requesting named data) send INTEREST messages into the network that
discover the appropriate data simply by its name. are forwarded by network elements to a destination that can provide
the requested named data object. Corresponding responses are sent as
so-called DATA messages that follow the reverse INTEREST path.
Each unique data object is named unambiguously in a hierarchical
naming scheme and is authenticated through Public-Key cryptography
(data objects can also optionally be encrypted in different ways).
The naming concept and the object-based security approach lay the
foundation for location independent operation. The network can
generally operate without any notion of location, and nodes
(consumers, forwarders) can forward requests for named data objects
directly, i.e., without any additional address resolution. Location
independence also enables additional features, for example the
possibility to replicate and cache named data objects. On-patch
caching is a standard feature in many ICN systems -- typically for
enhancing reliability and performance.
In CCNx and NDN, forwarders are stateful, i.e., they keep track of
forwarded INTEREST to later match the received DATA messages.
Stateful forwarding (in conjunction with the general named-based and
location-independent operation) also empowers forwarders to execute
individual forwarding strategies and perform optimizations such as
in-network retransmissions, multicasting requests (in cases there are
several opportunities for accessing a particular named data object)
etc.
Naming data and application-specific naming conventions are naturally
important aspects in ICN. It is common that applications define
their own naming convention (i.e., semantics of elements in the name
hierarchy). Such names can often directly derived from application
requirements, for example a name like /my-home/living-
room/light/switch/main could be relevant in a smart home setting, and
corresponding devices and application could use a corresponding
convention to facilitate controllers finding sensors and actors in
such a system with minimal user configuration.
The aforementioned features make ICN amenable to data discovery.
Because there is no name/address chasm as in IP-based systems, data
can be discovered by sending INTEREST to named data objects directly
(assuming a naming convention as described above). Moreover, ICN can
authenticate received data objects directly, for example using local
trust anchors in the network (for example in a home network).
Advanced ICN features for data discovery include the concept of
manifests in CCNx, i.e., ICN objects that describe data collections,
and data set synchronization protocols in NDN (https://named-
data.net/publications/li2018sync-intro/) that can inform consumers
about the availability of new data in a tree-based data structure
(with automatic retrieval and authentication). Also, ICN is not
limited to accessing static data. Frameworks such as Named Function
Networking (http://www.named-function.net) and RICE can provide the
general ICN feature for discovery not only for data but also for name
functions (for in-network computing) and for their results.
5. Use Cases of edge data discovery 5. Use Cases of edge data discovery
1. Autonomous Vehicles 1. Autonomous Vehicles
Autonomous vehicles rely on the processing of huge amounts of complex Autonomous vehicles rely on the processing of huge amounts of complex
data in real-time for fast and accurate decisions. These vehicles data in real-time for fast and accurate decisions. These vehicles
will rely on high performance compute, storage and network resources will rely on high performance compute, storage and network resources
to process the volumes of data they produce in a low latency way. to process the volumes of data they produce in a low latency way.
Various systems will need a standard way to discover the pertinent Various systems will need a standard way to discover the pertinent
skipping to change at page 10, line 38 skipping to change at page 10, line 24
networking equipment further away from the device edge. networking equipment further away from the device edge.
3. Elevator Networks 3. Elevator Networks
Elevators are one of many industrial applications of edge computing. Elevators are one of many industrial applications of edge computing.
Edge equipment receives data from 100's of elevator sensors. The Edge equipment receives data from 100's of elevator sensors. The
data coming into the edge equipment is vibration, temperature, speed, data coming into the edge equipment is vibration, temperature, speed,
level, video, etc. We need the ability to identify where the data we level, video, etc. We need the ability to identify where the data we
need to evalute is located. need to evalute is located.
4. Service Function Chaining
Service function chaining (SFC) allows the instantiation of an
ordered set of service functions and the subsequent "steering" of
traffic through them. Service functions provide a specific treatment
of received packets, therefore they need to be known so they can be
used in a given service composition via SFC. So far, how the SFs are
discovered and composed has been out of the scope of discussions in
IETF. While there are some mechanisms that can be used and/or
extended to provide this functionality, work needs to be done. An
example of this can be found in [I-D.bernardos-sfc-discovery].
In an SFC environment deployed at the edge, the discovery protocol
may also need the following kind of meta-data information per SF:
o Service Function Type, identifying the category of SF provided.
o SFC-aware: Yes/No. Indicates if the SF is SFC-aware.
o Route Distinguisher (RD): IP address indicating the location of
the SF(I).
o Pricing/costs details.
o Migration capabilities of the SF: whether a given function can be
moved to another provider (potentially including information about
compatible providers topologically close).
o Mobility of the device hosting the SF, with e.g. the following
sub-options:
Level: no, low, high; or a corresponding scale (e.g., 1 to 10).
Current geographical area (e.g., GPS coordinates, post code).
Target moving area (e.g., GPS coordinates, post code).
o Power source of the device hosting the SF, with e.g. the following
sub-options:
Battery: Yes/No. If Yes, the following sub-options could be
defined:
Capacity of the battery (e.g., mmWh).
Charge status (e.g., %).
Lifetime (e.g., minutes).
Discovery of resources in an NFV environment: virtualized resources
do not need to be limited to those available in traditional data
centers, where the infrastructure is stable, static, typically
homogeneous and managed by a single admin entity. Computational
capabilities are becoming more and more ubiquitous, with terminal
devices getting extremely powerful, as well as other types of devices
that are close to the end users at the edge (e.g., vehicular onboard
devices for infotainment, micro data centers deployed at the edge,
etc.). It is envisioned that these devices would be able to offer
storage, computing and networking resources to nearby network
infrastructure, devices and things (the fog paradigm). These
resources can be used to host functions, for example to offload/
complement other resources available at traditional data centers, but
also to reduce the end-to-end latency or to provide access to
specialized information (e.g., context available at the edge) or
hardware. Similar to the discovery of functions, while there are
mechanisms that can be reused/extended, there is no complete solution
yet defined. An example of work in this area is
[I-D.bernardos-intarea-vim-discovery]." The availability of this
meta-data about the capabilities of nearby physical as well as
virtualized resources can be made discoverable through edge data
discovery mechanisms.
6. IANA Considerations 6. IANA Considerations
N/A N/A
7. Security Considerations 7. Security Considerations
Security considerations will be a critical component of edge data Security considerations will be a critical component of edge data
discovery particularly as intelligence is moved to the extreme edge discovery particularly as intelligence is moved to the extreme edge
where data is to be extracted. where data is to be extracted.
8. Acknowledgement 8. Acknowledgement
The co-authors thank Dave Oran for his detailed feedback on an
earlier version of this draft.
9. Normative References 9. Normative References
[I-D.bernardos-intarea-vim-discovery] [I-D.bernardos-intarea-vim-discovery]
Bernardos, C. and A. Mourad, "IPv6-based discovery and Bernardos, C. and A. Mourad, "IPv6-based discovery and
association of Virtualization Infrastructure Manager (VIM) association of Virtualization Infrastructure Manager (VIM)
and Network Function Virtualization Orchestrator (NFVO)", and Network Function Virtualization Orchestrator (NFVO)",
draft-bernardos-intarea-vim-discovery-01 (work in draft-bernardos-intarea-vim-discovery-01 (work in
progress), February 2019. progress), February 2019.
[I-D.bernardos-sfc-discovery] [I-D.bernardos-sfc-discovery]
Bernardos, C. and A. Mourad, "Service Function discovery Bernardos, C. and A. Mourad, "Service Function discovery
in fog environments", draft-bernardos-sfc-discovery-02 in fog environments", draft-bernardos-sfc-discovery-02
(work in progress), March 2019. (work in progress), March 2019.
[I-D.irtf-icnrg-ccnxmessages]
Mosko, M., Solis, I., and C. Wood, "CCNx Messages in TLV
Format", draft-irtf-icnrg-ccnxmessages-09 (work in
progress), January 2019.
[I-D.irtf-icnrg-ccnxsemantics]
Mosko, M., Solis, I., and C. Wood, "CCNx Semantics",
draft-irtf-icnrg-ccnxsemantics-10 (work in progress),
January 2019.
[I-D.kutscher-icnrg-rice]
Krol, M., Habak, K., Oran, D., Kutscher, D., and I.
Psaras, "Remote Method Invocation in ICN", draft-kutscher-
icnrg-rice-00 (work in progress), October 2018.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997, DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>. <https://www.rfc-editor.org/info/rfc2119>.
[RFC7927] Kutscher, D., Ed., Eum, S., Pentikousis, K., Psaras, I.,
Corujo, D., Saucez, D., Schmidt, T., and M. Waehlisch,
"Information-Centric Networking (ICN) Research
Challenges", RFC 7927, DOI 10.17487/RFC7927, July 2016,
<https://www.rfc-editor.org/info/rfc7927>.
Authors' Addresses Authors' Addresses
Mike McBride Mike McBride
Huawei Futurewei
Email: michael.mcbride@huawei.com Email: michael.mcbride@futurewei.com
Dirk Kutscher Dirk Kutscher
Emden University Emden University
Email: ietf@dkutscher.net Email: ietf@dkutscher.net
Eve Schooler Eve Schooler
Intel Intel
Email: eve.m.schooler@intel.com Email: eve.m.schooler@intel.com
URI: http://www.eveschooler.com
Carlos J. Bernardos Carlos J. Bernardos
Universidad Carlos III de Madrid Universidad Carlos III de Madrid
Av. Universidad, 30 Av. Universidad, 30
Leganes, Madrid 28911 Leganes, Madrid 28911
Spain Spain
Phone: +34 91624 6236 Phone: +34 91624 6236
Email: cjbc@it.uc3m.es Email: cjbc@it.uc3m.es
URI: http://www.it.uc3m.es/cjbc/ URI: http://www.it.uc3m.es/cjbc/
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