COINRG                                                        M. McBride
Internet-Draft                                                 Futurewei
Intended status: Standards Track                             D. Kutscher
Expires: August 1, 2020                                 Emden University
                                                             E. Schooler
                                                           CJ. Bernardos
                                                        January 29, 2020

                      Edge Data Discovery for COIN


   This document describes the problem of distributed data discovery in
   edge computing, and in particular for computing-in-the-network
   (COIN), both the marshalling of data at the outset of a computation
   and the persistence of the resultant data after the computation.
   Although the data might originate at the network edge, 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
   developed to support distributed data discovery at the network edge
   and beyond.

Status of This Memo

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   This Internet-Draft will expire on August 1, 2020.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Edge Data . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.2.  Background  . . . . . . . . . . . . . . . . . . . . . . .   3
     1.3.  Requirements Language . . . . . . . . . . . . . . . . . .   4
     1.4.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   4
   2.  Edge Data Discovery Problem Scope . . . . . . . . . . . . . .   4
     2.1.  A Cloud-Edge Continuum  . . . . . . . . . . . . . . . . .   5
     2.2.  Types of Edge Data  . . . . . . . . . . . . . . . . . . .   6
       2.2.1.  Example Meta Data . . . . . . . . . . . . . . . . . .   7
   3.  Scenarios for Discovering Edge Data Resources . . . . . . . .   8
   4.  Edge Data Discovery . . . . . . . . . . . . . . . . . . . . .   9
     4.1.  Types of Discovery  . . . . . . . . . . . . . . . . . . .   9
     4.2.  Naming the Data . . . . . . . . . . . . . . . . . . . . .   9
   5.  Use Cases of edge data discovery  . . . . . . . . . . . . . .  10
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   8.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  11
   9.  Normative References  . . . . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   Edge computing is an architectural shift that migrates Cloud
   functionality (compute, storage, networking, control, data
   management, etc.) out of the back-end data center to be more
   proximate to the IoT data being generated and analyzed at the edges
   of the network.  Edge computing provides local compute, storage and
   connectivity services, often required for latency- and bandwidth-
   sensitive applications.  Thus, Edge Computing plays a key role in
   verticals such as Energy, Manufacturing, Automotive, Video

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   Surveillance, Retail, Gaming, Healthcare, Mining, Buildings and Smart

1.1.  Edge Data

   Edge computing is motivated at least in part by the sheer volume of
   data that is being created by endpoint devices (sensors, cameras,
   lights, vehicles, drones, wearables, etc.) at the very network edge
   and that flows upstream, in a direction for which the network was not
   originally designed.  In fact, in dense IoT deployments (e.g., many
   video cameras are streaming high definition video), where multiple
   data flows collect or converge at edge nodes, data is likely to need
   transformation (transcoded, subsampled, compressed, analyzed,
   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
   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
   locations in the network on route to its final destination.  With an
   increasing percentage of devices connecting to the Internet being
   mobile, support for in-the-network caching and replication is
   critical for continuous data availability, not to mention efficient
   network and battery usage for endpoint devices.

   Additionally, as mobile devices' memory/storage fill up, in an edge
   context they may have the ability to offload their data to other
   proximate devices or resources, leaving a bread crumb trail of data
   in their wakes.  Therefore, although data might originate at edge
   devices, as more and more data is continuously created, processed and
   stored, it becomes increasingly dispersed throughout the physical
   world (outside of or scattered across managed local data centers),
   increasingly isolated in separate local edge clouds or data silos.
   Thus there needs to be a standard way to find it.  New and existing
   protocols will need to be identified/developed/enhanced for these
   purposes.  Being able to discover distributed data at the edge or in
   the middle of the network - will be an important component of Edge

1.2.  Background

   Several IETF T2T RG Edge Computing discussions have been held over
   the last couple years, a comparative study on the definition of Edge
   computing was presented in multiple sessions in T2T RG in 2018 and an
   Edge Computing I-D was submitted early 2019.  An IETF BEC (beyond
   edge computing) effort has been evaluating potential gaps in existing
   edge computing architectures.  Edge Data Discovery is one potential

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   gap that was identified and that needs evaluation and a solution.
   The newly proposed COIN RG highlights the need for computations in
   the network to be able to marshal potentially distributed input data
   and to handle resultant output data, i.e., its placement, storage
   and/or possible migration strategy.

1.3.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in RFC 2119 [RFC2119].

1.4.  Terminology

   o  Edge: The edge encompasses all entities not in the back-end cloud.
      The device edge is the boundary between digital and physical
      entities in the last mile network.  Sensors, gateways, compute
      nodes are included.  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
      network edge, where nearness is determined by the system
      requirements.  This includes high performance compute, storage and
      network equipment on either the device or infrastructure edge.

   o  Edge Data Discovery: The process of finding required data from
      edge entities, i.e., from databases, files systems, device memory
      that might be physically distributed in the network, and providing
      access to it logically as if it were a single unified source,
      perhaps through its namespace, that can be evaluated or searched.

   o  ICN: Information Centric Networking.  An ICN-enabled network
      routes data by name (vs address), caches content natively in the
      network, and employs data-centric security.  Data discovery may
      require that data be associated with a name or names, a series of
      descriptive attributes, and/or a unique identifier.

2.  Edge Data Discovery Problem Scope

   Our focus is on how to define and scope the edge data discovery
   problem.  This requires some discussion of the evolving definition of
   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 about edge data.

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2.1.  A Cloud-Edge Continuum

   Although Edge Computing data typically originates at edge devices,
   there is nothing that precludes edge data from being created anywhere
   in the cloud-to-edge computing continuum (Figure 1).  New edge data
   may result as a byproduct of computation being performed on the data
   stream anywhere along its path in the network.  For
   example,infrastructure edges may create new edge data when multiple
   data streams converge upon this aggregation point and require
   transformation (e.g., to fit within the available resources, to
   smooth raw measurements to eliminate high-frequency noise, to
   obfuscate data for privacy).

   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.

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                     | Back-end Cloud Data Center    |
                               ***  Cloud
                              *   * Interconnect
                     | Core Data Center              |
                               ***  Backbone
                              *   * Network
                     | Regional Data Center          |
                               ***  Metropolitan
                              *   * Network
                     | Infrastructure Edge           |
                               ***  Access
                              *   * Network
                     | Device Edge                   |

                Figure 1: Cloud-to-edge computing continuum

2.2.  Types of Edge Data

   Besides classically constrained IoT device sensor and measurement
   data accumulating throughout the edge computing infrastructure, edge
   data may also take the form of higher frequency and higher volume
   streaming data (from a continuous sensor or from a camera), meta data
   (about the data), control data (regarding an event that was
   triggered), and/or an executable that embodies a function, service,
   or any other piece of code or algorithm.  Edge data also could be
   created after multiple streams converge at an edge node and are
   processed, transformed, or aggregated together in some manner.

   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

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   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.

2.2.1.  Example Meta Data

   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

         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

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         Battery: Yes/No.  If Yes, the following sub-options could be

         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

3.  Scenarios for Discovering Edge Data Resources

   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
       but possibly virtualized and/or ephemeral data directory

   2.  A device wants to discover data resources available at or near
       its current location.  As some of these resources may be mobile,
       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,

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4.  Edge Data Discovery

   How can we discover data on the edge and make use of it?  There are
   proprietary implementations that collect data from various databases
   and consolidate it for evaluation.  We need a standard protocol set
   for doing this data discovery, on the device or infrastructure edge,
   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
   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
   own tools.  The tools are outside the scope of this document, but the
   discovery of that data is in scope.

4.1.  Types of Discovery

   There are many aspects of discovery and many different protocols that
   address each aspect.

   Discovery of new devices added to an environment.  Discovery of their
   capabilities/services in client/server environments.  Discovery of
   these new devices automatically.  Discovering a device and then
   synchronizing the device inventory and configuration for edge
   services.  There are many existing protocols to help in this
   discovery: UPnP, mDNS, DNS-SD, SSDP, NFC, XMPP, W3C network service
   discovery, etc.

   Edge devices discover each other in a standard way.  We can use DHCP,
   SNMP, SMS, COAP, LLDP, and routing protocols such as OSPF for devices
   to discovery one another.

   Discovery of link state and traffic engineering data/services by
   external devices.  BGP-LS is one solution.

   The question is if one or more of these protocols might be a suitable
   contender to extend to support edge data discovery?

4.2.  Naming the Data

   Named Data Networking (NDN) is one of five research projects funded
   by the U.S.  National Science Foundation under its Future Internet
   Architecture Program.  NDN has its roots in an earlier project,
   Content-Centric Networking (CCN), which Van Jacobson started at Xerox
   PARC around the time of his Google talk, to turn his architecture
   vision into a running prototype (see also his CoNEXT 2009 paper and
   especially Jacobsons ACM Queue interview).  The motivation is the
   mis-match of todays Internet architecture and its usage.  Today we
   build, support, and use Internet applications and services on top of
   an extremely capable architecture not designed to support them.  What

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   if we had an architecture designed to support them?  Specifically,
   todays IP packets can name only endpoints of conversations (IP
   addresses) at the network layer.  What if we generalize this layer to
   name any information (or content), not just endpoints?  We make it
   easier to develop, manage, secure, and use our networks.  NDN can be
   applied to edge data discovery to make it much easier to extract data
   and meta-data by naming it.  If data was named we would be able to
   discover the appropriate data simply by its name.

5.  Use Cases of edge data discovery

   1.  Autonomous Vehicles

   Autonomous vehicles rely on the processing of huge amounts of complex
   data in real-time for fast and accurate decisions.  These vehicles
   will rely on high performance compute, storage and network resources
   to process the volumes of data they produce in a low latency way.
   Various systems will need a standard way to discover the pertinent
   data for decision making

   2.  Video Surveillance

   The majority of the video surveillance footage will remain at the
   edge infrastructure (not sent to the cloud data center).  This
   footage is coming from vehicles, factories, hotels, universities,
   farms, etc.Much of the video footage will not be interesting to those
   evaluating the data.  A mechanism, set of protocols perhaps, is
   needed to identify the interesting data at the edge.  What
   constitutes interesting will be context specific, e.g., video frames
   with a car in it, a backyard nocturnal creature in it, a person or
   bicyclist or etc.  Interesting video data may be stored longer in
   storage systems at the very edge of the network or in flight in
   networking equipment further away from the device edge.

   3.  Elevator Networks

   Elevators are one of many industrial applications of edge computing.
   Edge equipment receives data from 100's of elevator sensors.  The
   data coming into the edge equipment is vibration, temperature, speed,
   level, video, etc.  We need the ability to identify where the data we
   need to evalute is located.

6.  IANA Considerations


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7.  Security Considerations

   Security considerations will be a critical component of edge data
   discovery particularly as intelligence is moved to the extreme edge
   where data is to be extracted.

8.  Acknowledgement

9.  Normative References

              Bernardos, C. and A. Mourad, "IPv6-based discovery and
              association of Virtualization Infrastructure Manager (VIM)
              and Network Function Virtualization Orchestrator (NFVO)",
              draft-bernardos-intarea-vim-discovery-02 (work in
              progress), August 2019.

              Bernardos, C. and A. Mourad, "Service Function discovery
              in fog environments", draft-bernardos-sfc-discovery-03
              (work in progress), September 2019.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

Authors' Addresses

   Mike McBride


   Dirk Kutscher
   Emden University


   Eve Schooler


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   Carlos J. Bernardos
   Universidad Carlos III de Madrid
   Av. Universidad, 30
   Leganes, Madrid  28911

   Phone: +34 91624 6236

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