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Use Cases for In-Network Computing

Document Type Active Internet-Draft (coinrg RG)
Authors Ike Kunze , Klaus Wehrle , Dirk Trossen , Marie-Jose Montpetit , Xavier de Foy , David Griffin , Miguel Rio
Last updated 2023-03-11
Replaces draft-kunze-coin-industrial-use-cases
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COINRG                                                          I. Kunze
Internet-Draft                                                 K. Wehrle
Intended status: Informational                               RWTH Aachen
Expires: 12 September 2023                                    D. Trossen
                                                         M. J. Montpetit
                                                               X. de Foy
                                        InterDigital Communications, LLC
                                                              D. Griffin
                                                                  M. Rio
                                                           11 March 2023

                   Use Cases for In-Network Computing


   Computing in the Network (COIN) comes with the prospect of deploying
   processing functionality on networking devices, such as switches and
   network interface cards.  While such functionality can be beneficial,
   it has to be carefully placed into the context of the general
   Internet communication and it needs to be clearly identified where
   and how those benefits apply.

   This document presents some use cases to demonstrate how a number of
   salient COIN-related applications can benefit from COIN, further
   identifying their essential requirements.

Status of This Memo

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

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 12 September 2023.

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Copyright Notice

   Copyright (c) 2023 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 (
   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.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Providing New COIN Experiences  . . . . . . . . . . . . . . .   4
     3.1.  Mobile Application Offloading . . . . . . . . . . . . . .   4
     3.2.  Extended Reality and Immersive Media  . . . . . . . . . .  10
     3.3.  Personalized and interactive performing arts  . . . . . .  13
   4.  Supporting new COIN Systems . . . . . . . . . . . . . . . . .  17
     4.1.  In-Network Control / Time-sensitive applications  . . . .  17
     4.2.  Large Volume Applications . . . . . . . . . . . . . . . .  20
     4.3.  Industrial Safety . . . . . . . . . . . . . . . . . . . .  23
   5.  Improving existing COIN capabilities  . . . . . . . . . . . .  25
     5.1.  Content Delivery Networks . . . . . . . . . . . . . . . .  25
     5.2.  Compute-Fabric-as-a-Service (CFaaS) . . . . . . . . . . .  27
     5.3.  Virtual Networks Programming  . . . . . . . . . . . . . .  29
   6.  Enabling new COIN capabilities  . . . . . . . . . . . . . . .  33
     6.1.  Distributed AI  . . . . . . . . . . . . . . . . . . . . .  33
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  35
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  35
   9.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  35
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  36
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  36
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  36
     11.2.  Informative References . . . . . . . . . . . . . . . . .  37
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  40

1.  Introduction

   The Internet was designed as a best-effort packet network, forwarding
   packets from source to destination with limited guarantees regarding
   their timely and successful reception.  Data manipulation,
   computation, and more complex protocol functionality is generally
   provided by the end-hosts while network nodes are kept simple and
   only offer a "store and forward" packet facility.  This simplicity of
   purpose of the network has shown to be suitable for a wide variety of
   applications and has facilitated the rapid growth of the Internet.

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   However, with the rise of new services, some of which are described
   in this document, there are more and more fields that require more
   than best-effort forwarding including strict performance guarantees
   or closed-loop integration to manage data flows.  In this context,
   allowing for a tighter integration of computing and networking
   resources for enabling a more flexible distribution of computation
   tasks across the network, e.g., beyond 'just' endpoints, may help to
   achieve the desired guarantees and behaviors as well as increase
   overall performance.

   The vision of 'in-network computing' and the provisioning of such
   capabilities that capitalize on joint computation and communication
   resource usage throughout the network is part of the move from a
   telephone network analogy of the Internet into a more distributed
   computer board architecture.  We refer to those capabilities as 'COIN
   capabilities' in the remainder of the document.

   We believe that this vision of 'in-network computing' can be best
   outlined along four dimensions of use cases, namely those that (i)
   provide new user experiences through the utilization of COIN
   capabilities (referred to as 'COIN experiences'), (ii) enable new
   COIN systems, e.g., through new interactions between communication
   and compute providers, (iii) improve on already existing COIN
   capabilities, and (iv) enable new COIN capabilities.  Sections 3
   through 6 capture those categories of use cases and provide the main
   structure of this document.  The goal is to present how computing
   resources inside the network impact existing services and
   applications or allow for innovation in emerging fields.

   By delving into some individual examples within each of the above
   categories, we outline opportunities and propose possible research
   questions for consideration by the wider community when pushing
   forward 'in-network computing' architectures.  Furthermore,
   identifying requirements for an evolving solution space of COIN
   capabilities is another objective of the use case descriptions.  To
   achieve this, the following taxonomy is proposed to describe each of
   the use cases:

   1.  Description: High-level presentation of the purpose of the use
       case and a short explanation of the use case behavior.

   2.  Characterization: Explanation of the services that are being
       utilized and realized as well as the semantics of interactions in
       the use case.

   3.  Existing solutions: Description of current methods that may
       realize the use case (if they exist), not claiming to
       exhaustively review the landscape of solutions.

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   4.  Opportunities: An outline of how COIN capabilities may support or
       improve on the use case in terms of performance and other

   5.  Research questions: Essential questions that are suitable for
       guiding research to achieve the identified opportunities.

   6.  Requirements: Description of requirements for any COIN capability
       solutions that may need development along the opportunities
       outlined in item 4; we limit requirements to those directly
       describing COIN capabilities, recognizing that any use case will
       realistically hold many additional requirements for its

   This document discusses these six aspects along a number of
   individual use cases.  A companion document [USECASEANALYSIS] is
   tasked with performing a cross-use case analysis, i.e., summarizing
   the key research questions and identifying key requirements across
   all use cases.

2.  Terminology

   This document uses the terminology outlined in [TERMINOLOGY].

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  Providing New COIN Experiences

3.1.  Mobile Application Offloading

3.1.1.  Description

   The scenario can be exemplified in an immersive gaming application,
   where a single user plays a game using a VR headset.
   The headset hosts functions that "display" frames to the user, as
   well as the functions for VR content processing and frame rendering
   that also incorporate input data received from sensors in the VR

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   Once this application is partitioned into constituent (COIN) programs
   and deployed throughout a COIN system, utilizing the COIN execution
   environment, only the "display" (COIN) program may be left in the
   headset, while the compute intensive real-time VR content processing
   (COIN) program can be offloaded to a nearby resource rich home PC or
   a PND in the operator's access network, for a better execution
   (faster and possibly higher resolution generation).

3.1.2.  Characterization

   Partitioning a mobile application into several constituent (COIN)
   programs allows for denoting the application as a collection of
   (COIN) functions for a flexible composition and a distributed
   execution.  In our example above, most functions of a mobile
   application can be categorized into any of three, "receiving",
   "processing", and "displaying" function groups.

   Any device may realize one or more of the (COIN) programs of a mobile
   application and expose them to the (COIN) system and its constituent
   (COIN) execution environments.  When the (COIN) program sequence is
   executed on a single device, the outcome is what you see today as
   applications running on mobile devices.

   However, the execution of a (COIN) program may be moved to other
   (e.g., more suitable) devices, including PNDs, which have exposed the
   corresponding (COIN) program as individual (COIN) program instances
   to the (COIN) system by means of a 'service identifier'.  The result
   is the equivalent to 'mobile function offloading', for possible
   reduction of power consumption (e.g., offloading CPU intensive
   process functions to a remote server) or for improved end user
   experience (e.g., moving display functions to a nearby smart TV) by
   selecting more suitably placed (COIN) program instances in the
   overall (COIN) system.

   Figure 1 shows one realization of the above scenario, where a 'DPR
   app' is running on a mobile device (containing the partitioned
   Display(D), Process(P) and Receive(R) COIN programs) over an SDN
   network.  The packaged applications are made available through a
   localized 'playstore server'.  The mobile application installation is
   realized as a 'service deployment' process, combining the local app
   installation with a distributed deployment (and orchestration) of one
   or more (COIN) programs on most suitable end systems or PNDs
   ('processing server').

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                                +----------+ Processing Server
              Mobile            | +------+ |
           +---------+          | |  P   | |
           |   App   |          | +------+ |
           | +-----+ |          | +------+ |
           | |D|P|R| |          | |  SR  | |
           | +-----+ |          | +------+ |         Internet
           | +-----+ |          +----------+            /
           | |  SR | |              |                  /
           | +-----+ |            +----------+     +------+
           +---------+           /|SDN Switch|_____|Border|
                     +-------+ /  +----------+     |  SR  |
                     | 5GAN  |/        |           +------+
                     +-------+         |
         +---------+                   |
         |+-------+|               +----------+
         ||Display||              /|SDN Switch|
         |+-------+|   +-------+ / +----------+
         |+-------+|  /|WIFI AP|/
         ||   D   || / +-------+     +--+
         |+-------+|/                |SR|
         |+-------+|                /+--+
         ||  SR   ||            +---------+
         |+-------+|            |Playstore|
         +---------+            | Server  |
             TV                 +---------+

             Figure 1: Application Function Offloading Example.

   Such localized deployment could, for instance, be provided by a
   visiting site, such as a hotel or a theme park.  Once the
   'processing' (COIN) program is terminated on the mobile device, the
   'service routing' (SR) elements in the network route (service)
   requests instead to the (previously deployed) 'processing' (COIN)
   program running on the processing server over an existing SDN
   network.  Here, capabilities and other constraints for selecting the
   appropriate (COIN) program, in case of having deployed more than one,
   may be provided both in the advertisement of the (COIN) program and
   the service request itself.

   As an extension to the above scenarios, we can also envision that
   content from one processing (COIN) program may be distributed to more
   than one display (COIN) program, e.g., for multi/many-viewing
   scenarios, thereby realizing a service-level multicast capability
   towards more than one (COIN) program.

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3.1.3.  Existing Solutions

   The ETSI Mobile Edge Computing (MEC) [ETSI] suite of technologies
   provides solutions for mobile function offloading by allowing mobile
   applications to select resources in edge devices to execute functions
   instead of the mobile device directly.  For this, ETSI MEC utilizes a
   set of interfaces for the selection of suitable edge resources,
   connecting to so-called MEC application servers, while also allowing
   for sending data for function execution to the application server.

   However, the technologies do not utilize micro-services as described
   in our use case and also do not allow for the dynamic selection and
   redirection of micro-service calls to varying edge resources rather
   than a single MEC application server.

   Also, the selection of the edge resource (the app server) is
   relatively static, relying on DNS-based endpoint selection, which
   does not cater to the requirements of the example provided above,
   where the latency for redirecting to another device lies within few
   milliseconds for aligning with the framerate of the display micro-

   Lastly, MEC application servers are usually considered resources
   provided by the network operator through its MEC infrastructure,
   while our use case here also foresees the placement and execution of
   micro-services in end user devices.

   There also exists a plethora of mobile offloading platforms provided
   through proprietary platforms, all of which follow a similar approach
   as ETSI MEC in that a selected edge application server is being
   utilized to send functional descriptions and data for execution.

   The draft at [APPCENTRES] outlines a number of enabling technologies
   for the use case, some of which have been realized in an Android-
   based realization of the micro-services as a single application,
   which is capable to dynamically redirect traffic to other micro-
   service instances in the network.  This capability, together with the
   underlying path-based forwarding capability (using SDN) was
   demonstrated publicly, e.g., at the Mobile World Congress 2018 and

3.1.4.  Opportunities

   *  The packaging of (COIN) programs into existing mobile application
      packaging may enable the migration from current (mobile) device-
      centric execution of those mobile applications toward a possible
      distributed execution of the constituent (COIN) programs that are
      part of the overall mobile application.

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   *  The orchestration for deploying (COIN) program instances in
      specific end systems and PNDs alike may open up the possibility
      for localized infrastructure owners, such as hotels or venue
      owners, to offer their compute capabilities to their visitors for
      improved or even site-specific experiences.

   *  The execution of (current mobile) app-level (COIN) programs may
      speed up the execution of said (COIN) program by relocating the
      execution to more suitable devices, including PNDs.

   *  The support for service-level routing of requests (service routing
      in [APPCENTRES] may support higher flexibility when switching from
      one (COIN) program instance to another, e.g., due to changing
      constraints for selecting the new (COIN) program instance.

   *  The ability to identify service-level COIN elements will allow for
      routing service requests to those COIN elements, including PNDs,
      therefore possibly allowing for new COIN functionality to be
      included in the mobile application.

   *  The support for constraint-based selection of a specific (COIN)
      program instance over others (constraint-based routing in
      [APPCENTRES]) may allow for a more flexible and app-specific
      selection of (COIN) program instances, thereby allowing for better
      meeting the app-specific and end user requirements.

3.1.5.  Research Questions

   *  RQ 3.1.1: How to combine service-level orchestration frameworks
      with app-level packaging methods?

   *  RQ 3.1.2: How to reduce latencies involved in (COIN) program
      interactions where (COIN) program instance locations may change

   *  RQ 3.1.3: How to signal constraints used for routing requests
      towards (COIN) program instances in a scalable manner?

   *  RQ 3.1.4: How to identify (COIN) programs and program instances?

   *  RQ 3.1.5: How to identify a specific choice of (COIN) program
      instances over others?

   *  RQ 3.1.6: How to provide affinity of service requests towards
      (COIN) program instances, i.e., longer-term transactions with
      ephemeral state established at a specific (COIN) program instance?

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   *  RQ 3.1.7: How to provide constraint-based routing decisions at
      packet forwarding speed?

   *  RQ 3.1.8: What COIN capabilities may support the execution of
      (COIN) programs and their instances?

3.1.6.  Requirements

   *  Req 3.1.1: Any COIN system MUST provide means for routing of
      service requests between resources in the distributed environment.

   *  Req 3.1.2: Any COIN system MUST provide means for identifying
      services exposed by (COIN) programs for directing service

   *  Req 3.1.3: Any COIN system MUST provide means for identifying
      (COIN) program instances for directing (affinity) requests to a
      specific (COIN) program instance.

   *  Req 3.1.4: Any COIN system MUST provide means for dynamically
      choosing the best possible service sequence of one or more (COIN)
      programs for a given application experience, i.e., support for
      chaining (COIN) program executions.

   *  Req 3.1.5: Means for discovering suitable (COIN) programs SHOULD
      be provided.

   *  Req 3.1.6: Any COIN system MUST provide means for pinning the
      execution of a service of a specific (COIN) program to a specific
      resource, i.e., (COIN) program instance in the distributed

   *  Req 3.1.7: Any COIN system SHOULD provide means for packaging
      micro-services for deployments in distributed networked computing

   *  Req 3.1.8: The packaging MAY include any constraints regarding the
      deployment of (COIN) program instances in specific network
      locations or compute resources, including PNDs.

   *  Req 3.1.9: Such packaging SHOULD conform to existing application
      deployment models, such as mobile application packaging, TOSCA
      orchestration templates or tar balls or combinations thereof.

   *  Req 3.1.10: Any COIN system MUST provide means for real-time
      synchronization and consistency of distributed application states.

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3.2.  Extended Reality and Immersive Media

3.2.1.  Description

   Virtual Reality (VR), Augmented Reality (AR), and immersive media,
   now globally referred to as Extended Reality (XR) and the bases for
   the metaverse, are the drivers of a number of advances in interactive
   technologies.  While initially associated with gaming and
   entertainment, metaverse applications now include remote diagnosis,
   maintenance, telemedicine, manufacturing and assembly, intelligent
   agriculture, smart cities, and immersive classrooms.  XR is one
   example of the multisource-multidestination problem that combines
   video and haptics in interactive multi-party interactions under
   strict delay requirements.

   Indeed, XR applications require real-time interactivity for immersive
   and increasingly mobile immersive applications with tactile and time-
   sensitive data.  Because high bandwidth is needed for high resolution
   images and local rendering for 3D images and holograms, they are
   difficult to run over traditional networks which limits some of its
   potential benefits in the collaborative space.  As a consequence,
   innovation is needed to unlock the full potential of the

3.2.2.  Characterization

   XR experiences, especially those involving collaboration, are
   difficult to deliver with a client-server cloud-based solution as
   they require a combination of: stream synchronization, low delays and
   delay variations, means to recover from losses and optimized caching
   and rendering as close as possible to the user at the network edge.
   Furthermore, when XR deals with personal information and protected
   content, XR application must also provide a secure environment and
   ensure user privacy.  Additionally, the sheer amount of data needed
   for and generated by the XR applications, such as video holography,
   put them squarely in the realm of data-driven applications that can
   use recent trend analysis and mechanisms, as well as machine learning
   to find the optimal caching and processing solution and hopefully
   reduce the size of the data that needs transiting through the
   network.  Other mechanims such as data filtering and reduction,
   functional distribution and partitioning are also needed to
   accommodate the low delay needs for the same applications.

   Those characterisitics of XR makes it ideal to profit from some COIN
   capabilities to enable XR over networks.  COIN can enable the
   distribution of the service components across different nodes on the
   path from content source to rendering destination.  For example, data
   filtering, image rendering, and video processing leveraging different

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   HW capabilities with combinations of CPU and GPU at the network edge
   and in the fog, where the content is consumed, represent possible
   remedies for the high bandwidth demands of XR.  Machine learning
   across the network nodes can better manage the data flows by
   distributing them over more adequate paths.  In order to provide
   adequate quality of experience, multi-variate and heterogeneous
   resource allocation and goal optimization problems need to be solved,
   likely requiring advanced analysis and articificial intelligence.
   For the purpose of this document, it is important to note that the
   use of COIN for XR does not imply a specific protocol but targets an
   architecture enabling the deployment of the services.  In this
   context, similar considerations as for Section 3.1 apply.

3.2.3.  Existing Solutions

   XR profits from extensive research in the past years in gaming,
   machine learning, network telemetry, high resolution imaging, smart
   cities, and IoT.  Information Centric Networking (and related)
   approaches that combine publish subscribe and distributed storage are
   also very suited for the multisource-multidestination applications of
   XR.  Hence XR solutions exist and are more and more deployed outside
   entertainment, and with the focus on the Metaverse the number of
   publications related to XR has skyrocketed.

   However, in terms of networking which is the focus of this document
   current deployments remain mostly one-way: information is sent to the
   destination, rendered and displayed based on local processing.  A lot
   of the video information goes upstream.  There are still very little
   truly interactive immersive media applications over networks except
   within a single subnetwork that is characteristic of some smart
   cities, manufacturing applications or training.

3.2.4.  Opportunities

   While delay is inherently related to information transmission and if
   we continue the analogy of the computer board to highlight some of
   the COIN capabilities in terms of computation and storage but also
   allocation of resources, there are some opportunities that XR could
   take advantage of:

   *  Reduced latency: 20 ms is usually cited as an upper limit for XR
      applications.  Storage and preprocessing of scenes in local
      elements (includng in the mobile network) could extend the reach
      of XR applications at least over the extended edge.

   *  Video transmission: The use of better transcoding, advanced
      context-based compression algorithms, pre-fetching and pre-
      caching, as well as movement prediction all help to reduce

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      bandwidth consumption.  While this is now limited to local
      processing it is not outside the realm of COIN to push some of
      these functionalities to the network especially as realted to
      caching/fetching but also context based flow direction and

   *  Monitoring: Since bandwidth and data are fundamental for XR
      deployment, COIN functionality could help to better monitor and
      distribute the XR services over collaborating network elements to
      optimize end-to-end performance.

   *  Functional decomposition: Advanced functional decomposition,
      localization, and discovery of computing and storage resources in
      the network can help to optimize user experience in general.

   *  Intelligent network management and configuration: The move to
      artificial intelligence in network management to learn about flows
      and adapt resources based on both dataplane and control plane
      programmability can help the overall deployment of XR services.

3.2.5.  Research Questions

   *  RQ 3.2.1: Can current PNDs provide the speed required for
      executing complex filtering operations, including metadata
      analysis for complex and dynamic scene rendering?

   *  RQ 3.2.2: Where should PNDs equipped with these operations be
      located for optimal performance gains?

   *  RQ 3.2.3: How can the interoperability of CPU/GPU be optimized and
      used jointly with PNDs to combine low-level packet filtering and
      redirection with the higher layer processing needed for image
      processing, feature selection, and haptics?

   *  RQ 3.2.4: Can the use of joint learning algorithms across both
      data center and edge computers be used to create optimal function
      allocation and the creation of semi-permanent datasets and
      analytics for usage trending and flow management resulting in
      better localization of XR functions?

   *  RQ 3.2.5: Can COIN improve the dynamic distribution of control,
      forwarding, and storage resources and related usage models in XR?

   *  RQ 3.2.6: How COIN provide the necessary infrastructure for the
      use of interactive XR everywhere?

3.2.6.  Requirements

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   *  Req 3.2.1: COIN systems for XR MUST allow joint collaboration
      across networks not just as part of the same subnetwork.

   *  Req 3.2.2: COIN systems for XR SHOULD provide multi-stream
      combining in the network for multi-views and efficient

   *  Req 3.2.3: COIN systems for XR SHOULD be able to dynamically
      include extra streams for data-intensive services and processes.

   *  Req 3.2.4: COIN systems for XR MAY use edge networking and
      computing for improved performance and performance management
      independent of a cloud connection.

   *  Req 3.2.5: COIN systems for XR MAY integrate local and fog caching
      with cloud-based pre-rendering.

   *  Req 3.2.6: COIN systems for XR SHOULD jointly optimize COIN and
      higher layer protocols to reduce latency especially in data-
      intensive applications at the edge.

   *  Req 3.2.7: COIN systems for XR SHOULD support nomadicity and

   *  Req 3.2.8: COIN systems for XR SHOULD provide means for
      performance optimization that reduces transmitted data and
      optimizes loss protection.

   *  Req 3.2.9: COIN systems for XR MAY provide means for trend
      analysis and telemetry.

   *  Req 3.2.10: COIN systems for XR SHOULD integrate PNDs with
      holography, 3D displays, and image rendering processors for
      offering to improve service location selections.

   *  Req 3.2.11: COIN systems for XR MAY provide means for managing the
      quality of XR sessions through reduced in-network congestion and
      improve flow delivery by determining how to prioritize XR data.

3.3.  Personalized and interactive performing arts

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3.3.1.  Description

   This use case covers live productions of the performing arts where
   the performers and audience are in different physical locations.  The
   performance is conveyed to the audience through multiple networked
   streams which may be tailored to the requirements of individual
   audience members; and the performers receive live feedback from the

   There are two main aspects: i) to emulate as closely as possible the
   experience of live performances where the performers and audience are
   co-located in the same physical space, such as a theater; and ii) to
   enhance traditional physical performances with features such as
   personalization of the experience according to the preferences or
   needs of the audience members.

   Examples of personalization include:

   *  Viewpoint selection such as choosing a specific seat in the
      theater or for more advanced positioning of the audience member's
      viewpoint outside of the traditional seating - amongst, above or
      behind the performers (but within some limits which may be imposed
      by the performers or the director for artistic reasons);

   *  Augmentation of the performance with subtitles, audio-description,
      actor-tagging, language translation, advertisements/product-
      placement, other enhancements/filters to make the performance
      accessible to disabled audience members (removal of flashing
      images for epileptics, alternative color schemes for color-blind
      audience members, etc.).

3.3.2.  Characterization

   There are several chained functional entities which are candidates
   for being deployed as (COIN) programs:

   *  Performer aggregation and editing functions

   *  Distribution and encoding functions

   *  Personalization functions

      -  to select which of the existing streams should be forwarded to
         the audience member

      -  to augment streams with additional metadata such as subtitles

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      -  to create new streams after processing existing ones, e.g., to
         interpolate between camera angles to create a new viewpoint or
         to render point clouds from the audience member's chosen

      -  to undertake remote rendering according to viewer position,
         e.g., creation of VR headset display streams according to
         audience head position - when this processing has been
         offloaded from the viewer's end-system to the COIN function due
         to limited processing power in the end-system, or to limited
         network bandwidth to receive all of the individual streams to
         be processed.

   *  Audience feedback sensor processing functions

   *  Audience feedback aggregation functions

   These are candidates for deployment as (COIN) Programs in PNDs rather
   than being located in end-systems (at the performers' site, the
   audience members' premises or in a central cloud location) for
   several reasons:

   *  Personalization of the performance according to audience
      preferences and requirements makes it unfeasible to be done in a
      centralized manner at the performer premises: the computational
      resources and network bandwidth would need to scale with the
      number of audience members' personalized streams.

   *  Rendering of VR headset content to follow viewer head movements
      has an upper bound on lag to maintain viewer QoE, which requires
      the processing to be undertaken sufficiently close to the viewer
      to avoid large network latencies.

   *  Viewer devices may not have the processing-power to undertake the
      personalization or the viewers' network may not have the capacity
      to receive all of the constituent streams to undertake the
      personalization functions.

   *  There are strict latency requirements for live and interactive
      aspects that require the deviation from the direct network path
      from performers to audience to be minimized, which reduces the
      opportunity to route streams via large-scale processing
      capabilities at centralized data-centers.

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3.3.3.  Existing solutions

   Note: Existing solutions for some aspects of this use case are
   covered in the Mobile Application Offloading, Extended Reality, and
   Content Delivery Networks use cases.

3.3.4.  Opportunities

   *  Executing media processing and personalization functions on-path
      as (COIN) Programs in PNDs can avoid detour/stretch to central
      servers, thus reducing latency and bandwidth consumption.  For
      example, the overall delay for performance capture, aggregation,
      distribution, personalization, consumption, capture of audience
      response, feedback processing, aggregation, and rendering should
      be achieved within an upper bound of latency (the tolerable amount
      is to be defined, but in the order of 100s of ms to mimic
      performers perceiving audience feedback, such as laughter or other
      emotional responses in a theater setting).

   *  Processing of media streams allows (COIN) Programs, PNDs and the
      wider (COIN) System/Environment to be contextually aware of flows
      and their requirements which can be used for determining network
      treatment of the flows, e.g., path selection, prioritization,
      multi-flow coordination, synchronization and resilience.

3.3.5.  Research Questions:

   *  RQ 3.3.1: In which PNDs should (COIN) Programs for aggregation,
      encoding and personalization functions be located?  Close to the
      performers or close to the audience members?

   *  RQ 3.3.2: How far from the direct network path from performer to
      audience should (COIN) programs be located, considering the
      latency implications of path-stretch and the availability of
      processing capacity at PNDs?  How should tolerances be defined by

   *  RQ 3.3.3: Should users decide which PNDs should be used for
      executing (COIN) Programs for their flows or should they express
      requirements and constraints that will direct decisions by the
      orchestrator/manager of the COIN System?

   *  RQ 3.3.4: How to achieve synchronization across multiple streams
      to allow for merging, audio-video interpolation, and other cross-
      stream processing functions that require time synchronization for
      the integrity of the output?  How can this be achieved considering
      that synchronization may be required between flows that are: i) on
      the same data pathway through a PND/router, ii) arriving/leaving

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      through different ingress/egress interfaces of the same PND/
      router, iii) routed through disjoint paths through different PNDs/
      routers?  This RQ raises issues associated with synchronisation
      across multiple media streams and sub-streams [RFC7272] as well as
      time synchronisation between PNDs/routers on multiple paths

   *  RQ 3.3.5: Where will COIN Programs be executed?  In the data-plane
      of PNDs, in other on-router computational capabilities within
      PNDs, or in adjacent computational nodes?

   *  RQ 3.3.6: Are computationally-intensive tasks - such as video
      stitching or media recognition and annotation (cf.  Section 3.2) -
      considered as suitable candidate (COIN) Programs or should they be
      implemented in end-systems?

   *  RQ 3.3.7: If the execution of COIN Programs is offloaded to
      computational nodes outside of PNDs, e.g. for processing by GPUs,
      should this still be considered as COIN?  Where is the boundary
      between COIN capabilities and explicit routing of flows to

3.3.6.  Requirements

   *  Req 3.3.1: Users SHOULD be able to specify requirements on network
      and processing metrics (such as latency and throughput bounds).

   *  Req 3.3.2 The COIN System SHOULD be able to respect user-specified
      requirements and constraints when routing flows and selecting PNDs
      for executing (COIN) Programs.

   *  Req 3.3.3: A COIN System SHOULD be able to synchronize flow
      treatment and processing across multiple related flows which may
      be on disjoint paths.

4.  Supporting new COIN Systems

4.1.  In-Network Control / Time-sensitive applications

4.1.1.  Description

   The control of physical processes and components of industrial
   production lines is essential for the growing automation of
   production and ideally allows for a consistent quality level.
   Traditionally, the control has been exercised by control software
   running on programmable logic controllers (PLCs) located directly
   next to the controlled process or component.  This approach is best-
   suited for settings with a simple model that is focused on a single

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   or few controlled components.

   Modern production lines and shop floors are characterized by an
   increasing number of involved devices and sensors, a growing level of
   dependency between the different components, and more complex control
   models.  A centralized control is desirable to manage the large
   amount of available information which often has to be pre-processed
   or aggregated with other information before it can be used.  PLCs are
   not designed for this array of tasks and computations could
   theoretically be moved to more powerful devices.  These devices are
   no longer close to the controlled objects and induce additional
   latency.  Moving compute functionality onto COIN execution
   environments inside the network offers a new solution space to these
   challenges, providing new compute locations with much smaller

4.1.2.  Characterization

   A control process consists of two main components as illustrated in
   Figure 2: a system under control and a controller.  In feedback
   control, the current state of the system is monitored, e.g., using
   sensors, and the controller influences the system based on the
   difference between the current and the reference state to keep it
   close to this reference state.

      state      ------------        --------    Output
   ---------->  | Controller | ---> | System | ---------->
              ^  ------------        --------       |
              |                                     |
              |   observed state                    |
              |                    ---------        |
               -------------------| Sensors | <-----

                  Figure 2: Simple feedback control model.

   Apart from the control model, the quality of the control primarily
   depends on the timely reception of the sensor feedback which can be
   subject to tight latency constraints, often in the single-digit
   millisecond range.  While low latencies are essential, there is an
   even greater need for stable and deterministic levels of latency,
   because controllers can generally cope with different levels of
   latency, if they are designed for them, but they are significantly
   challenged by dynamically changing or unstable latencies.  The
   unpredictable latency of the Internet exemplifies this problem if,
   e.g., off-premise cloud platforms are included.

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4.1.3.  Existing Solutions

   Control functionality is traditionally executed on PLCs close to the
   machinery.  These PLCs typically require vendor-specific
   implementations and are often hard to upgrade and update which makes
   such control processes inflexible and difficult to manage.  Moving
   computations to more freely programmable devices thus has the
   potential of significantly improving the flexibility.  In this
   context, directly moving control functionality to (central) cloud
   environments is generally possible, yet only feasible if latency
   constraints are lenient.

   Early approaches such as [RUETH] and [VESTIN] have already shown the
   general applicability of leveraging COIN for in-network control.

4.1.4.  Opportunities

   *  Performing simple control logic on PNDs and/or in COIN execution
      environments can bring the controlled system and the controller
      closer together, possibly satisfying the tight latency

   *  Creating a coupled control that is exercised via (i) simplified
      approximations of more complex control algorithms deployed in COIN
      execution environments, and (ii) more complex overall control
      schemes deployed in the cloud can allow for quicker, yet more
      inaccurate responses from within the network while still providing
      for sufficient control accuracy at higher latencies from afar.

4.1.5.  Research Questions

   *  RQ 4.1.1: How to derive simplified versions of the global
      (control) function?

      -  How to account for the limited computational precision of PNDs,
         typically only allowing for integer precision computation,
         while floating-point precision is needed by most control
         algorithms (cf.  [KUNZE-APPLICABILITY])?

      -  How to find suitable tradeoffs regarding simplicity of the
         control function ("accuracy of the control") and implementation
         complexity ("implementability")?

   *  RQ 4.1.2: How to distribute the simplified versions in the

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      -  Can there be different control levels, e.g., "quite inaccurate
         & very low latency" (PNDs, deep in the network), "more accurate
         & higher latency" (more powerful COIN execution environments,
         farer away), "very accurate & very high latency" (cloud
         environments, far away)?

      -  Who decides which control instance is executed and how?

      -  How do the different control instances interact?

4.1.6.  Requirements

   *  Req 4.1.1: The interaction between the COIN execution environments
      and the global controller SHOULD be explicit.

   *  Req 4.1.2: The interaction between the COIN execution environments
      and the global controller MUST NOT negatively impact the control

   *  Req 4.1.3: Actions of the COIN execution environments MUST be
      overridable by the global controller.

   *  Req 4.1.4: Functions in COIN execution environments SHOULD be
      executed with predictable delay.

   *  Req 4.1.5: Functions in COIN execution environments MUST be
      executed with predictable accuracy.

4.2.  Large Volume Applications

4.2.1.  Description

   In modern industrial networks, processes and machines are extensively
   monitored by distributed sensors with a large spectrum of
   capabilities, ranging from simple binary (e.g., light barriers) to
   sophisticated sensors with varying degrees of resolution.  Sensors
   further serve different purposes, as some are used for time-critical
   process control while others represent redundant fallback platforms.
   Overall, there is a high level of heterogeneity which makes managing
   the sensor output a challenging task.

   Depending on the deployed sensors and the complexity of the observed
   system, the resulting overall data volume can easily be in the range
   of several Gbit/s [GLEBKE].  These volumes are often already
   difficult to handle in local environments and it becomes even more
   challenging when off-premise clouds are used for managing the data.
   While large networking companies can simply upgrade their
   infrastructure to accommodate the accruing data volumes, most

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   industrial companies operate on tight infrastructure budgets and
   upgrading is hence not always feasible or possible.  A major
   challenge is thus to devise a methodology that is able to handle such
   amounts of data over limited access links.

   Data filtering and pre-processing, similar to the considerations in
   Section 3.2, can be building blocks for new solutions in this space.
   Such solutions, however, might also have to address the added
   challenge of business data leaving the premises and control of the
   company.  As this data could include sensitive information or
   valuable business secrets, additional security measures have to be
   taken.  Yet, typical security measures such as encrypting the data
   make filtering or pre-processing approaches hardly applicable as they
   typically work on unencrypted data.  Consequently, incorporating
   security into these approaches, either by adding functionality for
   handling encrypted data or devising general security measures, is
   thus an additional auspicious field for research.

4.2.2.  Characterization

   In essence, the described monitoring systems consist of sensors that
   produce large volumes of monitoring data.  This data is then
   transmitted to additional components that provide data processing and
   analysis capabilities or simply store the data in large data silos.

   As sensors are often set up redundantly, part of the collected data
   might also be redundant.  Moreover, sensors are often hard to
   configure or not configurable at all which is why their resolution or
   sampling frequency is often larger than required.  Consequently, it
   is likely that more data is transmitted than is needed or desired,
   prompting the deployment of filtering techniques.  For example, COIN
   programs deployed in the on-premise network could filter out
   redundant or undesired data before it leaves the premise using simple
   traffic filters, thus reducing the required (upload) bandwidths.  The
   available sensor data could be scaled down using packet-based sub-
   sampling or using filtering as long as the sensor value is in an
   uninteresting range while forwarding with a higher resolution once
   the sensor value range becomes interesting (cf.  [KUNZE-SIGNAL]).
   While the former variant is oblivious to the semantics of the sensor
   data, the latter variant requires an understanding of the current
   sensor levels.  In any case, it is important that end-hosts are
   informed about the filtering so that they can distinguish between
   data loss and data filtered out on purpose.

   In practice, the collected data is further processed using manifold
   computations.  Some of them are very complex or need the complete
   sensor data during the computation, but there are also simpler
   operations which can already be done on subsets of the overall

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   dataset or earlier on the communication path as soon as all data is
   available.  One example is finding the maximum of all sensor values
   which can either be done iteratively at each intermediate hop or at
   the first hop, where all data is available.  Using expert knowledge
   about the exact computation steps and the concrete transmission path
   of the sensor data, simple computation steps can thus be deployed in
   the on-premise network, again reducing the overall data volume.

4.2.3.  Existing Solutions

   Current approaches for handling such large amounts of information
   typically build upon stream processing frameworks such as Apache
   Flink.  While they allow for handling large volume applications, they
   are tied to performant server machines and upscaling the information
   density also requires a corresponding upscaling of the compute

4.2.4.  Opportunities

   *  (Semantic) packet filtering based on packet header and payload, as
      well as multi-packet information can (drastically) reduce the data
      volume, possibly even without losing any important information.

   *  (Semantic) data (pre-)processing, e.g., in the form of
      computations across multiple packets and potentially leveraging
      packet payload, can also reduce the data volume without losing any
      important information.

4.2.5.  Research Questions

   *  RQ 4.2.1: How can the overall data processing pipeline be divided
      into individual processing steps that could then be deployed as
      COIN functionality?

   *  RQ 4.2.2: How to design COIN programs for (semantic) packet

      -  Which criteria for filtering make sense?

   *  RQ 4.2.3: Which kinds of COIN programs can be leveraged for
      (pre-)processing steps?

      -  How complex can they become?

   *  RQ 4.2.4: How to distribute and coordinate COIN programs?

   *  RQ 4.2.5: How to dynamically change COIN programs?

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   *  RQ 4.2.6: How to incorporate the (pre-)processing and filtering
      steps into the overall system?

      -  How can changes to the data by COIN programs be signaled to the

4.2.6.  Requirements

   *  Req 4.2.1: Filters and preprocessors MUST conform to application-
      level syntax and semantics.

   *  Req 4.2.2: Filters and preprocessors MAY leverage packet header
      and payload information.

   *  Req 4.2.3: Filters and preprocessors SHOULD be reconfigurable at

4.3.  Industrial Safety

4.3.1.  Description

   Despite an increasing automation in production processes, human
   workers are still often necessary.  Consequently, safety measures
   have a high priority to ensure that no human life is endangered.  In
   traditional factories, the regions of contact between humans and
   machines are well-defined and interactions are simple.  Simple safety
   measures like emergency switches at the working positions are enough
   to provide a good level of safety.

   Modern factories are characterized by increasingly dynamic and
   complex environments with new interaction scenarios between humans
   and robots.  Robots can either directly assist humans or perform
   tasks autonomously.  The intersect between the human working area and
   the robots grows and it is harder for human workers to fully observe
   the complete environment.  Additional safety measures are essential
   to prevent accidents and support humans in observing the environment.

4.3.2.  Characterization

   Industrial safety measures are typically hardware solutions because
   they have to pass rigorous testing before they are certified and
   deployment-ready.  Standard measures include safety switches and
   light barriers.  Additionally, the working area can be explicitly
   divided into 'contact' and 'safe' areas, indicating when workers have
   to watch out for interactions with machinery.

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   These measures are static solutions, potentially relying on
   specialized hardware, and are challenged by the increased dynamics of
   modern factories where the factory configuration can be changed on
   demand.  Software solutions offer higher flexibility as they can
   dynamically respect new information gathered by the sensor systems,
   but in most cases they cannot give guaranteed safety.  COIN systems
   could leverage the increased availability of sensor data and the
   detailed monitoring of the factories to enable additional safety
   measures with shorter response times and higher guarantees.
   Different safety indicators within the production hall could be
   combined within the network so that PNDs can give early responses if
   a potential safety breach is detected.  For example, the positions of
   human workers and robots could be tracked and robots could be stopped
   when they get too close to a human in a non-working area or if a
   human enters a defined safety zone.  More advanced concepts could
   also include image data or combine arbitrary sensor data.

4.3.3.  Existing Solutions

   Due to the importance of safety, there is a wide range of software-
   based approaches aiming at enhancing security.  One example are tag-
   based systems, e.g., using RFID, where drivers of forklifts can be
   warned if pedestrian workers carrying tags are nearby.  Such
   solutions, however, require setting up an additional system and do
   not leverage existing sensor data.

4.3.4.  Opportunities

   *  Executing safety-critical COIN functions on PNDs could allow for
      early emergency reactions based on diverse sensor feedback with
      low latencies.

4.3.5.  Research Questions

   *  RQ 4.3.1: Which additional safety measures can be provided?

      -  Do these measures actually improve safety?

   *  RQ 4.3.2: Which sensor information can be combined and how?

4.3.6.  Requirements

   *  Req 4.3.1: COIN-based safety measures MUST NOT degrade existing
      safety measures.

   *  Req 4.3.2: COIN-based safety measures MAY enhance existing safety

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5.  Improving existing COIN capabilities

5.1.  Content Delivery Networks

5.1.1.  Description

   Delivery of content to end users often relies on Content Delivery
   Networks (CDNs).  CDNs store said content closer to end users for
   latency-reduced delivery and they often utilize DNS-based indirection
   to serve the request on behalf of the origin server.

5.1.2.  Characterization

   From the perspective of this draft, a CDN can be interpreted as a
   (network service level) set of (COIN) programs.  These programs
   implement a distributed logic for first distributing content from the
   origin server to the CDN ingress and then further to the CDN
   replication points which ultimately serve the user-facing content

5.1.3.  Existing Solutions

   CDN technologies have been well described and deployed in the
   existing Internet.  Core technologies like Global Server Load
   Balancing (GSLB) [GSLB] and Anycast server solutions are used to deal
   with the required indirection of a content request (usually in the
   form of an HTTP request) to the most suitable local CDN server.
   Content is replicated from seeding servers, which serve as injection
   points for content from content owners/producers, to the actual CDN
   servers, who will eventually serve the user's request.  The
   replication architecture and mechanisms itself differs from one (CDN)
   provider to another, and often utilizes private peering or network
   arrangements in order to distribute the content internationally and

   Studies such as those in [FCDN] have shown that content distribution
   at the level of named content, utilizing efficient (e.g., Layer 2)
   multicast for replication towards edge CDN nodes, can significantly
   increase the overall network and server efficiency.  It also reduces
   indirection latency for content retrieval as well as required edge
   storage capacity by benefiting from the increased network efficiency
   to renew edge content more quickly against changing demand.

5.1.4.  Opportunities

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   *  Supporting service-level routing of requests (service routing in
      [APPCENTRES]) to specific (COIN) program instances may improve on
      end user experience in faster retrieving (possibly also more,
      e.g., better quality) content.

   *  Supporting the constraint-based selection of a specific (COIN)
      program instance over others (constraint-based routing in
      [APPCENTRES]) may improve the overall end user experience by
      selecting a 'more suitable' (COIN) program instance over another,
      e.g., avoiding/reducing overload situations in specific (COIN)
      program instances.

   *  Supporting Layer 2 capabilities for multicast (compute
      interconnection and collective communication in [APPCENTRES]) may
      increase the network utilization and therefore increase the
      overall system utilization.

5.1.5.  Research Questions

   In addition to the research questions in Section 3.1.5:

   *  RQ 5.1.1: How to utilize L2 multicast to improve on CDN designs?
      How to utilize COIN capabilities in those designs?

   *  RQ 5.1.2: What forwarding methods may support the required
      multicast capabilities (see [FCDN])?

   *  RQ 5.1.3: What are the right routing constraints that reflect both
      compute and network capabilities?

   *  RQ 5.1.4: Could traffic steering be performed on the data path and
      per service request?  If so, what would be performance

   *  RQ 5.1.5: How could storage be traded off against frequent,
      multicast-based replication (see [FCDN])?

   *  RQ 5.1.6: What scalability limits exist for L2 multicast
      capabilities?  How to overcome them?

5.1.6.  Requirements

   Requirements 3.1.1 through 3.1.6 also apply for CDN service access.
   In addition:

   *  Req 5.1.1: Any solution SHOULD utilize Layer 2 multicast
      transmission capabilities for responses to concurrent service

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5.2.  Compute-Fabric-as-a-Service (CFaaS)

5.2.1.  Description

   Layer 2 connected compute resources, e.g., in regional or edge data
   centers, base stations, and even end user devices, provide the
   opportunity for infrastructure providers to offer CFaaS-like
   offerings to application providers.  App and service providers may
   utilize the compute fabric exposed by this CFaaS offering for the
   purposes defined through their applications and services.  In other
   words, the compute resources can be utilized to execute the desired
   (COIN) programs of which the application is composed, while utilizing
   the interconnection between those compute resources to do so in a
   distributed manner.

5.2.2.  Characterization

   We foresee those CFaaS offerings to be tenant-specific, a tenant here
   defined as the provider of at least one application.  For this, we
   foresee an interaction between CFaaS provider and tenant to
   dynamically select the appropriate resources to define the demand
   side of the fabric.  Conversely, we also foresee the supply side of
   the fabric to be highly dynamic with resources being offered to the
   fabric through, e.g., user-provided resources (whose supply might
   depend on highly context-specific supply policies) or infrastructure
   resources of intermittent availability such as those provided through
   road-side infrastructure in vehicular scenarios.

   The resulting dynamic demand-supply matching establishes a dynamic
   nature of the compute fabric that in turn requires trust
   relationships to be built dynamically between the resource
   provider(s) and the CFaaS provider.  This also requires the
   communication resources to be dynamically adjusted to suitably
   interconnect all resources into the (tenant-specific) fabric exposed
   as CFaaS.

5.2.3.  Existing Solutions

   There exist a number of technologies to build non-local (wide area)
   Layer 2 networks, which in turn allows for connecting compute
   resources for a distributed computational task.  5G-LAN [SA2-5GLAN]
   specifies a cellular L2 bearer for interconnecting L2 resources
   within a single cellular operator.  The work in [ICN5GLAN] outlines
   using a path-based forwarding solution over 5G-LAN as well as SDN-
   based LAN connectivity together with an ICN-based naming of IP and
   HTTP-level resources to achieve computational interconnections,
   including scenarios such as those outlined in Section 3.1.  L2
   network virtualization (see, e.g., [L2Virt]) is one of the methods

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   used for realizing so-called 'cloud-native' applications for
   applications developed with 'physical' networks in mind, thus forming
   an interconnected compute and storage fabric.

5.2.4.  Opportunities

   *  Supporting service-level routing of compute resource requests
      (service routing in [APPCENTRES]) may allow for utilizing the
      wealth of compute resources in the overall CFaaS fabric for
      execution of distributed applications, where the distributed
      constituents of those applications are realized as (COIN) programs
      and executed within a COIN system as (COIN) program instances.

   *  Supporting the constraint-based selection of a specific (COIN)
      program instance over others (constraint-based routing in
      [APPCENTRES]) will allow for optimizing both the CFaaS provider
      constraints as well as tenant-specific constraints.

   *  Supporting Layer 2 capabilities for multicast (compute
      interconnection and collective communication in [APPCENTRES]) will
      allow for increasing both network utilization but also possible
      compute utilization (due to avoiding unicast replication at those
      compute endpoints), thereby decreasing total cost of ownership for
      the CFaaS offering.

5.2.5.  Research Questions

   In addition to the research questions in Section 3.1.5:

   *  RQ 5.2.1: How to convey tenant-specific requirements for the
      creation of the L2 fabric?

   *  RQ 5.2.2: How to dynamically integrate resources, particularly
      when driven by tenant-level requirements and changing service-
      specific constraints?

   *  RQ 5.2.3: How to utilize COIN capabilities to aid the availability
      and accountability of resources, i.e., what may be (COIN) programs
      for a CFaaS environment that in turn would utilize the distributed
      execution capability of a COIN system?

5.2.6.  Requirements

   Requirements 3.1.1 through 3.1.6 also apply for the provisioning of
   services atop the CFaaS.  In addition:

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   *  Req 5.2.1: Any solution SHOULD expose means to specify the
      requirements for the tenant-specific compute fabric being utilized
      for the service execution.

   *  Req 5.2.2: Any solution SHOULD allow for dynamic integration of
      compute resources into the compute fabric being utilized for the
      app execution; those resources include, but are not limited to,
      end user provided resources.  From a COIN system perspective, new
      resources must be possible to be exposed as possible (COIN)
      execution environments.

   *  Req 5.2.3: Any solution MUST provide means to optimize the
      interconnection of compute resources, including those dynamically
      added and removed during the provisioning of the tenant-specific
      compute fabric.

   *  Req 5.2.4: Any solution MUST provide means for ensuring that
      availability and usage of resources is accounted for.

5.3.  Virtual Networks Programming

5.3.1.  Description

   The term "virtual network programming" is proposed to describe
   mechanisms by which tenants deploy and operate COIN programs in their
   virtual network.  Such COIN programs can, e.g., be P4 programs,
   OpenFlow rules, or higher layer programs.  This feature can enable
   other use cases described in this draft to be deployed using virtual
   networks services, over underlying networks such as datacenters,
   mobile networks, or other fixed or wireless networks.

   For example, COIN programs could perform the following on a tenant's
   virtual network:

   *  Allow or block flows, and request rules from an SDN controller for
      each new flow, or for flows to or from specific hosts that need
      enhanced security

   *  Forward a copy of some flows towards a node for storage and

   *  Update counters based on specific sources/destinations or
      protocols, for detailed analytics

   *  Associate traffic between specific endpoints, using specific
      protocols, or originated from a given application, to a given
      slice, while other traffic uses a default slice

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   *  Experiment with a new routing protocol (e.g., ICN), using a P4
      implementation of a router for this protocol

5.3.2.  Characterization

   To provide a concrete example of virtual COIN programming, we
   consider a use case using a 5G underlying network, the 5GLAN
   virtualization technology, and the P4 programming language and
   environment.  Section 5.1 of [I-D.ravi-icnrg-5gc-icn] provides a
   description of the 5G network functions and interfaces relevant to
   5GLAN, which are otherwise specified in [TS23.501] and [TS23.502].
   From the 5GLAN service customer/tenant standpoint, the 5G network
   operates as a switch.

   In the use case depicted in Figure 3, the tenant operates a network
   including a 5GLAN network segment (seen as a single logical switch),
   as well as fixed segments.  The mobile devices (or User Equipment
   nodes) UE1, UE2, UE3 and UE4 are in the same 5GLAN, as well as
   Device1 and Device2 (through UE4).  This scenario can take place in a
   plant or enterprise network, using, e.g., a 5G Non-Public Network.
   The tenant uses P4 programs to determine the operation of both the
   fixed and 5GLAN switches.  The tenant provisions a 5GLAN P4 program
   into the mobile network, and can also operate a controller.

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                                        ..... Tenant ........
                             P4 program :                   :
                             deployment :         Operation :
                                        V                   :
     +-----+  air interface +----------------+              :
     | UE1 +----------------+                |              :
     +-----+                |                |              :
                            |                |              :
     +-----+                |                |              V
     | UE2 +----------------+     5GLAN      |      +------------+
     +-----+                |    Logical     +------+ Controller |
                            |     Switch     |  P4  +-------+----+
     +-----+                |                |  runtime     |
     | UE3 +----------------+                |  API         |
     +-----+                |                |              |
                            |                |              |
     +-----+                |                |              |
   +-+ UE4 +----------------+                |              |
   | +-----+                +----------------+              |
   |                                                        |
   | Fixed or wireless connection                           |
   |                                    P4 runtime API      |
   |  +---------+           +-------------------------------+
   +--+ Device1 |           |
   |  +---------+           |
   |                        |
   |  +---------+    +------+-----+
   `--+ Device2 +----+ P4 Switch  +--->(fixed network)
      +---------+    +------------+

             Figure 3: 5G Virtual Network Programming Overview

5.3.3.  Existing Solutions

   Research has been conducted, for example by [Stoyanov], to enable P4
   network programming of individual virtual switches.  To our
   knowledge, no complete solution has been developed for deploying
   virtual COIN programs over mobile or datacenter networks.

5.3.4.  Opportunities

   Virtual network programming by tenants could bring benefits such as:

   *  A unified programming model, which can facilitate porting COIN
      programs between data centers, 5G networks, and other fixed and
      wireless networks, as well as sharing controller, code and

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   *  Increasing the level of customization available to customers/
      tenants of mobile networks or datacenters compared to typical
      configuration capabilities.  For example, 5G network evolution
      points to an ever increasing specialization and customization of
      private mobile networks, which could be handled by tenants using a
      programming model similar to P4.

   *  Using network programs to influence underlying network services,
      e.g., request specific QoS for some flows in 5G or datacenters, to
      increase the level of in-depth customization available to tenants.

5.3.5.  Research Questions

   *  RQ 5.3.1: Underlying Network Awareness: a virtual COIN program can
      be able to influence, and be influenced by, the underling network.
      Since some information and actions may be available on some nodes
      and not others, underlying network awareness may impose additional
      constraints on distributed network programs location.

   *  RQ 5.3.2: Splitting/Distribution: a virtual COIN program may need
      to be deployed across multiple computing nodes, leading to
      research questions around instance placement and distribution.
      For example, program logic should be applied exactly once or at
      least once per packet, while allowing optimal forwarding path by
      the underlying network.  Research challenges include defining
      manual (by the programmer) or automatic methods to distribute COIN
      programs that use a low or minimal amount of resources.
      Distributed P4 programs are studied in
      [I-D.hsingh-coinrg-reqs-p4comp] and [Sultana].

   *  RQ 5.3.3: Multi-Tenancy Support: multiple virtual COIN program
      instances can run on the same compute node.  While mechanisms were
      proposed for P4 multi-tenancy in a switch [Stoyanov], research
      questions remain about isolation between tenants and fair
      repartition of resources.

   *  RQ 5.3.4: Security: how can tenants and underlying networks be
      protected against security risks, including overuse or misuse of
      network resources, injection of traffic, or access to unauthorized

   *  RQ 5.3.5: Higher layer processing: can a virtual network model
      facilitate the deployment of COIN programs acting on application
      layer data?  This is an open question since the present section
      focused on packet/flow processing.

5.3.6.  Requirements

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   *  Req 5.3.1: A COIN system supporting virtualization SHOULD enable
      tenants to deploy COIN programs onto their virtual networks.

   *  Req 5.3.2: A virtual COIN program SHOULD process flows/packets
      once and only once (or at least once for idempotent operations),
      even if the program is distributed over multiple PNDs.

   *  Req 5.3.3: Multi-tenancy SHOULD be supported for virtual COIN
      programs, i.e., instances of virtual COIN programs from different
      tenants can share underlying PNDs.  This includes requirements for
      secure isolation between tenants, and fair (or policy-based)
      sharing of computing resources.

   *  Req 5.3.4: Virtual COIN programs SHOULD support mobility of

6.  Enabling new COIN capabilities

6.1.  Distributed AI

6.1.1.  Description

   There is a growing range of use cases demanding for the realization
   of AI capabilities among distributed endpoints.  Such demand may be
   driven by the need to increase overall computational power for large-
   scale problems.  From a COIN perspective, those capabilities may be
   realized as (COIN) programs and executed throughout the COIN system,
   including in PNDs.

   Some solutions may desire the localization of reasoning logic, e.g.,
   for deriving attributes that better preserve privacy of the utilized
   raw input data.  Quickly establishing (COIN) program instances in
   nearby compute resources, including PNDs, may even satisfy such
   localization demands on-the-fly (e.g., when a particular use is being
   realized, then terminated after a given time).

6.1.2.  Characterization

   Examples for large-scale AI problems include biotechnology and
   astronomy related reasoning over massive amounts of observational
   input data.  Examples for localizing input data for privacy reasons
   include radar-like application for the development of topological
   mapping data based on (distributed) radio measurements at base
   stations (and possibly end devices), while the processing within
   radio access networks (RAN) already constitutes a distributed AI
   problem to a certain extent albeit with little flexibility in
   distributing the execution of the AI logic.

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6.1.3.  Existing Solutions

   Reasoning frameworks, such as TensorFlow, may be utilized for the
   realization of the (distributed) AI logic, building on remote service
   invocation through protocols such as gRPC [GRPC] or MPI [MPI] with
   the intention of providing an on-chip NPU (neural processor unit)
   like abstraction to the AI framework.

   A number of activities on distributed AI exist in the area of
   developing the 5th and 6th generation mobile network with various
   activities in the 3GPP SDO as well as use cases developed for the
   ETSI MEC initiative mentioned in previous use cases.

6.1.4.  Opportunities

   *  Supporting service-level routing of requests (service routing in
      [APPCENTRES]), with AI services being exposed to the network and
      executed as part of (COIN) programs in selected (COIN) program
      instances, may provide a highly distributed execution of the
      overall AI logic, thereby addressing, e.g., localization but also
      computational concerns (scale-in/out).

   *  The support for constraint-based selection of a specific (COIN)
      program instance over others (constraint-based routing in
      [APPCENTRES]) may allow for utilizing the most suitable HW
      capabilities (e.g., support for specific AI HW assistance in the
      COIN element, including a PND), while also allowing to select
      resources, e.g., based on available compute ability such as number
      of cores to be used.

   *  Supporting collective communication between multiple instances of
      AI services, i.e., (COIN) program instances, may positively impact
      network but also compute utilization by moving from unicast
      replication to network-assisted multicast operation.

6.1.5.  Research Questions

   In addition to the research questions in Section 3.1.5:

   *  RQ 6.1.1: What are the communication patterns that may be
      supported by collective communication solutions?

   *  RQ 6.1.2: How to achieve scalable multicast delivery with rapidly
      changing receiver sets?

   *  RQ 6.1.3: What COIN capabilities may support the collective
      communication patterns found in distributed AI problems?

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   *  RQ 6.1.4: How to provide a service routing capability that
      supports any invocation protocol (beyond HTTP)?

6.1.6.  Requirements

   Requirements 3.1.1 through 3.1.6 also apply for general distributed
   AI capabilities.  In addition:

   *  Req 6.1.1: Any COIN system MUST provide means to specify the
      constraints for placing (AI) execution logic in the form of (COIN)
      programs in certain logical execution points (and their associated
      physical locations), including PNDs.

   *  Req 6.1.2: Any COIN system MUST provide support for app/micro-
      service specific invocation protocols for requesting (COIN)
      program services exposed to the COIN system.

7.  Security Considerations

   Deploying COIN solutions to the use cases described in this document
   may pose a risk for security breaches if the solutions are not
   deployed with security and authentication mechanisms in place.  In
   particular, many early PND-based approaches work on unencrypted plain
   text data and often customize packet payload to account for missing
   capabilities of early-generation PNDs.  Such need for operating on
   unencrypted data either limits the applicability of COIN solutions to
   those parts of the packet transfer that happens to be unencrypted or
   poses a strong requirement to user data to not being encrypted for
   the sake of utilizing COIN capabilities; a requirement hardly aligned
   with the strong trend to encrypting all user-related data in
   traversing packets.  Thus, even without having analyzed the use cases
   in more detail in this document, designing meaningful solutions for
   providing authentication as well as incorporating the rightfully
   ongoing trend to more and more end-to-end encrypted traffic into COIN
   will be key.

8.  IANA Considerations


9.  Conclusion

   This document presented use cases gathered from several fields that
   can and could profit from capabilities that are provided by in-
   network and, more generally, distributed compute capabilities.  We
   distinguished between use cases in which COIN may enable new
   experiences (Section 3), expose new features (Section 6), or improve
   on existing system capabilities (Section 5), and other use cases

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   where COIN capabilities enable totally new applications, for example,
   in industrial networking (Section 4).

   Beyond the mere description and characterization of those use cases,
   we identified opportunities arising from utilizing COIN capabilities
   as well as research questions that may need to be addressed before
   being able to reap those opportunities.  We also outlined possible
   requirements for building a COIN system that may realize these use

   We acknowledge that this work offers no comprehensive overview of
   possible use cases and is thus only a snapshot of what may be
   possible if COIN capabilities existed.
   In fact, the decomposition of many current client-server applications
   into node by node transit could identify other opportunities for
   adding computing to forwarding notably in supply-chain, health care,
   intelligent cities and transportation and even financial services
   (among others).

   With this in mind, updates to this document might become necessary or
   desirable in the future to capture this extended view on what may be
   possible.  We are, however, confident that the current selection of
   use cases, each describing the dimensions of opportunities, research
   questions, and requirements, already represents a useful set of
   scenarios that yield themselves for a subsequent analysis that is
   currently intended to be performed in [USECASEANALYSIS].  Through
   this, the use cases presented here together with the intended
   analysis provide direct input into the milestones of the COIN RG in
   terms of required functionalities.

10.  Acknowledgements

   The authors would like to thank Chathura Sarathchandra for reviewing
   the document and David Oran, Phil Eardley, Stuart Card, Jeffrey He,
   Toerless Eckert, and Jon Crowcroft for providing comments on earlier
   versions of the document.

11.  References

11.1.  Normative References

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

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   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

11.2.  Informative References

              Trossen, D., Sarathchandra, C., and M. Boniface, "In-
              Network Computing for App-Centric Micro-Services", Work in
              Progress, Internet-Draft, draft-sarathchandra-coin-
              appcentres-04, 26 January 2021,

   [ETSI]     ETSI, "Multi-access Edge Computing (MEC)", 2022,

   [FCDN]     Al-Naday, M., Reed, M. J., Riihijarvi, J., Trossen, D.,
              Thomos, N., and M. Al-Khalidi, "A Flexible and Efficient
              CDN Infrastructure without DNS Redirection of Content
              Reflection", <>.

   [GLEBKE]   Glebke, R., Henze, M., Wehrle, K., Niemietz, P., Trauth,
              D., Mattfeld MBA, P., and T. Bergs, "A Case for Integrated
              Data Processing in Large-Scale Cyber-Physical Systems",
              Proceedings of the Annual Hawaii International Conference
              on System Sciences, DOI 10.24251/hicss.2019.871, 2019,

   [GRPC]     "High performance open source universal RPC framework",

   [GSLB]     Cloudflare, "What is global server load balancing
              (GSLB)?", 2022,

              Singh, H. and M. Montpetit, "Requirements for P4 Program
              Splitting for Heterogeneous Network Nodes", Work in
              Progress, Internet-Draft, draft-hsingh-coinrg-reqs-p4comp-
              03, 18 February 2021,

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              Ravindran, R., Suthar, P., Trossen, D., Wang, C., and G.
              White, "Enabling ICN in 3GPP's 5G NextGen Core
              Architecture", Work in Progress, Internet-Draft, draft-
              ravi-icnrg-5gc-icn-04, 31 May 2019,

   [ICN5GLAN] Trossen, D., Wang, C., Robitzsch, S., Reed, M., AL-Naday,
              M., and J. Riihijarvi, "IP-based Services over ICN in 5G
              LAN Environments", Work in Progress, Internet-Draft,
              draft-trossen-icnrg-ip-icn-5glan-00, 6 June 2019,

              Kunze, I., Glebke, R., Scheiper, J., Bodenbenner, M.,
              Schmitt, R., and K. Wehrle, "Investigating the
              Applicability of In-Network Computing to Industrial
              Scenarios", 2021 4th IEEE International Conference on
              Industrial Cyber-Physical Systems (ICPS),
              DOI 10.1109/icps49255.2021.9468247, May 2021,

              Kunze, I., Niemietz, P., Tirpitz, L., Glebke, R., Trauth,
              D., Bergs, T., and K. Wehrle, "Detecting Out-Of-Control
              Sensor Signals in Sheet Metal Forming using In-Network
              Computing", 2021 IEEE 30th International Symposium on
              Industrial Electronics (ISIE),
              DOI 10.1109/isie45552.2021.9576221, June 2021,

   [L2Virt]   Kreger-Stickles, L., "First principles: L2 network
              virtualization for lift and shift", 2022,

   [MPI]      Vishnu, A., Siegel, C., and J. Daily, "Scaling Distributed
              Machine Learning with In-Network Aggregation",

   [RFC7272]  van Brandenburg, R., Stokking, H., van Deventer, O.,
              Boronat, F., Montagud, M., and K. Gross, "Inter-
              Destination Media Synchronization (IDMS) Using the RTP
              Control Protocol (RTCP)", RFC 7272, DOI 10.17487/RFC7272,
              June 2014, <>.

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   [RFC8039]  Shpiner, A., Tse, R., Schelp, C., and T. Mizrahi,
              "Multipath Time Synchronization", RFC 8039,
              DOI 10.17487/RFC8039, December 2016,

   [RUETH]    Rueth, J., Glebke, R., Wehrle, K., Causevic, V., and S.
              Hirche, "Towards In-Network Industrial Feedback Control",
              Proceedings of the 2018 Morning Workshop on In-
              Network Computing, DOI 10.1145/3229591.3229592, August
              2018, <>.

              3GPP-5glan, "SP-181129, Work Item Description,
              Vertical_LAN(SA2), 5GS Enhanced Support of Vertical and
              LAN Services", 3GPP , 2021,

   [Stoyanov] Stoyanov, R. and N. Zilberman, "MTPSA: Multi-Tenant
              Programmable Switches", ACM P4 Workshop in Europe
              (EuroP4'20) , 2020,

   [Sultana]  Sultana, N., Sonchack, J., Giesen, H., Pedisich, I., Han,
              Z., Shyamkumar, N., Burad, S., DeHon, A., and B. T. Loo,
              "Flightplan: Dataplane Disaggregation and Placement for P4
              Programs", 2020,

              Kunze, I., Wehrle, K., Trossen, D., Montpetit, M., de Foy,
              X., Griffin, D., and M. Rio, "Terminology for Computing in
              the Network", Work in Progress, Internet-Draft, draft-
              irtf-coinrg-coin-terminology-00, 10 March 2023,

   [TS23.501] 3gpp-23.501, "Technical Specification Group Services and
              System Aspects; System Architecture for the 5G System;
              Stage 2 (Rel.17)", 3GPP , 2021,

   [TS23.502] 3gpp-23.502, "Technical Specification Group Services and
              System Aspects; Procedures for the 5G System; Stage 2
              (Rel.17)", 3GPP , 2021,

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              Kunze, I., Wehrle, K., Trossen, D., Montpetit, M., de Foy,
              X., Griffin, D., and M. Rio, "Use Case Analysis for
              Computing in the Network", Work in Progress, Internet-
              Draft, draft-irtf-coinrg-use-case-analysis-00, 10 March
              2023, <

   [VESTIN]   Vestin, J., Kassler, A., and J. Akerberg, "FastReact: In-
              Network Control and Caching for Industrial Control
              Networks using Programmable Data Planes", 2018 IEEE 23rd
              International Conference on Emerging Technologies and
              Factory Automation (ETFA), DOI 10.1109/etfa.2018.8502456,
              September 2018,

Authors' Addresses

   Ike Kunze
   RWTH Aachen University
   Ahornstr. 55
   D-52074 Aachen

   Klaus Wehrle
   RWTH Aachen University
   Ahornstr. 55
   D-52074 Aachen

   Dirk Trossen
   Huawei Technologies Duesseldorf GmbH
   Riesstr. 25C
   D-80992 Munich

   Marie-Jose Montpetit
   Concordia University
   1455 De Maisonneuve
   Montreal  H3G 1M8

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   Xavier de Foy
   InterDigital Communications, LLC
   1000 Sherbrooke West
   Montreal  H3A 3G4

   David Griffin
   University College London
   Gower St
   WC1E 6BT
   United Kingdom

   Miguel Rio
   University College London
   Gower St
   WC1E 6BT
   United Kingdom

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