COINRG                                                          I. Kunze
Internet-Draft                                                 K. Wehrle
Intended status: Informational                               RWTH Aachen
Expires: 8 September 2022                                     D. Trossen
                                                          M.J. Montpetit
                                                               X. de Foy
                                        InterDigital Communications, LLC
                                                              D. Griffin
                                                                  M. Rio
                                                            7 March 2022

                   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
   in several contexts, it has to be carefully placed into the context
   of the general Internet communication.

   This document discusses some use cases to demonstrate how real
   applications can benefit from COIN and to showcase essential
   requirements that have to be fulfilled by COIN applications.

Status of This Memo

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   This Internet-Draft will expire on 8 September 2022.

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Copyright Notice

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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   4
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   6
   3.  Providing New COIN Experiences  . . . . . . . . . . . . . . .   6
     3.1.  Mobile Application Offloading . . . . . . . . . . . . . .   6
       3.1.1.  Description . . . . . . . . . . . . . . . . . . . . .   6
       3.1.2.  Characterization  . . . . . . . . . . . . . . . . . .   7
       3.1.3.  Existing Solutions  . . . . . . . . . . . . . . . . .   9
       3.1.4.  Opportunities . . . . . . . . . . . . . . . . . . . .   9
       3.1.5.  Research Questions  . . . . . . . . . . . . . . . . .   9
       3.1.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  10
     3.2.  Extended Reality and Immersive Media  . . . . . . . . . .  11
       3.2.1.  Description . . . . . . . . . . . . . . . . . . . . .  11
       3.2.2.  Characterization  . . . . . . . . . . . . . . . . . .  11
       3.2.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  12
       3.2.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  13
       3.2.5.  Research Questions  . . . . . . . . . . . . . . . . .  13
       3.2.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  14
     3.3.  Personalised and interactive performing arts  . . . . . .  14
       3.3.1.  Description . . . . . . . . . . . . . . . . . . . . .  15
       3.3.2.  Characterization  . . . . . . . . . . . . . . . . . .  15
       3.3.3.  Existing solutions  . . . . . . . . . . . . . . . . .  17
       3.3.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  17
       3.3.5.  Research Questions: . . . . . . . . . . . . . . . . .  17
       3.3.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  18
   4.  Supporting new COIN Systems . . . . . . . . . . . . . . . . .  18
     4.1.  Industrial Network Scenario . . . . . . . . . . . . . . .  19
     4.2.  In-Network Control / Time-sensitive applications  . . . .  20
       4.2.1.  Description . . . . . . . . . . . . . . . . . . . . .  20
       4.2.2.  Characterization  . . . . . . . . . . . . . . . . . .  21
       4.2.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  21
       4.2.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  22
       4.2.5.  Research Questions  . . . . . . . . . . . . . . . . .  22

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       4.2.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  23
     4.3.  Large Volume Applications - Filtering . . . . . . . . . .  23
       4.3.1.  Description . . . . . . . . . . . . . . . . . . . . .  23
       4.3.2.  Characterization  . . . . . . . . . . . . . . . . . .  24
       4.3.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  25
       4.3.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  25
       4.3.5.  Research Questions  . . . . . . . . . . . . . . . . .  26
       4.3.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  26
     4.4.  Large Volume Applications - (Pre-)Preprocessing . . . . .  26
       4.4.1.  Description . . . . . . . . . . . . . . . . . . . . .  26
       4.4.2.  Characterization  . . . . . . . . . . . . . . . . . .  26
       4.4.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  27
       4.4.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  27
       4.4.5.  Research Questions  . . . . . . . . . . . . . . . . .  27
       4.4.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  27
     4.5.  Industrial Safety . . . . . . . . . . . . . . . . . . . .  28
       4.5.1.  Description . . . . . . . . . . . . . . . . . . . . .  28
       4.5.2.  Characterization  . . . . . . . . . . . . . . . . . .  28
       4.5.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  28
       4.5.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  29
       4.5.5.  Research Questions  . . . . . . . . . . . . . . . . .  29
       4.5.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  29
   5.  Improving existing COIN capabilities  . . . . . . . . . . . .  29
     5.1.  Content Delivery Networks . . . . . . . . . . . . . . . .  29
       5.1.1.  Description . . . . . . . . . . . . . . . . . . . . .  29
       5.1.2.  Characterization  . . . . . . . . . . . . . . . . . .  30
       5.1.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  30
       5.1.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  30
       5.1.5.  Research Questions  . . . . . . . . . . . . . . . . .  30
       5.1.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  31
     5.2.  Compute-Fabric-as-a-Service (CFaaS) . . . . . . . . . . .  31
       5.2.1.  Description . . . . . . . . . . . . . . . . . . . . .  31
       5.2.2.  Characterization  . . . . . . . . . . . . . . . . . .  31
       5.2.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  32
       5.2.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  32
       5.2.5.  Research Questions  . . . . . . . . . . . . . . . . .  32
       5.2.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  33
     5.3.  Virtual Networks Programming  . . . . . . . . . . . . . .  33
       5.3.1.  Description . . . . . . . . . . . . . . . . . . . . .  33
       5.3.2.  Characterization  . . . . . . . . . . . . . . . . . .  34
       5.3.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  36
       5.3.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  36
       5.3.5.  Research Questions  . . . . . . . . . . . . . . . . .  37
       5.3.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  38
   6.  Enabling new COIN capabilities  . . . . . . . . . . . . . . .  38
     6.1.  Distributed AI  . . . . . . . . . . . . . . . . . . . . .  38
       6.1.1.  Description . . . . . . . . . . . . . . . . . . . . .  38
       6.1.2.  Characterization  . . . . . . . . . . . . . . . . . .  39

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       6.1.3.  Existing Solutions  . . . . . . . . . . . . . . . . .  39
       6.1.4.  Opportunities . . . . . . . . . . . . . . . . . . . .  39
       6.1.5.  Research Questions  . . . . . . . . . . . . . . . . .  40
       6.1.6.  Requirements  . . . . . . . . . . . . . . . . . . . .  40
   7.  Analysis  . . . . . . . . . . . . . . . . . . . . . . . . . .  40
     7.1.  Opportunities . . . . . . . . . . . . . . . . . . . . . .  40
     7.2.  Research Questions  . . . . . . . . . . . . . . . . . . .  41
       7.2.1.  Categorization  . . . . . . . . . . . . . . . . . . .  41
       7.2.2.  Analysis  . . . . . . . . . . . . . . . . . . . . . .  42
     7.3.  Requirements  . . . . . . . . . . . . . . . . . . . . . .  49
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  49
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  49
   10. Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  49
   11. List of Use Case Contributors . . . . . . . . . . . . . . . .  50
   12. References  . . . . . . . . . . . . . . . . . . . . . . . . .  50
     12.1.  Normative References . . . . . . . . . . . . . . . . . .  50
     12.2.  Informative References . . . . . . . . . . . . . . . . .  50
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  53

1.  Introduction

   The Internet was designed as a best-effort packet network that offers
   limited guarantees regarding the timely and successful transmission
   of packets.  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 design choice has shown suitable for a wide
   variety of applications and has helped in the rapid growth of the

   However, with the expansion of the Internet, 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, 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 core to the efforts in the COIN RG; we
   refer to those capabilities as 'COIN capabilities' in the remainder
   of the document.

   We believe that such 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

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   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 the presence
   of computing resources inside the network impacts existing services
   and applications or allows for innovation in emerging fields.

   Through delving into some individual examples within each of the
   above categories, we aim to outline opportunities and propose
   possible research questions for consideration by the wider community
   when pushing forward the 'in-network computing' vision.  Furthermore,
   insights into possible requirements for an evolving solution space of
   collected COIN capabilities is another objective of the individual
   use case descriptions.  This results in the following taxonomy used
   to describe each of the use cases:

   1.  Description: Purpose of the use case and 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: Describe, if existing, current methods that
       may realize the use case.

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

   5.  Research questions: State essential questions that are suitable
       for guiding research to achieve the outlined opportunities

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

   In Section 7, we will summarize the key research questions across all
   use cases and identify key requirements across all use cases.  This
   will provide a useful input into future roadmapping on what COIN
   capabilities may emerge and how solutions of such capabilities may
   look like.  It will also identify what open questions remain for
   these use cases to materialize as well as define requirements to
   steer future (COIN) research work.

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2.  Terminology

   The following terminology has been partly aligned with

   (COIN) Program: a set of computations requested by a user

   (COIN) Program Instance: one currently executing instance of a

   (COIN) Function: a specific computation that can be invoked as part
   of a program

   COIN Capability: a feature enabled through the joint processing of
   computation and communication resources in the network

   COIN Experience: a new user experience brought about through the
   utilization of COIN capabilities

   Programmable Network Devices (PNDs): network devices, such as network
   interface cards and switches, which are programmable, e.g., using P4
   or other languages.

   (COIN) Execution Environment: a class of target environments for
   function execution, for example, a JVM-based execution environment
   that can run functions represented in JVM byte code

   COIN System: the PNDs (and end systems) and their execution
   environments, together with the communication resources
   interconnecting them, operated by a single provider or through
   interactions between multiple providers that jointly offer COIN

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

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 combining
   with input data received from sensors in the VR headset.

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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) programs may be left in the
   headset, while the compute intensive real-time VR content processing
   (COIN) programs 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 (COIN) functions may be moved to other
   (e.g., more suitable) devices, including PNDs, which have exposed the
   corresponding (COIN) programs as individual (COIN) program instances
   to the (COIN) system by means of a 'service identifier'.  The result
   of the latter 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 suitable 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 (COIN) program deployment (and
   orchestration) on most suitable end systems or PNDs ('processing

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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

   NOTE: material on solutions like ETSI MEC will be added here later

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 application towards a possible
      distributed execution of the constituent (COIN) programs that are
      part of the overall mobile application.

   *  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 identifying service-level in-network computing
      elements will allow for routing service requests to those COIN
      elements, including PNDs, therefore possibly allowing for new in-
      network 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

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   *  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 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?

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

   *  RQ 3.1.8: What in-network 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 requests

   *  (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

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   *  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.

3.2.  Extended Reality and Immersive Media

3.2.1.  Description

   Virtual Reality (VR), Augmented Reality (AR) and immersive media (the
   metaverse) taken together as Extended Reality (XR) are the drivers of
   a number of advances in interactive technologies.  XR is one example
   of the Multisource-Multidestination Problem that combines video,
   haptics, and tactile experiences in interactive or networked multi-
   party and social interactions.  While initially associated with
   gaming and entertainment, XR applications now include remote
   diagnosis, maintenance, telemedicine, manufacturing and assembly,
   autonomous systems, smart cities, and immersive classrooms.

   Because XR requirements include the need to provide real-time
   interactivity for immersive and increasingly mobile immersive
   applications with tactile and time-sensitive data and high bandwidth
   for high resolution images and local rendering for 3D images and
   holograms, they are difficult to run over traditional networks; in
   consequence innovation is needed to deply the full potential of the

3.2.2.  Characterization

   Collaborative XR experiences 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.  XR deals with personal information
   and potentially protected content this an 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 can use recent trend analysis and mechanisms, including
   machine learning to find these trends and reduce the size of the data
   sets.  Video holography and haptics require very low delay or
   generate large amounts of data, both requiring a careful look at data
   filtering and reduction, functional distribution and partitioning.

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   The operation of XR over networks requires some computing in the
   nodes from content source to destination.  But a lot of these remain
   in the realm of research to resolve the resource allocation problem
   and provide adequate quality of experience.  These include multi-
   variate and heterogeneous goal optimization problems at merging nodes
   requiring advanced analysis.  Image rendering and video processing in
   XR leverages different HW capabilities combinations of CPU and GPU at
   the edge (even at the mobile edge) and in the fog network where the
   content is consumed.  It is important to note that the use of in-
   network computing for XR does not imply a specific protocol but
   targets an architecture enabling the deployment of the services.

3.2.3.  Existing Solutions

   In-network computing for XR profits from the heritage of extensive
   research in the past years on Information Centric Networking, Machine
   Learning, network telemetry, imaging and IoT as well as distributed
   security and in-network coding.

   *  Enabling Scalable Edge Video Analytics with Computing-In-Network
      (Jun Chen Jiang of the University of Chicago): this work brings a
      periodical re-profiling to adapt the video pipeline to the dynamic
      video content that is a characteristic of XR.  The implication is
      that we "need tight network-app coupling" for real time video

   *  VR journalism, interactive VR movies and meetings in cyberspace
      (many projects PBS, MIT interactive documentary lab, Huawei
      research - references to be provided): typical VR is not made for
      multiparty and these applications require a tight coupling of the
      local and remote rendering and data capture and combinations of
      cloud (for more static information) and edge (for dynamic

   *  Local rendering of holographic content using near field
      computation (heritage from advances cockpit interactions - looking
      for non military papers): a lot has been said recently of the
      large amounts of data necessary to transmit and use holographic
      imagery in communications.  Transmitting the near field
      information and rendering the image locally allows to reduce the
      data rates by 1 or 2.

   *  ICE-AR [ICE] project at UCLA (Jeff Burke): while this project is a
      showcase of the NDN network artchitecture it also uses a lof of
      edge-cloud capabilities for example for inter-server games and
      advanced video applications.

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3.2.4.  Opportunities

   *  Reduced latency: the physical distance between the content cloud
      and the users must be short enough to limit the propagation delay
      to the 20 ms usually cited for XR applications; the use of local
      CPU and IoT devices for range of interest (RoI) detection and
      fynamic rendering may enable this.

   *  Video transmission: better transcoding and use of advanced
      context-based compression algorithms, pre-fetching and pre-caching
      and movement prediction not only in the cloud.

   *  Monitoring: telemetry is a major research topic for COIN and it
      enables to monitor and distribute the XR services.

   *  Network access: push some networking functions in the kernel space
      into the user space to enable the deployment of stream specific
      algorithms for congestion control and application-based load
      balancing based on machine learning and user data patterns.

   *  Functional decomposition: functional decomposition, localization
      and discovery of computing and storage resources in the network.
      But it is not only finding the best resources but qualifying those
      resources in terms of reliability especially for mission critical
      services in XR (medicine for example).  This could include
      intelligence services.

3.2.5.  Research Questions

   *  RQ 3.2.1: Can current programmable network entities be sufficient
      to provide the speed required to provide and execute complex
      filtering operations that includes metadata analysis for complex
      and dynamic scene rendering?

   *  RQ 3.2.2: How can the interoperability of CPU/GPU be optimized to
      combine low level packet filtering with the higher layer
      processors needed for image processing and haptics?

   *  RQ 3.2.3: Can the use of joint learning algorithms across both
      data center and edge computers be used to create optimal
      functionality allocation and the creation of semi-permanent
      datasets and analytics for usage trending resulting in better
      localization of XR functions?

   *  RQ 3.2.4: Can COIN improve the dynamic distribution of control,
      forwarding and storage resources and related usage models in XR?

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3.2.6.  Requirements

   *  Req 3.2.1: Allow joint collaboration.

   *  Req 3.2.2: Provide multi-views.

   *  Req 3.2.3: Include extra streams dynamically for data intensive
      services, manufacturing and industrial processes.

   *  Req 3.2.4: Enable multistream, multidevice, multidestination

   *  Req 3.2.5: Use new Internet Architectures at the edge for improved
      performance and performance management.

   *  Req 3.2.6: Integrate with holography, 3D displays and image
      rendering processors.

   *  Req 3.2.7: All the use of multicast distribution and processing as
      well as peer to peer distribution in bandwidth and capacity
      constrained environments.

   *  Req 3.2.8: Evaluate the integration local and fog caching with
      cloud-based pre-rendering.

   *  Req 3.2.9: Evaluate ML-based congestion control to manage XR
      sessions quality of service and to determine how to priortize

   *  Req 3.2.10: Consider higher layer protocols optimization to reduce
      latency especially in data intensive applications at the edge.

   *  Req 3.2.11: Provide trust, including blockchains and smart-
      contracts to enable secure community building across domains.

   *  Req 3.2.12: Support nomadicity and mobility (link to mobile edge).

   *  Req 3.2.13: Use 5G slicing to create independent session-driven

   *  Req 3.2.14: Provide performance optimization by data reduction,
      tunneling, session virtualization and loss protection.

   *  Req 3.2.15: Use AI/ML for trend analysis and data reduction when

3.3.  Personalised 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 theatre; and ii) to
   enhance traditional physical performances with features such as
   personalisation of the experience according to the preferences or
   needs of the audience members.

   Examples of personalisation include:

   *  Viewpoint selection such as choosing a specific seat in the
      theatre 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 colour schemes for colour-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

   *  Personalisation 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: 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 in-network
         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:

   *  Personalisation of the performance according to audience
      preferences and requirements makes it unfeasible to be done in a
      centralised manner at the performer premises: the computational
      resources and network bandwidth would need to scale with the
      number of audience members' personalised 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
      personalisation or the viewers' network may not have the capacity
      to receive all of the constituent streams to undertake the
      personalisation 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 minimised, which reduces the
      opportunity to route streams via large-scale processing
      capabilities at centralised data-centres.

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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 personalisation functions on-path
      as (COIN) Programs in PNDs will avoid detour/stretch to central
      servers which increases latency as well as the consumption of
      bandwidth on more network resources (links and routers).  For
      example, in this use case the chain of (COIN) Programs and
      propagation over the interconnecting network segments for
      performance capture, aggregation, distribution, personalisation,
      consumption, capture of audience response, feedback processing,
      aggregation, 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 laugher or other emotional responses in a theatre

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

3.3.5.  Research Questions:

   *  RQ 3.3.1: In which PNDs should (COIN) Programs for aggregation,
      encoding and personalisation 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 network synchronisation across multiple
      streams to allow for merging, audio-video interpolation and other
      cross-stream processing functions that require time

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      synchronisation for the integrity of the output?  How can this be
      achieved considering that synchronisation may be required between
      flows that are: i) on the same data pathway through a PND/router,
      ii) arriving/leaving through different ingress/egress interfaces
      of the same PND/router, iii) routed through disjoint paths through
      different PNDs/routers?

   *  RQ 3.3.5: Where will COIN Programs will 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 - 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 in-network processing?  Where
      is the boundary between in-network processing capabilities and
      explicit routing of flows to endsystems?

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) and
      the COIN System should be able to respect those requirements and
      constraints when routing flows and selecting PNDs for executing
      (COIN) Programs.

   *  Req 3.3.2: A COIN System should be able to synchronise flow
      treatment and processing across multiple related flows which may
      be on disjoint paths.

4.  Supporting new COIN Systems

   While the best-effort nature of the Internet enables a wide variety
   of applications, there are several domains whose requirements are
   hard to satisfy over regular best-effort networks.

   Consequently, there is a large number of specialized appliances and
   protocols designed to provide the required strict performance
   guarantees, e.g., regarding real-time capabilities.

   Time-Sensitive-Networking [TSN] as an enhancement to the standard
   Ethernet, e.g., tries to achieve these requirements on the link layer
   by statically reserving shares of the bandwidth.  However, solutions
   on the link layer alone are not always sufficient.

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   The industrial domain, e.g., currently evolves towards increasingly
   interconnected systems in turn increasing the complexity of the
   underlying networks, making them more dynamic, and creating more
   diverse sets of requirements.  Concepts satisfying the dynamic
   performance requirements of modern industrial applications thus
   become harder to develop.  In this context, COIN offers new
   possibilities as it allows to flexibly distribute computation tasks
   across the network and enables novel forms of interaction between
   communication and computation providers.

   This document illustrates the potential for new COIN systems using
   the example of the industrial domain by characterizing and analyzing
   specific scenarios to showcase potential requirements, as specifying
   general requirements is difficult due to the domain's mentioned

4.1.  Industrial Network Scenario

   Common components of industrial networks can be divided into three
   categories as illustrated in Figure 2.  Following
   [I-D.mcbride-edge-data-discovery-overview], EDGE DEVICES, such as
   sensors and actuators, constitute the boundary between the physical
   and digital world.  They communicate the current state of the
   physical world to the digital world by transmitting sensor data or
   let the digital world interact with the physical world by executing
   actions after receiving (simple) control information.  The processing
   of the sensor data and the creation of the control information is
   done on COMPUTING DEVICES.  They range from small-powered controllers
   close to the EDGE DEVICES, to more powerful edge or remote clouds in
   larger distances.  The connection between the EDGE and COMPUTING
   DEVICES is established by NETWORKING DEVICES.  In the industrial
   domain, they range from standard devices, e.g., typical Ethernet
   switches, which can interconnect all Ethernet-capable hosts, to
   proprietary equipment with proprietary protocols only supporting
   hosts of specific vendors.

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    |Sensor| ------------|              ~~~~~~~~~~~~      ------------
    --------       -------------        { Internet } --- |Remote Cloud|
       .           |Access Point|---    ~~~~~~~~~~~~      ------------
    --------       -------------   |          |
    |Sensor| ----|        |        |          |
    --------     |        |       --------    |
       .         |        |       |Switch| ----------------------
       .         |        |       --------                       |
       .         |        |                   ------------       |
    ----------   |        |----------------- | Controller |      |
    |Actuator| ------------                   ------------       |
    ----------   |    --------                            ------------
       .         |----|Switch|---------------------------| Edge Cloud |
    ----------        --------                            ------------
    |Actuator|  ---------|

   |-----------|       |------------------|     |-------------------|

     Figure 2: Industrial networks show a high level of heterogeneity.

4.2.  In-Network Control / Time-sensitive applications

4.2.1.  Description

   The control of physical processes and components of a production line
   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 or few controlled components.

   Modern production lines and shop floors are characterized by an
   increasing amount 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

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4.2.2.  Characterization

   A control process consists of two main components as illustrated in
   Figure 3: 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 3: 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.

4.2.3.  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.

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4.2.4.  Opportunities

   COIN offers the possibility of bringing the system and the controller
   closer together, thus possibly satisfying the latency requirements,
   by performing simple control logic on PNDs and/or in COIN execution

   While control models, in general, can become involved, there is a
   variety of control algorithms that are composed of simple
   computations such as matrix multiplication.  These are supported by
   some PNDs and it is thus possible to compose simplified
   approximations of the more complex algorithms and deploy them in the
   network.  While the simplified versions induce a more inaccurate
   control, they allow for a quicker response and might be sufficient to
   operate a basic tight control loop while the overall control can
   still be exercised from the cloud.


   *  Execute simple (end-host) COIN functions on PNDs to satisfy tight
      latency constraints of control processes

4.2.5.  Research Questions

   Bringing the required computations to PNDs is challenging as these
   devices typically only allow for integer precision computation while
   floating-point precision is needed by most control algorithms.
   Additionally, computational capabilities vary for different available
   PNDs [KUNZE].  Yet, early approaches like [RUETH] and [VESTIN] have
   already shown the general applicability of such ideas, but there are
   still a lot of open research questions not limited to the following:

   Research Questions:

   *  RQ 4.2.1: How to derive simplified versions of the global
      (control) function?

      -  How to account for the limited computational precision of PNDs?

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

   *  RQ 4.2.2: 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?

4.2.6.  Requirements

   *  Req 4.2.1: The interaction between the COIN execution environments
      and the global controller SHOULD be explicit.

   *  Req 4.2.2: The interaction between the COIN execution environments
      and the global controller MUST NOT negatively impact the control

   *  Req 4.2.3: Actions of the COIN execution environments MUST be
      overridable by the global controller.

   *  Req 4.2.4: Functions in COIN execution environments SHOULD be
      executed with predictable delay.

   *  Req 4.2.5: Functions in COIN execution environments MUST be
      executed with predictable accuracy.

4.3.  Large Volume Applications - Filtering

4.3.1.  Description

   In modern industrial networks, processes and machines can be
   monitored closely resulting in large volumes of available
   information.  This data can be used to find previously unknown
   correlations between different parts of the value chain, e.g., by
   deploying machine learning (ML) techniques, which in turn helps to
   improve the overall production system.  Newly gained knowledge can be
   shared between different sites of the same company or even between
   different companies [PENNEKAMP].

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   Traditional company infrastructure is neither equipped for the
   management and storage of such large amounts of data nor for the
   computationally expensive training of ML approaches.  Off-premise
   cloud platforms offer cost-effective solutions with a high degree of
   flexibility and scalability, however, moving all data to off-premise
   locations poses infrastructural challenges.  Pre-processing or
   filtering the data already in COIN execution environments can be a
   new solution to this challenge.

4.3.2.  Characterization  General Characterization of Large Volume Applications

   Processes in the industrial domain are monitored by distributed
   sensors which range from simple binary (e.g., light barriers) to
   sophisticated sensors measuring the system with varying degrees of
   resolution.  Sensors can further serve different purposes, as some
   might be used for time-critical process control while others are only
   used as redundant fallback platforms.  Overall, there is a high level
   of heterogeneity which makes managing the sensor output a challenging

   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].  Using off-premise clouds for managing
   the data requires uploading or streaming the growing volume of sensor
   data using the companies' Internet access which is typically limited
   to a few hundred of Mbit/s.  While large networking companies can
   simply upgrade their infrastructure, most industrial companies rely
   on traditional ISPs for their Internet access.  Higher access speeds
   are hence tied to higher costs and, above all, subject to the supply
   of the ISPs and consequently not always available.  A major challenge
   is thus to devise a methodology that is able to handle such amounts
   of data over limited access links.

   Another aspect is that business data leaving the premise and control
   of the company further comes with security concerns, as sensitive
   information or valuable business secrets might be contained in it.
   Typical security measures such as encrypting the data make COIN
   techniques hardly applicable as they typically work on unencrypted
   data.  Adding security to COIN approaches, either by adding
   functionality for handling encrypted data or devising general
   security measures, is thus an auspicious field for research which we
   describe in more detail in Section 8.

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   Sensors are often set up redundantly, i.e., part of the collected
   data might also be redundant.  Moreover, they 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.

4.3.3.  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

4.3.4.  Opportunities

   PNDs and COIN execution environments are in a unique position to
   reduce the data rates due to their line-rate packet processing
   capabilities.  Using these capabilities, it is possible to filter out
   redundant or undesired data before it leaves the premise using simple
   traffic filters that are deployed in the on-premise network.  There
   are different approaches to how this topic can be tackled.

   A first step could be to scale down the available sensor data to the
   data rate that is needed.  For example, if a sensor transmits with a
   frequency of 5 kHz, but the control entity only needs 1 kHz, only
   every fifth packet containing sensor data is let through.
   Alternatively, sensor data could be filtered down to a lower
   frequency while the sensor value is in an uninteresting range, but
   let through with higher resolution once the sensor value range
   becomes interesting.

   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.


   *  (Semantic) packet filtering based on packet header and payload, as
      well as multi-packet information

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4.3.5.  Research Questions

   *  RQ 4.3.1: How to design COIN programs for (semantic) packet

      -  Which criteria for filtering make sense?

   *  RQ 4.3.2: How to distribute and coordinate COIN programs?

   *  RQ 4.3.3: How to dynamically change COIN programs?

   *  RQ 4.3.4: How to signal traffic filtering by COIN programs to end-

4.3.6.  Requirements

   *  Req 4.3.1: Filters MUST conform to application-level syntax and

   *  Req 4.3.2: Filters MAY leverage packet header and payload

   *  Req 4.3.3: Filters SHOULD be reconfigurable at run-time.

4.4.  Large Volume Applications - (Pre-)Preprocessing

4.4.1.  Description

   See Section 4.3.1.

4.4.2.  Characterization  General Characterization of Large Volume Applications

   See Section  Specific Characterization for Preprocessing Solutions

   There are manifold computations that can be performed on the sensor
   data in the cloud.  Some of them are very complex or need the
   complete sensor data during the computation, but there are also
   simpler operations which can be done on subsets of the overall
   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.

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4.4.3.  Existing Solutions

   See Section 4.3.3.

4.4.4.  Opportunities

   Using expert knowledge about the exact computation steps and the
   concrete transmission path of the sensor data, simple computation
   steps can be deployed in the on-premise network to reduce the overall
   data volume and potentially speed up the processing time in the

   Related work has already shown that in-network aggregation can help
   to improve the performance of distributed ML applications [SAPIO].
   Investigating the applicability of stream data processing techniques
   to PNDs is also interesting, because sensor data is usually streamed.


   *  (Semantic) data (pre-)processing, e.g., in the form of
      computations across multiple packets and potentially leveraging
      packet payload

4.4.5.  Research Questions

   *  RQ 4.4.1: Which kinds of COIN programs can be leveraged for
      (pre-)processing steps?

      -  How complex can they become?

   *  RQ 4.4.2: How to distribute and coordinate COIN programs?

   *  RQ 4.4.3: How to dynamically change COIN programs?

   *  RQ 4.4.4: How to incorporate the (pre-)processing steps into the
      overall system?

4.4.6.  Requirements

   *  Req 4.4.1: Preprocessors MUST conform to application-level syntax
      and semantics.

   *  Req 4.4.2: Preprocessors MAY leverage packet header and payload

   *  Req 4.4.3: Preprocessors SHOULD be reconfigurable at run-time.

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4.5.  Industrial Safety

4.5.1.  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 decent 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.

4.5.2.  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.

   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.

4.5.3.  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.

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4.5.4.  Opportunities

   COIN systems could leverage the increased availability of sensor data
   and the detailed monitoring of the factories to enable additional
   safety measures.  Different safety indicators within the production
   hall can be combined within the network so that PNDs can give early
   responses if a potential safety breach is detected.

   One possibility could be to track the positions of human workers and
   robots.  Whenever a robot gets too close to a human in a non-working
   area or if a human enters a defined safety zone, robots are stopped
   to prevent injuries.  More advanced concepts could also include image
   data or combine arbitrary sensor data.


   *  Execute simple (end-host) COIN functions on PNDs to create early
      emergency reactions based on diverse sensor feedback

4.5.5.  Research Questions

   *  RQ 4.5.1: Which additional safety measures can be provided?

      -  Do these measures actually improve safety?

   *  RQ 4.5.2: Which sensor information can be combined and how?

4.5.6.  Requirements

   *  Req 4.5.1: COIN-based safety measures MUST NOT degrade existing
      safety measures.

   *  Req 4.5.2: COIN-based safety measures MAY enhance existing safety

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) storing said content closer to end users for latency
   reduced delivery with DNS-based indirection being utilized to serve
   the request on behalf of the origin server.

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5.1.2.  Characterization

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

5.1.3.  Existing Solutions

   NOTE: material on solutions will be added here later

   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 reduces required
   edge storage capacity by benefiting from the increased network
   efficiency to renew edge content more quickly against changing

5.1.4.  Opportunities

   *  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 situation 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 those request question for Section 3.1:

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

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   *  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 at 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

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

5.2.1.  Description

   Layer 2 connected compute resources, e.g., in regional or edge data
   centres, base stations and even end-user devices, provide the
   opportunity for infrastructure providers to offer CFaaS type of
   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 inter-connection 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

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   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 interconnect
   all resources suitably into the (tenant-specific) fabric exposed as

5.2.3.  Existing Solutions

   NOTE: material on solutions will be added here later

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

   Similar to those for Section 3.1.  In addition:

   *  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?

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   *  RQ 5.2.3: How to utilize in-network 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

   For the provisioning of services atop the CFaaS, requirements 3.1.1
   through 3.1.6 should be addressed, too.  In addition:

   *  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 inter-
      connection of compute resources, including those dynamically added
      and removed during the provisioning of the tenant-specific compute

   *  Req 5.2.4: Any solution MUST provide means for ensuring
      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 for example 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 needs
      enhanced security

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   *  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 use a default slice

   *  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 4, the tenant operates a network
   including a 5GLAN network segment (seen as a single logical switch),
   as well as fixed segments.  This can be in a plant or enterprise
   network, using for an example a 5G Non-Public Network (NPN).  The
   tenant uses P4 programs to determine the operation of the fixed and
   5GLAN switches.  The tenant provisions a 5GLAN P4 program into the
   mobile network, and can also operate a controller.  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).

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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 4: 5G Virtual Network Programming Overview

   Looking in more details in Figure 5, the 5GLAN P4 program can be
   split between multiple data plane nodes (PDU Session Anchor (PSA)
   User Plane Functions (UPF), other UPFs, or even mobile devices),
   although in some cases the P4 program may be hosted on a single node.
   In the most general case, a distributed deployment is useful to keep
   traffic on optimal paths, because, except in simple cases, within a
   5GLAN all traffic will not pass through a single node.  In this
   example, P4 programs could be deployed in UPF1, UPF2, UPF3, UE3 and
   UE4.  UE1-UE2 traffic is using a local switch on PSA UPF1, UE1-UE3
   traffic is tunneled between PSA UPF1 and PSA UPF2 through the N19
   interface, and UE1-UE4 traffic is forwarded throughan external Data
   Network (DN).  Traffic between Device1 and Device2 is forwarded
   through UE4.

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                            +-----+          +-----+      +------------+
                            | AMF |          | SMF |      | Controller |
                            +-+-+-+          +--+--+      +-----+------+
                             /  |               |             P4|
                  +---------+   |             N4|        Runtime|
             N1  /              |N2             |               V
         +------+               |               |     (all P4 programs*)
        /                       |               |
     +--+--+  air interface +---+-----+ N3 +-+--+----------+  N6  +----+
     | UE1 +----------------+  (R)AN  +----+   PSA UPF1*   +----->+    |
     +-----+                +---------+    +-+-------+-----+      |    |
        |                       |            |  |    |            |    |
     +--+--+                +---+-----+      |  |    |            |    |
     | UE2 +----------------+  (R)AN  +------'  |    | N19        | DN |
     +-----+                +---------+         |    |            |    |
        |                       |               |    |            |    |
     +--+--+                +---+-----+    +----+----+-----+      |    |
     | UE3*+----------------+  (R)AN  +----+    PSA UPF2*  +      |    |
     +-----+                +---------+    +---------+-----+      |    |
        |                       |               |    | N19        |    |
     +--+--+                +---+-----+    +----+----+-----+  N6  |    |
   +-+ UE4*+----------------+  (R)AN  +----+    PSA UPF3*  +----->+    |
   | +-----+                +---------+    +---------------+      +----+
   | Fixed or wireless connection
   |  +---------+
   +--+ Device1 |           (* indicates the presence of a P4 program)
   |  +---------+
   |  +---------+    +------------+
   `--+ Device2 +----+ P4 Switch* +--->(fixed network)
      +---------+    +------------+

              Figure 5: 5G Virtual Network Programming Details

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 developped for deploying
   virtual COIN programs over mobile or datacenter networks.

5.3.4.  Opportunities

   Virtual network programming by tenants could bring benefits such as:

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   *  A unified programming model, which can facilitate porting in-
      network computing between data centers, 5G networks, and other
      fixed and wireless networks, as well as sharing controller, code
      and expertise.

   *  Increasing the level of customization available to customers/
      tenants of mobile networks or datacenters, when compared with
      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 service
      (e.g., request specific QoS for some flows in 5G or datacenters),
      to increases the level of in-depth customization available to

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
      (e.g., the 5G network or data center).  For example, a virtual
      COIN program may be aware of the slice used by a flow, and
      possibly influence slice selection.  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.  As
      a primary reason for this, program logic should be applied exactly
      once or at least once per packet, while allowing optimal
      forwarding path by the underlying network.  For example, a 5GLAN
      P4 program may need to run on multiple UPFs.  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 mechanism were
      proposed for P4 multi-tenancy in a switch [Stoyanov], research
      questions remains, about isolation between tenants, fair
      repartition of resources.

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   *  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, 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

   *  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).

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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 constitute a distributed AI
   problem to a certain extent albeit with little flexibility in
   distributing the execution of the AI logic.

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.

   NOTE: material on solutions like ETSI MEC and 3GPP work will be added
   here later

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.

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6.1.5.  Research Questions

   *  RQ 6.1.1: similar to use case in Section 3.1

   *  RQ 6.1.2: What are the communication patterns that may be
      supported by collective communication solutions?

   *  RQ 6.1.3: How to achieve scalable multicast delivery with rapidly
      changing receiver sets?

   *  RQ 6.1.4: What in-network capabilities may support the collective
      communication patterns found in distributed AI problems?

   *  RQ 6.1.5: 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.  Analysis

   The goal of this analysis is to identify aspects that are relevant
   across all use cases to help in shaping the research agenda of
   COINRG.  For this purpose, this section will condense the
   opportunities, research questions, as well as requirements of the
   different presented use cases and analyze these for similarities
   across the use cases.

   Through this, we intend to identify cross-cutting opportunities,
   research questions as well as requirements (for COIN system
   solutions) that may aid the future work of COINRG as well as the
   larger research community.

7.1.  Opportunities

   To be added later.

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7.2.  Research Questions

   After carefully considering the different use cases along with their
   research questions, we propose the following layered categorization
   to structure the content of the research questions which we
   illustrate in Figure 6.

      +                       Applicability Areas                    +
      + .............................................................+
      + Transport |   App  |    Data    |  Routing &  | (Industrial) +
      +           | Design | Processing | Forwarding  |    Control   +

      +    Distributed Computing FRAMEWORKS and LANGUAGES to COIN    +

      +                ENABLING TECHNOLOGIES for COIN                +

      +                      VISION(S) for COIN                      +

                Figure 6: Research Questions Categorization

7.2.1.  Categorization

   Three categories deal with concretizing fundamental building blocks
   of COIN and COIN itself.

   *  VISION(S) for COIN: Questions that aim at defining and shaping the
      exact scope of COIN.

   *  ENABLING TECHNOLOGIES for COIN: Questions that target the
      capabilities of the technologies and devices intended to be used
      in COIN.

   *  Distributed Computing FRAMEWORKS and LANGUAGES to COIN: Questions
      that aim at concretizing how a framework or languages for
      deploying and operating COIN systems might look like.

   Additionally, there are use-case near research questions that are
   heavily influenced by the specific constraints and goals of the use
   cases.  We call this category "applicability areas" and refine it
   into the following subgroups:

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   *  Transport:

   *  App Design:

   *  Data Processing:

   *  Routing & Forwarding:

   *  (Industrial) Control

7.2.2.  Analysis  VISION(S) for COIN

   The following research questions presented in the use cases belong to
   this category:

   3.1.8, 3.2.1, 3.3.5, 3.3.6, 3.3.7, 5.3.3, 6.1.2, 6.1.4

   The research questions centering around the COIN VISION dig into what
   is considered COIN and what scope COIN functionality should have.  In
   contrast to the ENABLING TECHNOLOGIES, this section looks at the
   problem from a more philosophical perspective.  Where to perform computations

   The first aspect of this is where/on which devices COIN programs
   will/should be executed (3.3.5).  In particular, it is debatable
   whether COIN programs will/should only be executed in PNDs or whether
   other "adjacent" computational nodes are also in scope.  In case of
   the latter, an arising question is whether such computations are
   still to be considered as "in-network processing" and where the exact
   line is between "in-network processing" and "routing to end systems"
   (3.3.7).  In this context, it is also interesting to reason about the
   desired feature sets of PNDs (and other COIN execution environments)
   as these will shift the line between "in-network processing" and
   "routing to end systems" (3.1.8).  Are tasks suitable for COIN

   Digging deeper into the desired feature sets, some research questions
   address the question of which domains are to be considered of
   interest/relevant to COIN.  For example, whether computationally-
   intensive tasks are suitable candidates for (COIN) Programs (3.3.6).

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Internet-Draft               COIN Use Cases                   March 2022  (Is COIN)/(What parts of COIN are) suitable for the tasks

   Turning the previous aspect around, some questions try to reason
   whether COIN can be sensibly used for specific tasks.  For example,
   it is a question of whether current PNDs are fast and expressive
   enough for complex filtering operations (3.2.1).

   There are also more general notions of this question, e.g., what "in-
   network capabilities" might be used to address certain problem
   patterns (6.1.4) and what new patterns might be supported (6.1.2).
   What is interesting about these different questions is that the
   former raises the question of whether COIN can be used for specific
   tasks while the latter asks which tasks in a larger domain COIN might
   be suitable for.  What are desired forms for deploying COIN functionality

   The final topic addressed in this part deals with the deployment
   vision for COIN programs (5.3.3).

   In general, multiple programs can be deployed on a single PND/COIN
   element.  However, to date, multi-tenancy concepts are, above all,
   available for "end-host-based" platforms, and, as such, there are
   manifold questions centering around (1) whether multi-tenancy is
   desirable for PNDs/COIN elements and (2) how exactly such
   functionality should be shaped out, e.g., which (new forms of)
   hardware support needs to be provided by PNDs/COIN elements.  ENABLING TECHNOLOGIES for COIN

   The following research questions presented in the use cases belong to
   this category:

   3.1.7, 3.1.8, 3.2.2, 4.3.4, 4.4.4, 5.1.1, 5.1.2, 5.1.6, 5.3.1, 6.1.3,

   The research questions centering around the ENABLING TECHNOLOGIES for
   COIN dig into what technologies are needed to enable COIN, which of
   the existing technologies can be reused for COIN and what might be
   needed to make the VISION(S) for COIN a reality.  In contrast to the
   VISION(S), this section looks at the problem from a practical

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Internet-Draft               COIN Use Cases                   March 2022  COIN compute technologies

   Picking up on the topics discussed in Section and
   Section, this category deals with how such technologies
   might be realized in PNDs and with which functionality should even be
   realized (3.1.8).  Forwarding technology

   Another group of research questions focuses on "traditional"
   networking tasks, i.e., L2/L3 switching and routing decisions.

   For example, how COIN-powered routing decisions can be provided at
   line-rate (3.1.7).  Similarly, how (L2) multicast can be used for
   COIN (vice versa) (5.1.1), which (new) forwarding capabilities might
   be required within PNDs to support the concepts (5.1.2), and how
   scalability limits of existing multicast capabilities might be
   overcome using COIN (5.1.6).

   In this context, it is also interesting how these technologies can be
   used to address quickly changing receiver sets (6.1.3), especially in
   the context of collective communication (6.1.4).  Incorporating COIN in existing systems

   Some research questions deal with questions around how COIN
   (functionality) can be included in existing systems.

   For example, if COIN is used to perform traffic filtering, how end-
   hosts can be made aware that data/information/traffic is deliberately
   withheld (4.3.4).  Similarly, if data is pre-processed by COIN, how
   can end-hosts be signaled the new semantics of the received data

   In particular, these are not only questions concerning the
   functionality scope of PNDs or protocols but might also depend on how
   programming frameworks for COIN are designed.  Overall, this category
   deals with how to handle knowledge and action imbalances between
   different nodes within COIN networks (5.3.1).  Enhancing device interoperability

   Finally, the increasing diversity of devices within COIN raises
   interesting questions of how the capabilities of the different
   devices can be combined and optimized (3.2.2).

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Internet-Draft               COIN Use Cases                   March 2022  Distributed Computing FRAMEWORKS and LANGUAGES to COIN

   The following research questions presented in the use cases belong to
   this category:

   3.1.1, 3.2.3, 3.3.1, 3.3.2, 3.3.3, 3.3.5, 4.2.1, 4.2.2, 4.3.2/4.4.2,
   4.3.3/4.4.3, 4.3.4, 4.4.4, 5.2.1, 5.2.2, 5.2.3, 5.3.1, 5.3.2, 5.3.3,
   5.3.4, 5.3.5,

   This category mostly deals with how COIN programs can be deployed and
   orchestrated.  COIN program composition

   One aspect of this topic is how the exact functional scope of COIN
   programs can/should be defined.  For example, it might be an idea to
   define an "overall" program that then needs to be deployed to several
   devices (5.3.2).  In that case, how should this composition be done:
   manually or automatically?  Further aspects to consider here are how
   the different computational capabilities of the available devices can
   be taken into account and how these can be leveraged to obtain
   suitable distributed versions of the overall program (4.2.1).

   In particular, it is an open question of how "service-level"
   frameworks can be combined with "app-level" packaging methods (3.1.1)
   or whether virtual network models can help facilitate the composition
   of COIN programs (5.3.5).  This topic also again includes the
   considerations regarding multi-tenancy support (5.3.3, cf.
   Section as such function distribution might necessitate
   deploying functions of several entities on a single device.  COIN function placement

   In this context, another interesting aspect is where exactly
   functions should be placed and who should influence these decisions.
   Such function placement could, e.g., be guided by the available
   devices (3.3.5, c.f.  Section and their position with
   regards to the communicating entities (3.3.1), and it could also be
   specified in terms of the "distance" from the "direct" network path

   However, it might also be an option to leave the decision to users or
   at least provide means to express requirements/constraints (3.3.3).
   Here, the main question is how tenant-specific requirements can
   actually be conveyed (5.2.1).

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Internet-Draft               COIN Use Cases                   March 2022  COIN function deployment

   Once the position for deployment is fixed, a next problem that arises
   is how the functions can actually be deployed (4.3.2,4.4.2).  Here,
   first relevant questions are how COIN programs/program instances can
   be identified (3.1.4) and how preferences for specific COIN program
   instances can be noted (3.1.5).  It is then interesting to define how
   different COIN program can be coordinated (4.3.2,4.4.2), especially
   if there are program dependencies (4.2.2, cf. Section  COIN dynamic system operation

   In addition to static solutions to the described problems, the
   increasing dynamics of today's networks will also require dynamic
   solutions.  For example, it might be necessary to dynamically change
   COIN programs at run-time (4.3.3, 4.4.3) or to include new resources,
   especially if service-specific constraints or tenant requirements
   change (5.2.2).  It will be interesting to see if COIN frameworks can
   actually support the sometimes required dynamic changes (3.2.4).  In
   this context, providing availability and accountability of resources
   can also be an important aspect.  COIN system integration

   COIN systems will potentially not only exist in isolation, but will
   have to interact with existing systems.  Thus, there are also several
   questions addressing the integration of COIN systems into existing
   ones.  As already described in Section, the semantics of
   changes made by COIN programs, e.g., filtering packets or changing
   payload, will have to be communicated to end-hosts (4.3.4,4.4.4).
   Overall, there has to be a common middleground so that COIN systems
   can provide new functionality while not breaking "legacy" systems.
   How to bridge different levels of "network awareness" (5.3.1) in an
   explicit and general manner might be a crucial aspect to investigate.  COIN system properties - optimality, security and more

   A final category deals with meta objectives that should be tackled
   while thinking about how to realize the new concepts.  In particular,
   devising strategies for achieving an optimal function allocation/
   placement are important to effectively the high heterogeneity of the
   involved devices (3.2.3).

   On another note, security in all its facets needs to be considered as
   well, e.g., how to protect against misuse of the systems,
   unauthorized traffic and more (5.3.4).  We acknowledge that these
   issues are not yet discussed in detail in this document.

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Internet-Draft               COIN Use Cases                   March 2022  Applicability Area - Transport

   The following research questions presented in the use cases belong to
   this category:


   Further research questions concerning transport solutions are
   discussed in more detail in [TRANSPORT].

   Today's transport protocols are generally intended for end-to-end
   communications.  Thus, one important question is how COIN program
   interactions should be handled, especially if the deployment
   locations of the program instances change (quickly) (3.1.2).  Applicability Area - App Design

   The following research questions presented in the use cases belong to
   this category:

   4.3.1, 5.1.1, 5.1.3, 5.1.5

   The possibility of incorporating COIN resources into application
   programs increases the scope for how applications can be designed and
   implemented.  In this context, the general question of how the
   applications can be designed and which (low-level) triggers could be
   included in the program logic comes up (4.3.1).  Similarly, providing
   sensible constraints to route between compute and network
   capabilities (when both kinds of capabilities are included) is also
   important (5.1.3).  Many of these considerations boil down to a
   question of trade-off, e.g, between storage and frequent updates
   (5.1.5), and how (new) COIN capabilities can be sensibly used for
   novel application design (5.1.1).  Applicability Area - Data Processing

   The following research questions presented in the use cases belong to
   this category:

   3.2.3, 4.4.1, 4.5.2

   Many of the use cases deal with novel ways of processing data using
   COIN.  Interesting questions in this context are which types of COIN
   programs can be used to (pre-)process data (4.4.1) and which parts of
   packet information can be used for these processing steps, e.g.,
   payload vs. header information (4.5.2).  Additionally, data
   processing within COIN might even be used to support a better
   localization of the COIN functionality (3.2.3).

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Internet-Draft               COIN Use Cases                   March 2022  Applicability Area - Routing & Forwarding

   The following research questions presented in the use cases belong to
   this category:

   3.1.2, 3.1.3, 3.1.4, 3.1.5, 3.1.6, 5.1.2, 5.1.3, 5.1.4, 6.1.5,

   Being a central functionality of traditional networking devices,
   routing and forwarding are also prime candidates to profit from
   enhanced COIN capabilities.  In this context, a central question,
   also raised as part of the framework in Section, is how
   different COIN entities can be identified (3.1.4) and how the choice
   for a specific instance can be signalled (3.1.5).  Building upon
   this, next questions are which constraints could be used to make the
   forwarding/routing decisions (5.1.3), how these constraints can be
   signalled in a scalable manner (3.1.3), and how quickly changing COIN
   program locations can be included in these concepts, too (3.1.2).

   Once specific instances are chosen, higher-level questions revolve
   around "affinity".  In particular, how affinity on service-level can
   be provided (3.1.6), whether traffic steering should actually be
   performed on this level of granularity or rather on a lower level
   (5.1.4) and how invocation for arbitrary application-level protocols,
   e.g., beyond HTTP, can be supported (6.1.5).  Overall, a question is
   what specific forwarding methods should or can be supported using
   COIN (5.1.2).  Applicability Area - (Industrial) Control

   The following research questions presented in the use cases belong to
   this category:

   3.2.4, 3.3.1, 3.3.4, 4.2.1, 4.4.1, 4.5.1

   The final applicability area deals with use cases exercising some
   kind of control functionality.  These processes, above all, require
   low latencies and might thus especially profit from COIN
   functionality.  Consequently, the aforementioned question of function
   placement (cf.  Section, e.g., close to one of the end-
   points or deep in the network, is also a very relevant question for
   this category of applications (3.3.1).

   Focusing more explicitly on control processes, one idea is to deploy
   different controllers with different control granularities within a
   COIN system.  On the one hand, it is an interesting question how
   these controllers with different granularities can be derived based
   on one original controller (4.2.1).  On the other hand, how to
   achieve synchronisation between these controllers or, more generally,

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   between different entities or flows/streams within the COIN system is
   also a relevant problem (3.3.4).  Finally, it is still to be found
   out whether using COIN for such control processes indeed improves the
   existing systems, e.g., in terms of safety (4.5.1) or in terms of
   performance (3.2.4).

7.3.  Requirements

   To be added later.

8.  Security Considerations

   Note: This section will need consolidation once new use cases are
   added to the draft.  Current in-network computing approaches
   typically work on unencrypted plain text data because today's
   networking devices usually do not have crypto capabilities.

   As is already mentioned in Section 4.3.2, this above all poses
   problems when business data, potentially containing business secrets,
   is streamed into remote computing facilities and consequently leaves
   the control of the company.  Insecure on-premise communication within
   the company and on the shop-floor is also a problem as machines could
   be intruded from the outside.

   It is thus crucial to deploy security and authentication
   functionality on on-premise and outgoing communication although this
   might interfere with in-network computing approaches.  Ways to
   implement and combine security measures with in-network computing are
   described in more detail in [I-D.fink-coin-sec-priv].

9.  IANA Considerations


10.  Conclusion

   This draft presented use cases gathererd 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 (i) enable new
   experiences, (ii) expose new features or (iii) improve on existing
   system capabilities, and (iv) other use cases where COIN capabilities
   enable totally new applications, for example, in industrial

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   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 to reap
   those opportunities.  We also outlined possible requirements for
   realizing a COIN system addressing these use cases.

   But of course this is only a snapshot of the potential COIN use
   cases.  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 (amonsts others).  As these become better defined
   they will be added to the list presented here.  We are, however,
   confident that our analysis across all use cases in those dimensions
   of opportunities, research questions, and requirements has identified
   commonalities that will support future work in this space.  Hence,
   the use cases presented are directly positioned as input into the
   milestones of the COIN RG in terms of required functionalities.

11.  List of Use Case Contributors

   *  Dirk Trossen has contributed the following use cases: Section 3.1,
      Section 5.1, Section 5.2, Section 6.1.

   *  Marie-Jose Montpetit has contributed the XR use case
      (Section 3.2).

   *  David Griffin and Miguel Rio have contributed the use case on
      performing arts (Section 3.3).

   *  Ike Kunze and Klaus Wehrle have contributed the industrial use
      cases (Section 4).

   *  Xavier De Foy has contributed the use case on virtual networks
      programming (Section 5.3)

12.  References

12.1.  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,

12.2.  Informative References

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              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, <

   [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",

              Kutscher, D., Kaerkkaeinen, T., and J. Ott, "Directions
              for Computing in the Network", Work in Progress, Internet-
              Draft, draft-kutscher-coinrg-dir-02, 31 July 2020,

              Fink, I. B. and K. Wehrle, "Enhancing Security and Privacy
              with In-Network Computing", Work in Progress, Internet-
              Draft, draft-fink-coin-sec-priv-03, 22 October 2021,

              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, <

              McBride, M., Kutscher, D., Schooler, E., Bernardos, C. J.,
              Lopez, D. R., and X. D. Foy, "Edge Data Discovery for
              COIN", Work in Progress, Internet-Draft, draft-mcbride-

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              edge-data-discovery-overview-05, 1 November 2020,

              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,

   [ICE]      Burke, J., "ICN-Enabled Secure Edge Networking with
              Augmented Reality: ICE-AR.", ICE-AR Presentation at
              NDNCOM. , 2018, <

   [KUNZE]    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,

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

              Pennekamp, J., Henze, M., Schmidt, S., Niemietz, P., Fey,
              M., Trauth, D., Bergs, T., Brecher, C., and K. Wehrle,
              "Dataflow Challenges in an Internet of Production: A
              Security & Privacy Perspective", Proceedings of the ACM
              Workshop on Cyber-Physical Systems Security & Privacy -
              CPS-SPC'19, DOI 10.1145/3338499.3357357, 2019,

   [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, <>.

   [SAPIO]    Sapio, A., "Scaling Distributed Machine Learning with In-
              Network Aggregation", 2019,

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   [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., and D. Trossen, "Transport Protocol
              Issues of In-Network Computing Systems", Work in Progress,
              Internet-Draft, draft-kunze-coinrg-transport-issues-05, 25
              October 2021, <

   [TS23.501] 501, 3gpp-23., "Technical Specification Group Services and
              System Aspects; System Architecture for the 5G System;
              Stage 2 (Rel.17)", 3GPP , 2021,

   [TS23.502] 502, 3gpp-23., "Technical Specification Group Services and
              System Aspects; Procedures for the 5G System; Stage 2
              (Rel.17)", 3GPP , 2021,

   [TSN]      "IEEE Time-Sensitive Networking (TSN) Task Group",

   [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

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   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

   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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