rtgwg                                                             P. Liu
Internet-Draft                                              China Mobile
Intended status: Informational                                 P. Willis
Expires: 22 July 2022                                                 BT
                                                              D. Trossen
                                                                   C. Li
                                                     Huawei Technologies
                                                         18 January 2022


        Dynamic-Anycast (Dyncast) Use Cases & Problem Statement
                    draft-liu-dyncast-ps-usecases-02

Abstract

   Many service providers are exploring distributed computing techniques
   to achieve better service response time and optimized energy
   consumption. by moving Such techniques rely upon the distribution of
   computing services and capabilities over many locations in the
   network such as its edge(e.g., 5G MEC (Multi-access Edge Computing)
   scenarios), the metro region, virtualized central office, and other
   locations.  In such a distributed computing environment, providing
   services by soliciting computing resources hosted in various
   computing facilities (e.g., edges) is being considered, e.g., for
   computationally intensive and delay sensitive services.  Ideally,
   Services should be computationally balanced using service-specific
   metrics instead of simply dispatching the service requests in a
   static way or optimizing solely connectivity metrics.  For example,
   systematically directing end user-originated service requests to the
   geographically closest edge or some small computing units may lead to
   an unbalanced usage of computing resources, which may then degrade
   both the user experience and the overall service performance.

   This draft provides an overview of scenarios and problems associated
   with realizing such scenarios, identifying several key areas which
   require more investigations in terms of architecture and protocol to
   achieve balanced computing and networking resource utilization among
   edges providing the services.

Status of This Memo

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

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



Liu, et al.               Expires 22 July 2022                  [Page 1]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


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

   This Internet-Draft will expire on 22 July 2022.

Copyright Notice

   Copyright (c) 2022 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components
   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Definition of Terms . . . . . . . . . . . . . . . . . . . . .   4
   3.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     3.1.  Cloud Virtual Reality (VR) or Augmented Reality (AR)  . .   5
     3.2.  Connected Car . . . . . . . . . . . . . . . . . . . . . .   6
     3.3.  Digital Twin  . . . . . . . . . . . . . . . . . . . . . .   7
   4.  Problems in Existing Solutions  . . . . . . . . . . . . . . .   8
     4.1.  Dynamicity of Relations . . . . . . . . . . . . . . . . .   8
     4.2.  Efficiency  . . . . . . . . . . . . . . . . . . . . . . .  10
     4.3.  Complexity and Accuracy . . . . . . . . . . . . . . . . .  10
     4.4.  Metric Exposure and Use . . . . . . . . . . . . . . . . .  11
     4.5.  Security  . . . . . . . . . . . . . . . . . . . . . . . .  11
     4.6.  Changes to Infrastructure . . . . . . . . . . . . . . . .  12
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  12
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   8.  Informative References  . . . . . . . . . . . . . . . . . . .  13
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14









Liu, et al.               Expires 22 July 2022                  [Page 2]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


1.  Introduction

   Edge computing aims to provide better response times and transfer
   rate, with respect to Cloud Computing, by moving the computing
   towards the edge of the network.  Edge computing can be built on
   industrial PCs, embedded systems, gateways and others, all being
   located close to the end user.  There is an emerging requirement that
   multiple edge sites (called "edges" too in this document) are
   deployed at different locations to provide the service.  There are
   millions of home gateways, thousands of base stations and hundreds of
   central offices in a city that can serve as candidate edges for
   hosting service nodes.  Depending on the location of the edge and its
   capacity, each edge has different computing resources to be used for
   a service.  At peak hour, computing resources attached to a client's
   closest edge site may not be sufficient to handle all the incoming
   service requests.  Longer response times or even dropping of requests
   can be experienced by users.  Increasing the computing resources
   hosted on each edge site to the potential maximum capacity is neither
   feasible nor economical in many cases.

   Some user devices are purely battery-driven.  Offloading computation
   intensive processing to the edge can save battery power.  Moreover
   the edge may use a data set (for the computation) that may not exist
   on the user device because of the size of data pool or due to data
   governance reasons.

   At the same time, with new technologies such as serverless computing
   and container based virtual functions, the service node at an edge
   can be easily created and terminated in a sub-second scale, which in
   turn changes the availability of a computing resources for a service
   dramatically over time, therefore impacting the possibly "best"
   decision on where to send a service request from a client.

   Traditional techniques to manage the overall load balancing process
   of clients issuing requests include choose- the-closest or round-
   robin.  Those solutions are relatively static, which may cause an
   unbalanced distribution in terms of network load and computational
   load.  For example, DNS-based load balancing usually configures a
   domain in Domain Name System (DNS) such that client requests to the
   domain are distributed across a group of servers.  It usually
   provides several IP addresses for a domain name.

   There are some dynamic ways which attempt to distribute the request
   to the server that best fits a service-specific metric, such as the
   best available resources and minimal load.  They usually require
   L4-L7 handling of the packet processing.  It is not an efficient
   approach for a large number of short connections.  At the same time,
   such approaches can often not retrieve the desired metric, such as



Liu, et al.               Expires 22 July 2022                  [Page 3]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   the network status, in real time.  Therefore, the choice of the
   service node is almost entirely determined by the computing status,
   rather than the comprehensive consideration of both computing and
   network metrics.

   Distributing a service request to a specific service having multiple
   instances attached to multiple edge computing sites, while taking
   into account computing as well as service-specific metrics in the
   distribution decision, can be seen as a dynamic anycast (or "dyncast"
   for short) problem of sending service requests, without prescribing
   the use of a routing solution at this stage of the discussion.

   As a problem statement, this draft describes usage scenarios as well
   as key areas in which current solutions lead to problems that
   ultimately affect the deployment or the performance of the edge
   services.  Those key areas target the identification of possible
   solution components, while the overall purpose of this document is to
   stimulate discussions on the emerging needs outlined in our use cases
   and to start the process of determining how they are best satisfied
   within the IETF protocol suite or through suitable extensions to that
   protocol suite.

2.  Definition of Terms

   Service:  A service represents a defined endpoint of functionality
     encoded according to the specification for said service.

   Service instance:  One service can have several instances running on
     different nodes.  Service instance is a running environment (e.g.,
     a node) that makes the functionality of a service available.

   Service identifier:  Used to uniquely identify a service, at the same
     time identifying the whole set of service instances that each
     represent the same service behaviour, no matter where those service
     instances are running.

   Anycast:  An addressing and packet sending methodology that assign an
     "anycast" identifier for one or more service instances to which
     requests to an "anycast" identifier could be routed, following the
     definition in [RFC4786] as anycast being "the practice of making a
     particular Service Address available in multiple, discrete,
     autonomous locations, such that datagrams sent are routed to one of
     several available locations".

   Dyncast:  Dynamic Anycast, taking the dynamic nature of computing
     resource metrics into account to steer an anycast-like decision in
     sending an incoming service request.




Liu, et al.               Expires 22 July 2022                  [Page 4]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


3.  Use Cases

   This section presents several typical scenarios which require
   multiple edge sites to interconnect and to co-ordinate at the network
   layer to meet the service requirements and ensure user experience.
   The scenarios here are exemplary only for the purpose of this
   document and not comprehensive.

3.1.  Cloud Virtual Reality (VR) or Augmented Reality (AR)

   Cloud VR/AR are now used in some exhibitions, scenic spots and
   celebration ceremonies.  In the future, they would be used in more
   applications such as industrial internet, medical industry and meta
   verse . They are the basic technologies to provide a better user
   experience.

   Cloud VR/AR introduces the concept and technology of cloud computing
   to the rendering of audiovisual assets in such applications.  Here,
   the edge cloud helps encode/decode and render content.  The end
   device usually only uploads posture or control information to the
   edge and then VR/AR contents are rendered in the edge cloud.  The
   video and audio outputs generated from the edge cloud are encoded,
   compressed, and transmitted back to the end device or further
   transmitted to central data center via high bandwidth networks.

   Edge sites may use CPU or GPU for encode/decode.  GPU usually has
   better performance but CPU is simpler and more straightforward to use
   as well as possibly more widespread in deployment.  Available
   remaining resources determines if a service instance can be started.
   The instance's CPU, GPU and memory utilization has a high impact on
   the processing delay on encoding, decoding and rendering.  At the
   same time, the network path quality to the edge site is a key for
   user experience of quality of audio/ video and input command response
   times.

   A Cloud VR service, such as a mobile gaming service, brings
   challenging requirements to both network and computing so that the
   edge node to serve a service request has to be carefully selected to
   make sure it has sufficient computing resource and good network path.
   For example, for an entry-level Cloud VR (panoramic 8K 2D video) with
   110-degree Field of View (FOV) transmission, the typical network
   requirements are bandwidth 40Mbps, 20ms for motion-to-photon latency,
   packet loss rate is 2.4E-5; the typical computing requirements are 8K
   H.265 real-time decoding, 2K H.264 real-time encoding.  We can
   further divide the 20ms latency budget into (i) sensor sampling
   delay, (ii) image/frame rendering delay, (iii) display refresh delay,
   and (iv) network delay.  With upcoming high display refresh rate
   (e.g., 144Hz) and GPU resources being used for frame rendering, we



Liu, et al.               Expires 22 July 2022                  [Page 5]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   can expect an upper bound of roughly 5ms for the round trip latency
   in these scenarios, , which is close to the frame rendering computing
   delay.

   Furthermore, techniques may be employed that divide the overall
   rendering into base assets that are common across a number of clients
   participating in the service, while the client-specific input data is
   being utilized to render additional assets.  When being delivered to
   the client, those two assets are being combined into the overall
   content being consumed by the client.  The requirements for sending
   the client input data as well as the requests for the base assets may
   be different in terms of which service instances may serve the
   request, where base assets may be served from any nearby service
   instance (since those base assets may be served without requiring
   cross-request state being maintained), while the client-specific
   input data is being processed by a stateful service instance that
   changes, if at all, only slowly over time due to the stickiness of
   the service that is being created by the client-specific data.  Other
   splits of rendering and input tasks can be found in [TR22.874] for
   further reading.

   When it comes to the service instances themselves, those may be
   instantiated on-demand, e.g., driven by network or client demand
   metrics, while resources may also be released, e.g., after an idle
   timeout, to free up resources for other services.  Depending on the
   utilized node technologies, the lifetime of such "function as a
   service" may range from many minutes down to millisecond scale.
   Therefore computing resources across participating edges exhibit a
   distributed (in terms of locations) as well as dynamic (in terms of
   resource availability) nature.  In order to achieve a satisfying
   service quality to end users, a service request will need to be sent
   to and served by an edge with sufficient computing resource and a
   good network path.

3.2.  Connected Car

   In auxiliary driving scenarios, to help overcome the non-line-of-
   sight problem due to blind spot or obstacles, the edge node can
   collect comprehensive road and traffic information around the vehicle
   location and perform data processing, and then vehicles with high
   security risk can be warned accordingly, improving driving safety in
   complicated road conditions, like at intersections.  This scenario is
   also called "Electronic Horizon", as explained in[HORITA].

   For instance, video image information captured by, e.g., an in-car,
   camera is transmitted to the nearest edge node for processing.  The
   notion of sending the request to the "nearest" edge node is important
   for being able to collate the video information of "nearby" cars,



Liu, et al.               Expires 22 July 2022                  [Page 6]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   using, for instance, relative location information.  Furthermore,
   data privacy may lead to the requirement to process the data as close
   to the source as possible to limit data spread across too many
   network components in the network.

   Nevertheless, load at specific "closest" nodes may greatly vary,
   leading to the possibility for the closest edge node becoming
   overloaded, leading to a higher response time and therefore a delay
   in responding to the auxiliary driving request with the possibility
   of traffic delays or even traffic accidents occurring as a result.
   Hence, in such cases, delay-insensitive services such as in-vehicle
   entertainment should be dispatched to other light loaded nodes
   instead of local edge nodes, so that the delay-sensitive service is
   preferentially processed locally to ensure the service availability
   and user experience.

3.3.  Digital Twin

   A number of industry associations, such as the Industrial Digital
   Twin Association or the Digital Twin Consortium
   (https://www.digitaltwinconsortium.org/), have been founded to
   advocate the concept of the Digital Twin (DT) for a number of use
   case areas, such as smart cities, transportation, industrial control,
   among others.  The core concept of the DT is the "administrative
   shell" [Industry4.0], which serves as a digital representation of the
   information and technical functionality pertaining to the "assets"
   (such as an industrial machinery, a transportation vehicle, an object
   in a smart city or others) that is intended to be managed,
   controlled, and actuated.

   As an example for industrial control, the programmable logic
   controller (PLC) may be virtualized and the functionality aggregated
   across a number of physical assets into a single administrative shell
   for the purpose of managing those assets.  PLCs may be virtualized in
   order to move the PLC capabilities from the physical assets to the
   edge cloud.  Several PLC instances may exist to enable load balancing
   and fail-over capabilities, while also enabling physical mobility of
   the asset and the connection to a suitable "nearby" PLC instance.
   With this, traffic dynamicity may be similar to that observed in the
   connected car scenario in the previous sub-section.  Crucial here is
   high availability and bounded latency since a failure of the
   (overall) PLC functionality may lead to a production line stop, while
   boundary violations of the latency may lead to loosing
   synchronization with other processes and, ultimately, to production
   faults, tool failures or similar.






Liu, et al.               Expires 22 July 2022                  [Page 7]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   Particular attention in Digital Twin scenarios is given to the
   problem of data storage.  Here, decentralization, not only driven by
   the scenario (such as outlined in the connected car scenario for
   cases of localized reasoning over data originating from driving
   vehicles) but also through proposed platform solutions, such as those
   in [GAIA-X], plays an important role.  With decentralization,
   endpoint relations between client and (storage) service instances may
   frequently change as a result.

   Digital twin network(DTN)
   [I-D.zhou-nmrg-digitaltwin-network-concepts] has also been proposed
   recently.  It is to introduce digital twin technology into the
   network to build a network system with physical network entities and
   virtual twins, which can be mapped in real time.  The goal of digital
   twin network will be applied not only to industrial Internet, but
   also to operator network.  When the network is large, it needs real-
   time scheduling ability, more efficient and accurate data collection
   and modeling, and promote the automation, intelligent operation and
   maintenance and upgrading of the network.

4.  Problems in Existing Solutions

   There are a number of problems that may occur when realizing the use
   cases in the previous section, this section suggests a classification
   for those problems to aid the possible identification of solution
   components for addressing them.  There are no mature existing
   solutions to solve those problems, most of the existing solutions
   mentioned in the next could solve a similar problems more or less,
   which would be analyzed as a starting point.

4.1.  Dynamicity of Relations

   The mapping from a service identifier to a specific service instance
   that may execute the service for a client usually happens through
   resolving the service identification into a specific IP address at
   which the service instance is reachable.

   Application layer solutions can be foreseen, using an application
   server to resolve binding updates.  While the viability of these
   solutions will generally depend on the additional latency that is
   being introduced by the resolution via said application server,
   frequencies down to changing relations every few (or indeed EVERY)
   service requests is seen as difficult to be viable.

   Message brokers, however, could be used, dispatching incoming service
   requests from clients to a suitable service instance, where such
   dispatching could be controlled by service-specific metrics, such as
   computing load.  The introduction of such brokers, however, may lead



Liu, et al.               Expires 22 July 2022                  [Page 8]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   to adverse effects on efficiency, specifically when it comes to
   additional latencies due to the necessary communication with the
   broker; we discuss this problem separately in the next subsection.

   DNS[RFC1035] using 'early binding' to explicit bind from the service
   identification to the network address before sending user data, so
   the client creates an 'instance affinity' for the service identifier
   that binds the client to the resolved service instance address, which
   could also realize the load balancing.  However, we can foresee
   scenarios in which such 'instance affinity' may change very
   frequently, possibly even at the level of each service request.  DNS
   are not designed for this level of dynamicity.  Updates to the
   mapping between service identifier to service instance address cannot
   be pushed quickly enough into the DNS that takes several minutes
   updates to propagate, and clients would need to frequently resolve
   the original binding.  If try to DNS to meet the this level of
   dynamicity, frequent resolving of the same service name would likely
   lead to an overload of the it.  These issues are also discussed in
   section 5.4 of [I-D.sarathchandra-coin-appcentres].

   A solution that leaves the dispatching of service requests entirely
   to the client may be possible to achieve the needed dynamicity, but
   with the drawback that the individual destinations, i.e., the network
   identifiers for each service instance, must be known to the client
   for doing so.  While this may be viable for certain applications, it
   cannot generally scale with a large number of clients.  Furthermore,
   it may be undesirable for every client to know all available service
   instance identifiers, e.g., for reasons of not wanting to expose this
   information to clients from the perspective of the service provider
   but also, again, for scalability reasons if the number of service
   instances is very high.

   Existing solutions exhibit limitations in providing dynamic 'instance
   affinity', those limitations being inherently linked to the design
   used for the mapping between the service identifier and the address
   of the service instance.  These limitations may lead to 'instance
   affinity' to last many requests or even for the entire session
   between the client and the service, which may be undesirable from the
   service provider perspective in terms of best balance requests across
   many service instances.











Liu, et al.               Expires 22 July 2022                  [Page 9]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


4.2.  Efficiency

   The use of external resolvers, such as application layer repositories
   in general, also affects the efficiency of the overall service
   request.  Additional signaling is required between client and
   resolver, either through the application layer solution, which not
   only leads to more messaging but also to increased latency for the
   additional resolution.  Accommodating smaller instance affinities
   increases this additional signaling but also the latencies
   experienced, overall impacting the efficiency of the overall service
   transaction.

   As mentioned in the previous subsection, broker systems could be used
   to allow for dispatching service requests to different service
   instances at high dynamicity.  However, the usage of such broker
   inevitably introduces 'path stretch' compared to the possible direct
   path between client and service instance, increasing the overall flow
   completion time.

   Existing solutions may introduce additional latencies and
   inefficiencies in packet transmission due to the need for additional
   resolution steps or indirection points, and will lead to the
   accuracy problems to select the appropriate edge.

4.3.  Complexity and Accuracy

   As we can see from the discussion on efficiency in the previous
   subsection, the time when external resolvers collect the necessary
   information and deal with it to select the edge nodes, the network
   and computing resource status may changed already.  So any additional
   control decision on which service instance to choose for which
   incoming service request requires careful planning to keep potential
   inefficiencies, caused by additional latencies and path stretch, at a
   minimum.  Additional control plane elements, such as brokers, are
   usually neither well nor optimally placed in relation to the data
   path that the service request will ultimately traverse.

   Existing solutions require careful planning for the placement of
   necessary control plane functions in relation to the resulting data
   plane traffic to improve the accuracy; a problem often intractable in
   scenarios of varying service demand.










Liu, et al.               Expires 22 July 2022                 [Page 10]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


4.4.  Metric Exposure and Use

   Some systems may use the geographical location, as deduced from IP
   prefix, to pick the closest edge.  The issue here may be that edges
   may not be far apart in edge computing deployments, while it may also
   be hard to deduce geo-location from IP addresses.  Furthermore, the
   geo-location may not be the key distinguishing metric to be
   considered, particularly if geographic co-location does not
   necessarily mean network topology co-location.  Also, "closer
   geographically" does not consider the computing load of possible
   closer yet more loaded nodes, consequently leading to possibly worse
   performance for the end user.

   Solutions may also perform 'health checks' on an infrequent base
   (>1s) to reflect the service node status and switch in fail-over
   situations.  Health checks, however, inadequately reflect an overall
   computing status of a service instance.  It may therefore not reflect
   at all the decision basis a suitable service instance, e.g., based on
   the number of ongoing sessions as an indicator of load.  Infrequent
   checks may also be too coarse in granularity, e.g., for supporting
   mobility-induced dynamics such as the connected car scenario of
   Section 3.2.

   Service brokers may use richer computing metrics (such as load) but
   may lack the necessary network metrics.

   Existing solutions lack the necessary information to make the right
   decision on the selection of the suitable service instance due to the
   limited semantic or due to information not being exposed across
   boundaries between, e.g., service and network provider.

4.5.  Security

   Resolution systems opens up two vectors of attack, namely attacking
   the mapping system itself, as well as attacking the service instance
   directly after having been resolved.  The latter is particularly an
   issue for a service provider who may deploy significant service
   infrastructure since the resolved IP addresses will enable the client
   to directly attack the service instance but also infer (over time)
   information about available service instances in the service
   infrastructure with the possibility of even wider and coordinated
   Denial-of-Service (DoS) attacks.









Liu, et al.               Expires 22 July 2022                 [Page 11]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   Broker systems may prevent this ability by relying on a pure service
   identifier only for the client to broker communication, thereby
   hiding the direct communication to the service instance albeit at the
   expense of the additional latency and inefficiencies discussed in
   Section 4.1 and 4.2.  DoS attacks here would be entirely limited to
   the broker system only since the service instance is hidden by the
   broker.

   Existing solutions may expose control as well as data plane to the
   possibility of a distributed Denial-of-Service attack on the
   resolution system as well as service instance.  Localizing the attack
   to the data plane ingress point would be desirable from the
   perspective of securing service request routing, which is not
   achieved by existing solutions.

4.6.  Changes to Infrastructure

   Dedicated resolution systems, such as the DNS or broker-based
   systems, require appropriate investments into their deployment.
   While the DNS is an inherent part of the Internet infrastructure, its
   inability to deal with the dynamicity in service instance relations,
   as discussed in Section 4.1, may either require significant changes
   to the DNS or the establishment of a separate infrastructure to
   support the needed dynamicity.  In a manner, the efforts on Multi-
   Access Edge Computing [MEC], are proposing such additional
   infrastructure albeit not solely for solving the problem of suitably
   dispatching service requests to service instances (or application
   servers, as called in [MEC]).

   Existing solutions may expose control as well as data plane to the
   possibility of a distributed Denial-of-Service attack on the
   resolution system as well as service instance.  Localizing the attack
   to the data plane ingress point would be desirable from the
   perspective of securing service request routing, which is not
   achieved by existing solutions.

5.  Conclusion

   This document presents use cases in which we observe the demand for
   considering the dynamic nature of service requests in terms of
   requirements on the resources fulfilling them in the form of service
   instances.  In addition, those very service instances may themselves
   be dynamic in availability and status, e.g., in terms of load or
   experienced latency.

   As a consequence, the problem of satisfying service-specific metrics
   to allow for selecting the most suitable service instance among the
   pool of instances available to the service throughout the network is



Liu, et al.               Expires 22 July 2022                 [Page 12]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   a challenge, with a number of observed problems in existing
   solutions.  The use cases as well as the categorization of the
   observed problems may start the process of determining how they are
   best satisfied within the IETF protocol suite or through suitable
   extensions to that protocol suite.

6.  Security Considerations

   TBD

7.  IANA Considerations

   No IANA action is required so far.

8.  Informative References

   [RFC4786]  Abley, J. and K. Lindqvist, "Operation of Anycast
              Services", BCP 126, RFC 4786, DOI 10.17487/RFC4786,
              December 2006, <https://www.rfc-editor.org/info/rfc4786>.

   [RFC1035]  Mockapetris, P., "Domain names - implementation and
              specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,
              November 1987, <https://www.rfc-editor.org/info/rfc1035>.

   [I-D.zhou-nmrg-digitaltwin-network-concepts]
              Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
              Q., Boucadair, M., and C. Jacquenet, "Digital Twin
              Network: Concepts and Reference Architecture", Work in
              Progress, Internet-Draft, draft-zhou-nmrg-digitaltwin-
              network-concepts-06, 2 December 2021,
              <https://www.ietf.org/archive/id/draft-zhou-nmrg-
              digitaltwin-network-concepts-06.txt>.

   [I-D.sarathchandra-coin-appcentres]
              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,
              <https://www.ietf.org/archive/id/draft-sarathchandra-coin-
              appcentres-04.txt>.

   [TR22.874] 3GPP, "Study on traffic characteristics and performance
              requirements for AI/ML model transfer in 5GS (Release
              18)", 2021.

   [HORITA]   Horita, Y., "Extended electronic horizon for automated
              driving", Proceedings of 14th International Conference on
              ITS Telecommunications (ITST)", 2015.



Liu, et al.               Expires 22 July 2022                 [Page 13]


Internet-Draft  Dynamic-Anycast (Dyncast) Use Cases & Pr    January 2022


   [Industry4.0]
              Industry4.0, "Details of the Asset Administration Shell,
              Part 1 & Part 2", 2020.

   [GAIA-X]   Gaia-X, ""GAIA-X: A Federated Data Infrastructure for
              Europe"", 2021.

   [MEC]      ETSI, ""Multi-Access Edge Computing (MEC)"", 2021.

Acknowledgements

   The author would like to thank Yizhou Li, Luigi IANNONE, Mohamed
   Boucadair and Christian Jacquenet for their valuable suggestions to
   this document.

Authors' Addresses

   Peng Liu
   China Mobile

   Email: liupengyjy@chinamobile.com


   Peter Willis
   BT

   Email: peter.j.willis@bt.com


   Dirk Trossen
   Huawei Technologies

   Email: dirk.trossen@huawei.com


   Cheng Li
   Huawei Technologies

   Email: c.l@huawei.com












Liu, et al.               Expires 22 July 2022                 [Page 14]