rtgwg                                                             P. Liu
Internet-Draft                                              China Mobile
Intended status: Informational                                P. Eardley
Expires: 8 September 2022                                British Telecom
                                                              D. Trossen
                                                     Huawei Technologies
                                                            M. Boucadair
                                                           LM. Contreras
                                                                   C. Li
                                                     Huawei Technologies
                                                            7 March 2022

       Dynamic-Anycast (Dyncast) Use Cases and Problem Statement


   Many service providers have been exploring distributed computing
   techniques to achieve better service response time and optimized
   energy consumption.  Such techniques rely upon the distribution of
   computing services and capabilities over many locations in the
   network, such as its edge, the metro region, virtualized central
   office, and other locations.  In such a distributed computing
   environment, providing services by utilizing 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

   This document provides an overview of scenarios and problems
   associated with realizing such scenarios, identifying key engineering
   investigation areas which require adequate architectures and
   protocols to achieve balanced computing and networking resource
   utilization among facilities providing the services.

Status of This Memo

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

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Definition of Terms . . . . . . . . . . . . . . . . . . . . .   4
   3.  Sample Use Cases  . . . . . . . . . . . . . . . . . . . . . .   5
     3.1.  Cloud Virtual Reality (VR) or Augmented Reality (AR)  . .   6
     3.2.  Intelligent Transportation  . . . . . . . . . . . . . . .   8
     3.3.  Digital Twin  . . . . . . . . . . . . . . . . . . . . . .   9
   4.  Problems in Existing Solutions  . . . . . . . . . . . . . . .  10
     4.1.  Dynamicity of Relations . . . . . . . . . . . . . . . . .  10
     4.2.  Efficiency  . . . . . . . . . . . . . . . . . . . . . . .  12
     4.3.  Complexity and Accuracy . . . . . . . . . . . . . . . . .  12
     4.4.  Metric Exposure and Use . . . . . . . . . . . . . . . . .  13
     4.5.  Security  . . . . . . . . . . . . . . . . . . . . . . . .  13
     4.6.  Changes to Infrastructure . . . . . . . . . . . . . . . .  14
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  14
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  15
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  15
   8.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  15
   9.  Informative References  . . . . . . . . . . . . . . . . . . .  15
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

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1.  Introduction

   Edge computing aims to provide better response times and transfer
   rates compared to Cloud Computing, by moving the computing towards
   the edge of a network.  Edge computing can be built on embedded
   systems, gateways, and others, all being located close to end users'
   premises.  There is an emerging requirement that multiple edge sites
   (called "edges", for for short) are deployed at different locations
   to provide a 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 behaving as service nodes.  Depending on
   the location of an edge and its capacity, different computing
   resources can be contributed by each edge to deliver a service.  At
   peak hours, computing resources attached to a client's closest edge
   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 to
   the potential maximum capacity is neither feasible nor economically
   viable in many cases.

   Some user devices are battery-dependent.  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 among available sources.  For example, DNS-based load balancing
   usually configures a domain in the Domain Name System (DNS) such that
   client requests to that domain name are distributed across a group of
   servers.  It usually provides several IP addresses for a domain name.

   There are some dynamic solutions to distribute the requests to the
   server that best fits a service-specific metric, such as the best
   available resources and minimal load.  They usually require Layer 4 -
   Layer 7 handling of the packet processing, such as through DNS-based
   or indirection servers.  Such an approach is inefficient for large
   number of short connections.  At the same time, such approaches can

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   often not retrieve the desired metric, such as 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 considerations of both computing and network metrics or
   makes rather long-term decisions due to the (upper layer) overhead in
   the decision making itself.

   Distributing service requests to a specific service having multiple
   instances attached to multiple edges, while taking into account
   computing as well as service-specific metrics in the distribution
   decision, is seen as a dynamic anycast (or "dyncast", for short)
   problem of sending service requests, without prescribing the use of a
   routing solution.

   As a problem statement, this document describes sample usage
   scenarios as well as key areas in which current solutions lead to
   problems that ultimately affect the deployment (including the
   performance) of edge services.  Those key areas target the
   identification of candidate solution components.

2.  Definition of Terms

   This document makes use of the following terms:

   Service:  A monolithic functionality that is provided by an endpoint
     according to the specification for said service.  A composite
     service can be built by orchestrating monolithic services.

   Service instance:  Running environment (e.g., a node) that makes the
     functionality of a service available.  One service can have several
     instances running at different network locations.

   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 behavior, no matter where those service
     instances are running.

   Anycast:  An addressing and packet forwarding approach that assigns
     an "anycast" identifier for one or more service instances to which
     requests to an "anycast" identifier could be routed/forwarded,
     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.

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3.  Sample Use Cases

   This section presents a non-exhaustive list of scenarios which
   require multiple edge sites to interconnect and to coordinate at the
   network layer to meet the service requirements and ensure better user

   Before outlining the use cases, however, let us describe a basic
   model that we assume through which those use cases are being
   realized.  This model justifies the choice of the terminology
   introduced in Section 2.

   We assume that clients access one or more services with an objective
   to meet a desired user experience.  Each participating service may be
   realized at one or more places in the network (called, service
   instances).  Such service instances are instantiated and deployed as
   part of the overall service deployment process, e.g., using existing
   orchestration frameworks, within so-called edge sites, which in turn
   are reachable through a network infrastructure via an egress router.

   When a client issues a service request to a required service, the
   request is being steered by its ingress router to one of the
   available service instances that realize the requested service.  Each
   service instance may act as a client towards another service, thereby
   seeing its own outbound traffic steered to a suitable service
   instance of the request service and so on, achieving service
   composition and chaining as a result.

   The aforementioned selection of one of candidate service instances is
   done using traffic steering methods , where the steering decision may
   take into account pre-planned policies (assignment of certain clients
   to certain service instances), realize shortest-path to the 'closest'
   service instance, or utilize more complex and possibly dynamic metric
   information, such as load of service instances, latencies experienced
   or similar, for a more dynamic selection of a suitable service

   It is important to note that clients may move throughout the
   execution of a service, which may, as a result, position other
   service instance 'better' in terms of latency, load, or other
   metrics.  This creates a (physical) dynamicity that will need to be
   catered for.

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   Apart from the input into the traffic steering decision, under the
   aforementioned constraint of possible client mobility, its
   realization may differ in terms of the layer of the protocol stack at
   which the needed operations for the decision are implemented.
   Possible layers are application, transport, or network layers.
   Section 4 discusses some choice realization issues.

   As a summary, Figure 1 outlines the main aspects of the assumed
   system model for realizing the use cases that follow next.

        +------------+      +------------+       +------------+
      +------------+ |    +------------+ |     +------------+ |
      |    edge    | |    |    edge    | |     |    edge    | |
      |   site 1   |-+    |   site 2   |-+     |   site 3   |-+
      +-----+------+      +------+-----+       +------+-----+
            |                    |                    |
       +----+-----+        +-----+----+         +-----+----+
       | Router 1 |        | Router 2 |         | Router 3 |
       +----+-----+        +-----+----+         +-----+----+
            |                    |                    |
            |           +--------+--------+           |
            |           |                 |           |
            +-----------|  Infrastructure |-----------+
                        |                 |
                            | Ingress |
            +---------------|  Router |--------------+
            |               +----+----+              |
            |                    |                   |
         +--+--+              +--+---+           +---+--+
       +------+|            +------+ |         +------+ |
       |client|+            |client|-+         |client|-+
       +------+             +------+           +------+

                      Figure 1: Dyncast Use Case Model

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

   Cloud VR/AR services are used in some exhibitions, scenic spots, and
   celebration ceremonies.  In the future, they might be used in more
   applications, such as industrial internet, medical industry, and meta

   Cloud VR/AR introduces the concept 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

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   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

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

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   Furthermore, specific techniques may be employed to 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.  Intelligent Transportation

   For the convenience of transportation, more video capture devices are
   required to be deployed as urban infrastructure, and the better video
   quality is also required to facilitate the content analysis.  So, the
   transmission capacity of the network will need to be further
   increased, and the collected video data needs to be further
   processed, such as for pedestrian face recognition, vehicle moving
   track recognition, and prediction.  This, in turn, also impacts the
   requirements for the video processing capacity of computing nodes.

   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

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   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,
   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.

   In video recognition scenarios, when the number of waiting people and
   vehicles increases, more computing resources are needed to process
   the video content.  For rush hour traffic congestion and weekend
   personnel flow from the edge of a city to the city center, efficient
   network and computing capacity scheduling is also required.  Those
   would cause the overload of the nearest edge sites if there is no
   extra method used, and some of the service request flow might be
   steered to others edge site except the nearest one.

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
   promote 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

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   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.

   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 for networks[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 listed in the previous section.  This section suggests a
   classification for those problems to aid the possible identification
   of solution components for addressing them.

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.

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   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
   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] realizes an 'early binding' to explicitly 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.  One such driver may be frequently changing metrics
   for the decision making, such as latency and load of the involved
   service instance.  Also client mobility creates a natural/physical
   dynamicity with the result that 'better' service instances may become
   available and, vice versa, previous assignments of the client to a
   service instance may be less optimal, leading to reduced performance,
   such as through increased latency.

   DNS is 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 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

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   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, particularly when relying on an indirection
   point in the form of a resolution or load balancing server.  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.

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

   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 change 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

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   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.

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

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   infrastructure with the possibility of even wider and coordinated
   Denial-of-Service (DoS) attacks.

   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

   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.

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   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
   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

   Section 4.5 discusses some security considerations.

7.  IANA Considerations

   This document does not make any IANA request.

8.  Contributors

   The following people have substantially contributed to this document:

           Peter Willis

9.  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>.

              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-07, 5 March 2022,

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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,

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

   [TR-466]   BBF, "TR-466 Metro Compute Networking: Use Cases and High
              Level Requirements", 2021.

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

              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.


   The author would like to thank Yizhou Li, Luigi IANNONE, Christian
   Jacquenet, Kehan Yao and Yuexia Fu for their valuable suggestions to
   this document.

Authors' Addresses

   Peng Liu
   China Mobile
   Email: liupengyjy@chinamobile.com

   Philip Eardley
   British Telecom
   Email: philip.eardley@bt.com

   Dirk Trossen
   Huawei Technologies

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   Email: dirk.trossen@huawei.com

   Mohamed Boucadair
   Email: mohamed.boucadair@orange.com

   Luis M. Contreras
   Email: luismiguel.contrerasmurillo@telefonica.com

   Cheng Li
   Huawei Technologies
   Email: c.l@huawei.com

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