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Versions: 00 01 02 03 04                                                
dyncast                                                           P. Liu
Internet-Draft                                              China Mobile
Intended status: Informational                                 P. Willis
Expires: August 15, 2021                                              BT
                                                              D. Trossen
                                                       February 15, 2021

        Dynamic-Anycast (Dyncast) Use Cases & Problem Statement


   Service providers are exploring the edge computing to achieve better
   response time, control over data and carbon energy saving by moving
   the computing services towards the edge of the network in 5G MEC
   (Multi-access Edge Computing) scenarios, virtualized central office,
   and others.  Providing services by sharing computing resources from
   multiple edges is an emerging concept that is becoming more useful
   for computationally intensive tasks. Ideally, services should be
   computationally balanced using service-specific metrics instead of
   simply  dispatching the service in a static way, e.g., to the
   geographically closest edge since this may cause unbalanced usage of
   computing resources at edges which further degrades user experience
   and system utilization. This draft provides an overview of scenarios
   and problems associated with realizing such scenarios.

   The document identifies 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
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   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."

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   This Internet-Draft will expire on July 22, 2021.

Copyright Notice

   Copyright (c) 2019 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
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   publication of this document.  Please review these documents
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  2
   2.  Definition of Terms  . . . . . . . . . . . . . . . . . . . . .  4
   3.  Use Cases  . . . . . . . . . . . . . . . . . . . . . . . . . .  4
     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 . . . . . . . . . . . . . . . .  7
     4.1.  Dynamicity of Relations  . . . . . . . . . . . . . . . . .  7
     4.2.  Efficiency . . . . . . . . . . . . . . . . . . . . . . . .  9
     4.3.  Complexity . . . . . . . . . . . . . . . . . . . . . . . .  9
     4.4.  Metric Exposure and Use  . . . . . . . . . . . . . . . . . 10
     4.5.  Security . . . . . . . . . . . . . . . . . . . . . . . . . 11
     4.6.  Changes to Infrastructure  . . . . . . . . . . . . . . . . 11
   5.  Conclusions  . . . . . . . . . . . . . . . . . . . . . . . . . 12
   6.  Security Considerations  . . . . . . . . . . . . . . . . . . . 12
   7.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 12
   8.  Informative References . . . . . . . . . . . . . . . . . . . . 12
   Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . 13
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 13

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

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   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.

   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. 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.

   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 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

   Distributing a service request to a specific service having multiple
   instances attached to multiple edge computing sites, while taking

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   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.

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.

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

   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

   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

   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

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   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,
   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.

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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.

   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.

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.

4.1.  Dynamicity of Relations

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   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. This is the case, for
   instance, when utilizing DNS [RFC1035] for this step, as utilized in
   most content delivery networks or in DNS-based load balancing
   solutions. This is called 'early binding'  because an explicit
   binding from the service identification to the network address has to
   be performed before sending user data. Through this resolution, the
   client creates an 'instance affinity' for the service identifier that
   binds the client to the resolved service instance address.

   We can foresee scenarios in which such 'instance affinity' may change
   very frequently, possibly even at the level of each service request.
   Systems such as the DNS are not designed for this level of
   dynamicity. Firstly, updates to the mapping between service
   identifier to service instance address cannot be pushed quickly
   enough into the DNS to be available fast enough since it usually
   takes several minutes for DNS updates to propagate. Secondly, clients
   would need to frequently resolve the original binding, while also
   actively flushing the local DNS cache since most client
   implementations would provide cached results of previously resolved
   requests. Regardless of those aspects, frequent resolving of the same
   service name would likely lead to an overload of the DNS,
   particularly when scaling the number of clients and service instance
   relations. These issues are also discussed in section 5.4 of [I-
   D.sarathchandra-coin-appcentres], outlining the significant
   challenges for the flexible re-routing to appropriate service
   instances out of an available pool when utilizing DNS for this

   Application layer solutions can also be foreseen, which do not rely
   on the DNS but instead use 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.

   A solution that leaves the dispatching of service requests entirely

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   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.

4.2.  Efficiency

   The use of external resolvers, such as the DNS or 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 DNS or some 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.

4.3.  Complexity

   As we can see from the discussion on efficiency in the previous
   subsection, any additional control decision on which service instance
   to choose for which incoming service request requires careful

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   planning to keep potential inefficiencies, caused by additional
   latencies and path stretch, at a minimum. Additional control plane
   elements, such as DNS resolvers or brokers, are usually neither well
   nor optimally placed in relation to the data path that the service
   request will ultimately traverse. Solutions like EIGRP [RFC7868] are
   realized at the data plane and therefore remove those inefficiencies
   but suffer (as discussed in Section 4.4) from other limitations.

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

4.4.  Metric Exposure and Use

   Solutions such as EIGRP [RFC7868] do allow for a number of metrics
   being used for a routing decision although EIGRP has no notion of
   computing load as a metric since computing information is not being
   exposed to the routing layer realized by the network provider. In
   addition, EIGRP does not enforce instance affinity, which may lead to
   problems in our use cases of Section 3 in service requests may be
   sent mid-request to other service instances.

   Other 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.

   Resolution systems such as the DNS do often not allow for
   constraining requests to resolve a service name at all, while service
   brokers may use richer computing metrics (such as load) but may lack
   the necessary network metrics.

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      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

4.5.  Security

   Resolution systems, such as the DNS, open up two vectors of attack,
   namely attacking the mapping system itself, i.e., the DNS, 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.

   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]).

   The support for network layer solutions such as EIGRP [RFC7868]

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   requires suitable router upgrades, while still lacking a number of
   aspects important for the realization of the use cases in Section 3,
   including the support for instance affinity in the routing decision.

      Existing solutions require changes to either service and/or
      network infrastructure, with no solution limiting the necessary
      changes to the very ingress point of the network where the demand
      for more flexible service request routing initiates from in the
      form of the client initiating the service request.

5.  Conclusions

   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
   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


7.  IANA Considerations

   No IANA action is required so far.

8.  Informative References

   [RFC7868] D. Davage et al. , "Cisco's Enhanced Interior Gateway
       Routing Protocol (EIGRP)", RFC 7868, May 2016,

   [RFC4786] J. Abley, K. Lingqvist, "Operation of Anycast Services",
       RFC4786, December 2006, https://tools.ietf.org/html/rfc4786

       SPECIFICATION", RFC1035, November 1987,

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   [I-D.sarathchandra-coin-appcentres] Trossen, D., Sarathchandra, C.,
       and M. Boniface, "In-Network Computing for App-Centric Micro-
       Services", draft-sarathchandra-coin-appcentres-03 (work in
       progress), October 2020.

   [TR22.874] 3GPP, "Study on traffic characteristics and performance
       requirements for AI/ML model transfer in 5GS (Release 18)", TR
       22.874 V0.2.0, November 2020

   [Industry4.0]  Industry4.0, "Details of the Asset Administration
       Shell, Part 1 & Part 2", November 2020, https://www.plattform-

   [GAIA-X] Gaia-X, "GAIA-X: A Federated Data Infrastructure for
       Europe", accessed January 2021, https://www.data-

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

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   The author would like to thank Yizhou Li, Luigi IANNONE and Geng
   Liang for their valuable suggestions to this document.

Authors' Addresses

   Peng Liu
   China Mobile
   Email: liupengyjy@chinamobile.com

   Peter Willis
   Email: peter.j.willis@bt.com

   Dirk Trossen
   Email: dirk.trossen@huawei.com

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