Dynamic-Anycast (Dyncast) Use Cases and Problem Statement
draft-liu-dyncast-ps-usecases-00

dyncast                                                           P. Liu
Internet-Draft                                              China Mobile
Intended status: Informational                                 P. Willis
Expires: August 1, 2021                                               BT
                                                              D. Trossen
                                                                  Huawei
                                                        February 1, 2021

        Dynamic-Anycast (Dyncast) Use Cases and Problem Statement
                    draft-liu-dyncast-ps-usecases-00 

Abstract

   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

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

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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) . . .  4
     3.2.  Connected Car  . . . . . . . . . . . . . . . . . . . . . .  6
     3.3.  Digital Twin . . . . . . . . . . . . . . . . . . . . . . .  6
   4.  Shortcomings of Existing Solutions . . . . . . . . . . . . . .  7
   5.  Desirable System Characteristics and Requirements  . . . . . .  9
     5.1.  Anycast-based Service Addressing Methodology . . . . . . .  9
     5.2.  Instance Affinity  . . . . . . . . . . . . . . . . . . . . 10
     5.3.  Encoding Metrics . . . . . . . . . . . . . . . . . . . . . 10
     5.4.  Signaling Metrics  . . . . . . . . . . . . . . . . . . . . 11
     5.5.  Using Metrics in Routing Decisions . . . . . . . . . . . . 11
     5.6.  Supporting Service Dynamism  . . . . . . . . . . . . . . . 12
   6.  Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 13
   7.  Security Considerations  . . . . . . . . . . . . . . . . . . . 13
   8.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 13
   9.  Informative References . . . . . . . . . . . . . . . . . . . . 13
   Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . 14
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 14

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
 

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   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.

   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
   metrics.

   Distributing a service request to a specific service having multiple
 

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   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) routing problem. This draft describes usage scenarios,
   problem space and key areas of investigation for this dyncast
   problem.

2.  Definition of Terms

   Anycast: An addressing and routing methodology that assign an
   "anycast" address for one or more network locations to which requests
   to an "anycast" address could be routed.

   Dyncast: Dynamic Anycast, taking the dynamic nature of computing
   resource metrics into account to steer an anycast routing decision.

   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.

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 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
 

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   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 can expect an
   upper bound of roughly 5ms for the round trip latency in these
   scenarios.

   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.
 

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   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.

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
 

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   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.  Shortcomings of Existing Solutions

   Given that the current state of the art for routing is based on the
   network cost, computing resource and/or load information as well as
   other service-specific metrics are not available nor distributed at
   the network layer. At the same time, computing resource metrics are
   not well defined and understood by the network. Furthermore, although
   we have focused in our examples on computing load and networking
   latency, metrics that decide the selection of the most appropriate
   service instance may not be limited to those. Proximity, even of
   physical nature, as well as capabilities of computing and network
   resources may be other metrics used for the selection of an
   appropriate service instance, while a "computing metric" itself can
   include aspects such as CPU/GPU capacity and load, number of sessions
   currently serving, latency of service process expected, possibly
   applying weights to each metric. Overall, we observe that given the
   service-specific nature of the notion of "best instance", it is hard
   to make the best choice of the edge based on both computing and
   network metrics at the same time, leading to the problems observed in
   the following when realizing the use cases in Section 3. 
 

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   As a key takeaway from Section 3, we observe that a service request
   should be dynamically routed to the most suitable service instance in
   real time among the multiple edges in which service instances have
   been deployed. Existing mechanisms use one or more of the following
   ways and each of them has issues associated.

   o  Use the least network cost as metric to select the edge.  Issue:
      Computing information, among other metrics, is key to be
      considered in edge computing, and it is not included here. In our
      scenarios of Section 3, this may lead to service requests routed
      to closer albeit possibly overloaded edge-based service instances,
      degrading the service quality.

   o  Use of geographical location, as deduced from IP prefix, is used
      to pick the closest edge.  Issue: Edges are not so far apart in
      edge computing scenario.  Either hard to be deduced from IP
      address or the location is not the key distinguishing metric to be
      considered., particularly since geographic co-location does not
      necessarily mean network topology co-location. Furthermore,
      "closer geographically" does not consider the computing load of
      possible closer yet more loaded nodes, similar to the previous
      point.

   o  Health check on an infrequent base (>1s) to reflect the service
      node status, and switch when fail-over.  Issue: Health check is
      very different from computing status information of service
      instance and may not reflect at all the decision basis for the
      scenarios, e.g., the number of ongoing sessions as an indicator of
      load.  It may also be too coarse in granularity, e.g., for
      supporting mobility-induced dynamics such as the connected car
      scenario of Section 3.2.

   o  Application layer randomly picks or uses round-robin mechanism to
      pick a service instance.  Issue: It may share the load across
      multiple service instances in terms of the computing capacity all
      while assuming equal resource capability for each service
      instance, the network cost variance is barely considered.  Edges
      can be deployed in different cities which are not equal cost paths
      to a client. Therefore network status is also a major concern.
      Also, in our scenarios of Section 3, the choice of "nearest" or
      "closer" is required for a better choice.

   o  Global resolver and early binding (DNS-based load balancing):
      Client queries a global resolver or load balancer first and gets
      the exact server's address.  And then steer traffic using that
      address as binding address.  It is called early binding because an
      explicit binding address query has to be performed before sending
      user data.  Issue: Firstly, it clashes with the service dynamism
 

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      across all scenarios in Section 3. Also, the resolver does not
      have the capability of such high frequent change of indirection to
      new instance based on the frequent change of each service
      instance.  Secondly, edge computing flow can be short.  One or two
      round trips would be completed, requiring very frequent bindings
      with accompanying DNS resolutions, while an out-of-band query for
      specific server address has high overhead as it takes one more
      round trip. Lastly, to avoid DNS resolution for every request,
      out-of-band signaling would also be required to the client to
      remove any previous DNS resolution from the client-local DNS cache
      to avoid the use of stale DNS entries. 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 purpose.

   o  Traditional anycast.  Issue: Only works for single request/reply
      communication.  No instance affinity (see Section 5.2) guaranteed
      nor is any service-specific metric, such as load and latency,
      being considered. This would cause significant issues in all our
      use cases in Section 3

   o  EIGRP [RFC7868]: Although allowing a number of metrics, EIGRP has
      no notion of computing load as a metric, while it also does not
      enforce instance affinity, which may lead to problems in our use
      cases of Section 3 in service requests being sent mid-request to
      other service instances. 

5.  Desirable System Characteristics and Requirements

   In the following, we outline the desirable characteristics of a
   system to overcome the observed problems in Section 4 for the
   realization of the use cases in Section 3.

5.1.  Anycast-based Service Addressing Methodology

   A unique service identifier is used by all the service instances for
   a specific service no matter which edge it attaches to. An anycast
   like addressing and routing methodology among multiple edges makes
   sure the data packet can potentially reach any of the edges with the
   service instance attached. At the same time, each service instance
   has its own unicast address to be used by the attaching edge to
   access the service.Since a client will use the service identifier as
   the destination addressing, mapping of the service identifier to the
   unicast address will need to happen in-band, considering the metrics
   for selection to make this selection service-specific. From an
   addressing perspective, a desirable system  

 

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   o  MUST provide a discovery and mapping methodology for the in-band
      mapping of the service identifier (an anycast address) to a
      specific unicast address. 

5.2.  Instance Affinity

   A routing relation between a client and a service exists not at the
   packet but at the service request level in the sense that one or more
   service requests, possibly consisting of one or many more routing-
   level packets, must be ensured to be sent to said service.Each
   service may be provided by one or more service instances, each
   providing equivalent service functionality to their respective
   clients, while those service instances may be deployed at different
   locations in the network. With that, the routing problem becomes one
   between the client and a selected service instance for at least the
   duration of the service-level request, but possibly more than just
   one request. 

   This relationship between the client and the chosen service instance
   is described as "instance affinity" in the following, where the
   "affinity" spans across the aforementioned one or more service
   requests. This impacts the routing decision to be taken in that the
   normal packet level communication, i.e., each packet is forwarded
   individually based on the forwarding table at the time, will need
   extending with the notion of instance affinity since otherwise
   individual packets may be sent to different places when the network
   status changes, possibly segmenting individual requests and breaking
   service-level semantics. 

   The nature of this affinity is highly dependent on the nature of the
   specific service. The minimal affinity of a single request represents
   a stateless service, where each service request may be responded to
   without any state being held at the service instance for fulfilling
   the request. Providing any necessary information/state in-band as
   part of the service request, e.g., in the form of a multi-form body
   in an HTTP request or through the URL provided as part of the
   request, is one way to achieve such stateless nature. Alternatively,
   the affinity to a particular service instance may span more than one
   request, as in our VR example in Section 3.1, where previous client
   input is needed to render subsequent frames. Therefore, a desirable
   system

   o  MUST maintain "instance affinity" which MAY span one or more
      service requests, i.e., all the packets from the same flow MUST go
      to the same service instance.

5.3.  Encoding Metrics

 

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   As outlined in our scenarios in Section 3, metrics can have many
   different semantics, particularly if considered to be service-
   specific. Even the notion of a "computing load" metric may be
   computed in many different ways. What is crucial, however, is the
   representation and encoding of that metric when being conveyed to the
   routing fabric in order for the routing elements to act upon those
   metrics. Such representation may entail information on the semantics
   of the metric or it may be purely one or more semantic-free numerals.
   Agreement of the chosen representation among all service and network
   elements participating in the service-specific routing decision is
   important. Specifically, a desirable system 

   o  MUST agree on the service-specific metrics and their
      representation between service elements in the participating edges
      in the network and network elements acting upon them. 

   o  MAY obfuscate the specific semantic of the metric to preserve
      privacy of the service provider information towards the network
      provider.

   o  MAY include routing protocol metrics

5.4.  Signaling Metrics

   The aforementioned representation of metrics needs conveyance to the
   network elements that will need to act upon them. Depending on the
   service-specific decision logic, one or more metrics will need to be
   conveyed. Problems to be addressed here may be that of loop avoidance
   of any advertisement of metrics as well as the frequency of such
   conveyance and therefore the overall load that the signaling may add
   to the overall network traffic. While existing routing protocols may
   serve as a baseline for signaling metrics, other means to convey the
   metrics can equally be realized. Specifically, a desirable system

   o  MUST provide mechanisms to signal the metrics for using in routing
      decisions

   o  MUST realize means for rate control for signaling of metrics

   o  MUST implement mechanisms for loop avoidance in signaling metrics,
      when necessary

5.5.  Using Metrics in Routing Decisions

   Metrics being conveyed, as outlined in Section 5.4, in the agreed
   manner, as outlined in Section 5.3, will ultimately need suitable
   action in the routers of the network. Routing decisions can be
   manifold, possibly including (i) min or max over all metrics, (ii)
 

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   extending previous action with a random or first choice when more
   than one min/max entry found, (iii) weighted round robin of all
   entries, among others. It is important for the proper work of the
   service-specific routing decision, that it is understood to both
   network and service provider, which action (out of a possible set of
   supported actions) is to be used for a particular set of metrics.
   Specifically, a desirable system

   o  MUST specify a default action to be taken, if more than one action
      possible

   o  SHOULD enable other alternative actions to be taken. 

      o  Any solution MUST provide appropriate signaling of the desired
         action to the router. For this, the action MAY be signaled in
         combination with signaling the metric (see Section 5.4). 

      o  Any solution SHOULD allow associating the desired action to a
         specific service identifier.

5.6.  Supporting Service Dynamism

   Network cost in the current routing system usually does not change
   very frequently. However, computing load and service-specific metrics
   in general can be highly dynamic, e.g., changing rapidly with the
   number of sessions, CPU/GPU utilization and memory space. It has to
   be determined at what interval or events such information needs to be
   distributed among edges.  More frequent distribution of more accurate
   synchronization may result in more overhead in terms of signaling.

   Choosing the least path cost is the most common rule in routing.
   However, the logic does not work well when routing should be aware of
   service-specific metrics. Choosing the least computing load may
   result in oscillation. The least loaded edge can quickly be flooded
   by the huge number of new computing demands and soon become
   overloaded with tidal effects possibly following.

   Generally, a single instance may have very dynamic resource
   availability over time in order to serve service requests. This
   availability may be affected by computing resource capability and
   load, network path quality, and others. The balancing mechanisms
   should adapt to the service dynamism quickly and seamlessly. With
   this, the relationship between a single client and the set of
   possible service instances may possibly be very dynamic in that one
   request that is being dispatched to instance A may be followed by a
   request that is being dispatched to instance B and so on, generally
   within the notion of the service-specific service affinity discussed
   before in Section 5.2. With this in mind, a desirable system
 

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   o  MUST support the dynamics of metrics changing on, e.g., a per flow
      basis, without violating the metrics defined in the selection of
      the specific service instance, while taking into account the
      requirements for the signaling of metrics and routing decision
      (see Section 5.4 and 5.5).

6.  Conclusion

   This document presents use cases in which we observe the demand for
   consideration of the dynamic nature of service requests as well as
   the availability of network resources so as to satisfy service-
   specific metrics to allow for selecting the most suitable service
   instance among the pool of instances available to the service
   throughout the network. These use cases and the observed problems
   with existing solutions motivate the outline for a desirable system
   that may provide a solution for realizing the use cases outlined in
   this document; we call this system Dyncast due to its anycast-based
   addressing methodology.

   We have formulated high-level requirements for solutions to Dyncast,
   where the architecture should address how to distribute the resource
   information at the network layer and how to assure instance affinity
   in an anycast based service addressing environment, while realizing
   appropriate routing actions to satisfy the metrics provided.

7.  Security Considerations

   TBD

8.  IANA Considerations

   No IANA action is required so far.

9.  Informative References

   [RFC7868] D. Davage et al. , "Cisco's Enhanced Interior Gateway
       Routing Protocol (EIGRP)", RFC 7868, May 2016,
       https://tools.ietf.org/html/rfc7868

   [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

 

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   [Industry4.0]  Industry4.0, "Details of the Asset Administration
       Shell, Part 1 & Part 2", November 2020, https://www.plattform-
       i40.de/PI40/Redaktion/EN/Standardartikel/specification-
       administrationshell.html.

   [GAIA-X] Gaia-X, "GAIA-X: A Federated Data Infrastructure for
       Europe", accessed January 2021, https://www.data-
       infrastructure.eu/GAIAX/Navigation/EN/Home/home.html

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

Acknowledgements

   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
   BT
   Email: peter.j.willis@bt.com

   Dirk Trossen
   Huawei
   Email: dirk.trossen@huawei.com

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