Dynamic-Anycast (Dyncast) Use Cases and Problem Statement
draft-liu-dyncast-ps-usecases-03
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| Authors | Peng Liu , Philip Eardley , Dirk Trossen , Mohamed Boucadair , Luis M. Contreras , Cheng Li | ||
| Last updated | 2022-03-07 | ||
| Replaced by | draft-liu-can-ps-usecases | ||
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draft-liu-dyncast-ps-usecases-03
rtgwg P. Liu
Internet-Draft China Mobile
Intended status: Informational P. Eardley
Expires: 8 September 2022 British Telecom
D. Trossen
Huawei Technologies
M. Boucadair
Orange
LM. Contreras
Telefonica
C. Li
Huawei Technologies
7 March 2022
Dynamic-Anycast (Dyncast) Use Cases and Problem Statement
draft-liu-dyncast-ps-usecases-03
Abstract
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
performance.
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
experience.
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
instance.
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
verse.
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
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, which is close to the frame rendering computing
delay.
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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
transaction.
As mentioned in the previous subsection, broker systems could be used
to allow for dispatching service requests to different service
instances at high dynamicity. However, the usage of such broker
inevitably introduces 'path stretch' compared to the possible direct
path between client and service instance, increasing the overall flow
completion time.
Existing solutions may introduce additional latencies and
inefficiencies in packet transmission due to the need for additional
resolution steps or indirection points， and will lead to the
accuracy problems to select the appropriate edge.
4.3. Complexity and Accuracy
As we can see from the discussion on efficiency in the previous
subsection, the time when external resolvers collect the necessary
information and deal with it to select the edge nodes, the network
and computing resource status may 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
broker.
Existing solutions may expose control as well as data plane to the
possibility of a distributed Denial-of-Service attack on the
resolution system as well as service instance. Localizing the attack
to the data plane ingress point would be desirable from the
perspective of securing service request routing, which is not
achieved by existing solutions.
4.6. Changes to Infrastructure
Dedicated resolution systems, such as the DNS or broker-based
systems, require appropriate investments into their deployment.
While the DNS is an inherent part of the Internet infrastructure, its
inability to deal with the dynamicity in service instance relations,
as discussed in Section 4.1, may either require significant changes
to the DNS or the establishment of a separate infrastructure to
support the needed dynamicity. In a manner, the efforts on Multi-
Access Edge Computing [MEC], are proposing such additional
infrastructure albeit not solely for solving the problem of suitably
dispatching service requests to service instances (or application
servers, as called in [MEC]).
Existing solutions may expose control as well as data plane to the
possibility of a distributed Denial-of-Service attack on the
resolution system as well as service instance. Localizing the attack
to the data plane ingress point would be desirable from the
perspective of securing service request routing, which is not
achieved by existing solutions.
5. Conclusion
This document presents use cases in which we observe the demand for
considering the dynamic nature of service requests in terms of
requirements on the resources fulfilling them in the form of service
instances. In addition, those very service instances may themselves
be dynamic in availability and status, e.g., in terms of load or
experienced latency.
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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
BT
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>.
[I-D.zhou-nmrg-digitaltwin-network-concepts]
Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
Q., Boucadair, M., and C. Jacquenet, "Digital Twin
Network: Concepts and Reference Architecture", Work in
Progress, Internet-Draft, draft-zhou-nmrg-digitaltwin-
network-concepts-07, 5 March 2022,
<https://www.ietf.org/archive/id/draft-zhou-nmrg-
digitaltwin-network-concepts-07.txt>.
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[I-D.sarathchandra-coin-appcentres]
Trossen, D., Sarathchandra, C., and M. Boniface, "In-
Network Computing for App-Centric Micro-Services", Work in
Progress, Internet-Draft, draft-sarathchandra-coin-
appcentres-04, 26 January 2021,
<https://www.ietf.org/archive/id/draft-sarathchandra-coin-
appcentres-04.txt>.
[TR22.874] 3GPP, "Study on traffic characteristics and performance
requirements for AI/ML model transfer in 5GS (Release
18)", 2021.
[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]
Industry4.0, "Details of the Asset Administration Shell,
Part 1 & Part 2", 2020.
[GAIA-X] Gaia-X, ""GAIA-X: A Federated Data Infrastructure for
Europe"", 2021.
[MEC] ETSI, ""Multi-Access Edge Computing (MEC)"", 2021.
Acknowledgements
The author would like to thank Yizhou Li, Luigi IANNONE, 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
Orange
Email: mohamed.boucadair@orange.com
Luis M. Contreras
Telefonica
Email: luismiguel.contrerasmurillo@telefonica.com
Cheng Li
Huawei Technologies
Email: c.l@huawei.com
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