Dynamic-Anycast (Dyncast) Use Cases & Problem Statement
draft-liu-dyncast-ps-usecases-02
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| Authors | Peng Liu , Peter Willis , Dirk Trossen , Cheng Li | ||
| Last updated | 2022-01-17 | ||
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draft-liu-dyncast-ps-usecases-02
rtgwg P. Liu
Internet-Draft China Mobile
Intended status: Informational P. Willis
Expires: 22 July 2022 BT
D. Trossen
C. Li
Huawei Technologies
18 January 2022
Dynamic-Anycast (Dyncast) Use Cases & Problem Statement
draft-liu-dyncast-ps-usecases-02
Abstract
Many service providers are exploring distributed computing techniques
to achieve better service response time and optimized energy
consumption. by moving Such techniques rely upon the distribution of
computing services and capabilities over many locations in the
network such as its edge(e.g., 5G MEC (Multi-access Edge Computing)
scenarios), the metro region, virtualized central office, and other
locations. In such a distributed computing environment, providing
services by soliciting computing resources hosted in various
computing facilities (e.g., edges) is being considered, e.g., for
computationally intensive and delay sensitive services. Ideally,
Services should be computationally balanced using service-specific
metrics instead of simply dispatching the service requests in a
static way or optimizing solely connectivity metrics. For example,
systematically directing end user-originated service requests to the
geographically closest edge or some small computing units may lead to
an unbalanced usage of computing resources, which may then degrade
both the user experience and the overall service performance.
This draft provides an overview of scenarios and problems associated
with realizing such scenarios, identifying several key areas which
require more investigations in terms of architecture and protocol to
achieve balanced computing and networking resource utilization among
edges providing the services.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
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Internet-Drafts are draft documents valid for a maximum of six months
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This Internet-Draft will expire on 22 July 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 4
3. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. Cloud Virtual Reality (VR) or Augmented Reality (AR) . . 5
3.2. Connected Car . . . . . . . . . . . . . . . . . . . . . . 6
3.3. Digital Twin . . . . . . . . . . . . . . . . . . . . . . 7
4. Problems in Existing Solutions . . . . . . . . . . . . . . . 8
4.1. Dynamicity of Relations . . . . . . . . . . . . . . . . . 8
4.2. Efficiency . . . . . . . . . . . . . . . . . . . . . . . 10
4.3. Complexity and Accuracy . . . . . . . . . . . . . . . . . 10
4.4. Metric Exposure and Use . . . . . . . . . . . . . . . . . 11
4.5. Security . . . . . . . . . . . . . . . . . . . . . . . . 11
4.6. Changes to Infrastructure . . . . . . . . . . . . . . . . 12
5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 12
6. Security Considerations . . . . . . . . . . . . . . . . . . . 13
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
8. Informative References . . . . . . . . . . . . . . . . . . . 13
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 14
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
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1. Introduction
Edge computing aims to provide better response times and transfer
rate, with respect to Cloud Computing, by moving the computing
towards the edge of the network. Edge computing can be built on
industrial PCs, embedded systems, gateways and others, all being
located close to the end user. There is an emerging requirement that
multiple edge sites (called "edges" too in this document) are
deployed at different locations to provide the service. There are
millions of home gateways, thousands of base stations and hundreds of
central offices in a city that can serve as candidate edges for
hosting service nodes. Depending on the location of the edge and its
capacity, each edge has different computing resources to be used for
a service. At peak hour, computing resources attached to a client's
closest edge site may not be sufficient to handle all the incoming
service requests. Longer response times or even dropping of requests
can be experienced by users. Increasing the computing resources
hosted on each edge site to the potential maximum capacity is neither
feasible nor economical in many cases.
Some user devices are purely battery-driven. Offloading computation
intensive processing to the edge can save battery power. Moreover
the edge may use a data set (for the computation) that may not exist
on the user device because of the size of data pool or due to data
governance reasons.
At the same time, with new technologies such as serverless computing
and container based virtual functions, the service node at an edge
can be easily created and terminated in a sub-second scale, which in
turn changes the availability of a computing resources for a service
dramatically over time, therefore impacting the possibly "best"
decision on where to send a service request from a client.
Traditional techniques to manage the overall load balancing process
of clients issuing requests include choose- the-closest or round-
robin. Those solutions are relatively static, which may cause an
unbalanced distribution in terms of network load and computational
load. For example, DNS-based load balancing usually configures a
domain in Domain Name System (DNS) such that client requests to the
domain are distributed across a group of servers. It usually
provides several IP addresses for a domain name.
There are some dynamic ways which attempt to distribute the request
to the server that best fits a service-specific metric, such as the
best available resources and minimal load. They usually require
L4-L7 handling of the packet processing. It is not an efficient
approach for a large number of short connections. At the same time,
such approaches can often not retrieve the desired metric, such as
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the network status, in real time. Therefore, the choice of the
service node is almost entirely determined by the computing status,
rather than the comprehensive consideration of both computing and
network metrics.
Distributing a service request to a specific service having multiple
instances attached to multiple edge computing sites, while taking
into account computing as well as service-specific metrics in the
distribution decision, can be seen as a dynamic anycast (or "dyncast"
for short) problem of sending service requests, without prescribing
the use of a routing solution at this stage of the discussion.
As a problem statement, this draft describes usage scenarios as well
as key areas in which current solutions lead to problems that
ultimately affect the deployment or the performance of the edge
services. Those key areas target the identification of possible
solution components, while the overall purpose of this document is to
stimulate discussions on the emerging needs outlined in our use cases
and to start the process of determining how they are best satisfied
within the IETF protocol suite or through suitable extensions to that
protocol suite.
2. Definition of Terms
Service: A service represents a defined endpoint of functionality
encoded according to the specification for said service.
Service instance: One service can have several instances running on
different nodes. Service instance is a running environment (e.g.,
a node) that makes the functionality of a service available.
Service identifier: Used to uniquely identify a service, at the same
time identifying the whole set of service instances that each
represent the same service behaviour, no matter where those service
instances are running.
Anycast: An addressing and packet sending methodology that assign an
"anycast" identifier for one or more service instances to which
requests to an "anycast" identifier could be routed, following the
definition in [RFC4786] as anycast being "the practice of making a
particular Service Address available in multiple, discrete,
autonomous locations, such that datagrams sent are routed to one of
several available locations".
Dyncast: Dynamic Anycast, taking the dynamic nature of computing
resource metrics into account to steer an anycast-like decision in
sending an incoming service request.
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3. Use Cases
This section presents several typical scenarios which require
multiple edge sites to interconnect and to co-ordinate at the network
layer to meet the service requirements and ensure user experience.
The scenarios here are exemplary only for the purpose of this
document and not comprehensive.
3.1. Cloud Virtual Reality (VR) or Augmented Reality (AR)
Cloud VR/AR are now used in some exhibitions, scenic spots and
celebration ceremonies. In the future, they would be used in more
applications such as industrial internet, medical industry and meta
verse . They are the basic technologies to provide a better user
experience.
Cloud VR/AR introduces the concept and technology of cloud computing
to the rendering of audiovisual assets in such applications. Here,
the edge cloud helps encode/decode and render content. The end
device usually only uploads posture or control information to the
edge and then VR/AR contents are rendered in the edge cloud. The
video and audio outputs generated from the edge cloud are encoded,
compressed, and transmitted back to the end device or further
transmitted to central data center via high bandwidth networks.
Edge sites may use CPU or GPU for encode/decode. GPU usually has
better performance but CPU is simpler and more straightforward to use
as well as possibly more widespread in deployment. Available
remaining resources determines if a service instance can be started.
The instance's CPU, GPU and memory utilization has a high impact on
the processing delay on encoding, decoding and rendering. At the
same time, the network path quality to the edge site is a key for
user experience of quality of audio/ video and input command response
times.
A Cloud VR service, such as a mobile gaming service, brings
challenging requirements to both network and computing so that the
edge node to serve a service request has to be carefully selected to
make sure it has sufficient computing resource and good network path.
For example, for an entry-level Cloud VR (panoramic 8K 2D video) with
110-degree Field of View (FOV) transmission, the typical network
requirements are bandwidth 40Mbps, 20ms for motion-to-photon latency,
packet loss rate is 2.4E-5; the typical computing requirements are 8K
H.265 real-time decoding, 2K H.264 real-time encoding. We can
further divide the 20ms latency budget into (i) sensor sampling
delay, (ii) image/frame rendering delay, (iii) display refresh delay,
and (iv) network delay. With upcoming high display refresh rate
(e.g., 144Hz) and GPU resources being used for frame rendering, we
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can expect an upper bound of roughly 5ms for the round trip latency
in these scenarios, , which is close to the frame rendering computing
delay.
Furthermore, techniques may be employed that divide the overall
rendering into base assets that are common across a number of clients
participating in the service, while the client-specific input data is
being utilized to render additional assets. When being delivered to
the client, those two assets are being combined into the overall
content being consumed by the client. The requirements for sending
the client input data as well as the requests for the base assets may
be different in terms of which service instances may serve the
request, where base assets may be served from any nearby service
instance (since those base assets may be served without requiring
cross-request state being maintained), while the client-specific
input data is being processed by a stateful service instance that
changes, if at all, only slowly over time due to the stickiness of
the service that is being created by the client-specific data. Other
splits of rendering and input tasks can be found in [TR22.874] for
further reading.
When it comes to the service instances themselves, those may be
instantiated on-demand, e.g., driven by network or client demand
metrics, while resources may also be released, e.g., after an idle
timeout, to free up resources for other services. Depending on the
utilized node technologies, the lifetime of such "function as a
service" may range from many minutes down to millisecond scale.
Therefore computing resources across participating edges exhibit a
distributed (in terms of locations) as well as dynamic (in terms of
resource availability) nature. In order to achieve a satisfying
service quality to end users, a service request will need to be sent
to and served by an edge with sufficient computing resource and a
good network path.
3.2. Connected Car
In auxiliary driving scenarios, to help overcome the non-line-of-
sight problem due to blind spot or obstacles, the edge node can
collect comprehensive road and traffic information around the vehicle
location and perform data processing, and then vehicles with high
security risk can be warned accordingly, improving driving safety in
complicated road conditions, like at intersections. This scenario is
also called "Electronic Horizon", as explained in[HORITA].
For instance, video image information captured by, e.g., an in-car,
camera is transmitted to the nearest edge node for processing. The
notion of sending the request to the "nearest" edge node is important
for being able to collate the video information of "nearby" cars,
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using, for instance, relative location information. Furthermore,
data privacy may lead to the requirement to process the data as close
to the source as possible to limit data spread across too many
network components in the network.
Nevertheless, load at specific "closest" nodes may greatly vary,
leading to the possibility for the closest edge node becoming
overloaded, leading to a higher response time and therefore a delay
in responding to the auxiliary driving request with the possibility
of traffic delays or even traffic accidents occurring as a result.
Hence, in such cases, delay-insensitive services such as in-vehicle
entertainment should be dispatched to other light loaded nodes
instead of local edge nodes, so that the delay-sensitive service is
preferentially processed locally to ensure the service availability
and user experience.
3.3. Digital Twin
A number of industry associations, such as the Industrial Digital
Twin Association or the Digital Twin Consortium
(https://www.digitaltwinconsortium.org/), have been founded to
advocate the concept of the Digital Twin (DT) for a number of use
case areas, such as smart cities, transportation, industrial control,
among others. The core concept of the DT is the "administrative
shell" [Industry4.0], which serves as a digital representation of the
information and technical functionality pertaining to the "assets"
(such as an industrial machinery, a transportation vehicle, an object
in a smart city or others) that is intended to be managed,
controlled, and actuated.
As an example for industrial control, the programmable logic
controller (PLC) may be virtualized and the functionality aggregated
across a number of physical assets into a single administrative shell
for the purpose of managing those assets. PLCs may be virtualized in
order to move the PLC capabilities from the physical assets to the
edge cloud. Several PLC instances may exist to enable load balancing
and fail-over capabilities, while also enabling physical mobility of
the asset and the connection to a suitable "nearby" PLC instance.
With this, traffic dynamicity may be similar to that observed in the
connected car scenario in the previous sub-section. Crucial here is
high availability and bounded latency since a failure of the
(overall) PLC functionality may lead to a production line stop, while
boundary violations of the latency may lead to loosing
synchronization with other processes and, ultimately, to production
faults, tool failures or similar.
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Particular attention in Digital Twin scenarios is given to the
problem of data storage. Here, decentralization, not only driven by
the scenario (such as outlined in the connected car scenario for
cases of localized reasoning over data originating from driving
vehicles) but also through proposed platform solutions, such as those
in [GAIA-X], plays an important role. With decentralization,
endpoint relations between client and (storage) service instances may
frequently change as a result.
Digital twin network(DTN)
[I-D.zhou-nmrg-digitaltwin-network-concepts] has also been proposed
recently. It is to introduce digital twin technology into the
network to build a network system with physical network entities and
virtual twins, which can be mapped in real time. The goal of digital
twin network will be applied not only to industrial Internet, but
also to operator network. When the network is large, it needs real-
time scheduling ability, more efficient and accurate data collection
and modeling, and promote the automation, intelligent operation and
maintenance and upgrading of the network.
4. Problems in Existing Solutions
There are a number of problems that may occur when realizing the use
cases in the previous section, this section suggests a classification
for those problems to aid the possible identification of solution
components for addressing them. There are no mature existing
solutions to solve those problems, most of the existing solutions
mentioned in the next could solve a similar problems more or less,
which would be analyzed as a starting point.
4.1. Dynamicity of Relations
The mapping from a service identifier to a specific service instance
that may execute the service for a client usually happens through
resolving the service identification into a specific IP address at
which the service instance is reachable.
Application layer solutions can be foreseen, using an application
server to resolve binding updates. While the viability of these
solutions will generally depend on the additional latency that is
being introduced by the resolution via said application server,
frequencies down to changing relations every few (or indeed EVERY)
service requests is seen as difficult to be viable.
Message brokers, however, could be used, dispatching incoming service
requests from clients to a suitable service instance, where such
dispatching could be controlled by service-specific metrics, such as
computing load. The introduction of such brokers, however, may lead
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to adverse effects on efficiency, specifically when it comes to
additional latencies due to the necessary communication with the
broker; we discuss this problem separately in the next subsection.
DNS[RFC1035] using 'early binding' to explicit bind from the service
identification to the network address before sending user data, so
the client creates an 'instance affinity' for the service identifier
that binds the client to the resolved service instance address, which
could also realize the load balancing. However, we can foresee
scenarios in which such 'instance affinity' may change very
frequently, possibly even at the level of each service request. DNS
are not designed for this level of dynamicity. Updates to the
mapping between service identifier to service instance address cannot
be pushed quickly enough into the DNS that takes several minutes
updates to propagate, and clients would need to frequently resolve
the original binding. If try to DNS to meet the this level of
dynamicity, frequent resolving of the same service name would likely
lead to an overload of the it. These issues are also discussed in
section 5.4 of [I-D.sarathchandra-coin-appcentres].
A solution that leaves the dispatching of service requests entirely
to the client may be possible to achieve the needed dynamicity, but
with the drawback that the individual destinations, i.e., the network
identifiers for each service instance, must be known to the client
for doing so. While this may be viable for certain applications, it
cannot generally scale with a large number of clients. Furthermore,
it may be undesirable for every client to know all available service
instance identifiers, e.g., for reasons of not wanting to expose this
information to clients from the perspective of the service provider
but also, again, for scalability reasons if the number of service
instances is very high.
Existing solutions exhibit limitations in providing dynamic 'instance
affinity', those limitations being inherently linked to the design
used for the mapping between the service identifier and the address
of the service instance. These limitations may lead to 'instance
affinity' to last many requests or even for the entire session
between the client and the service, which may be undesirable from the
service provider perspective in terms of best balance requests across
many service instances.
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4.2. Efficiency
The use of external resolvers, such as application layer repositories
in general, also affects the efficiency of the overall service
request. Additional signaling is required between client and
resolver, either through the application layer solution, which not
only leads to more messaging but also to increased latency for the
additional resolution. Accommodating smaller instance affinities
increases this additional signaling but also the latencies
experienced, overall impacting the efficiency of the overall service
transaction.
As mentioned in the previous subsection, broker systems could be used
to allow for dispatching service requests to different service
instances at high dynamicity. However, the usage of such broker
inevitably introduces 'path stretch' compared to the possible direct
path between client and service instance, increasing the overall flow
completion time.
Existing solutions may introduce additional latencies and
inefficiencies in packet transmission due to the need for additional
resolution steps or indirection points, and will lead to the
accuracy problems to select the appropriate edge.
4.3. Complexity and Accuracy
As we can see from the discussion on efficiency in the previous
subsection, the time when external resolvers collect the necessary
information and deal with it to select the edge nodes, the network
and computing resource status may changed already. So any additional
control decision on which service instance to choose for which
incoming service request requires careful planning to keep potential
inefficiencies, caused by additional latencies and path stretch, at a
minimum. Additional control plane elements, such as brokers, are
usually neither well nor optimally placed in relation to the data
path that the service request will ultimately traverse.
Existing solutions require careful planning for the placement of
necessary control plane functions in relation to the resulting data
plane traffic to improve the accuracy; a problem often intractable in
scenarios of varying service demand.
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4.4. Metric Exposure and Use
Some systems may use the geographical location, as deduced from IP
prefix, to pick the closest edge. The issue here may be that edges
may not be far apart in edge computing deployments, while it may also
be hard to deduce geo-location from IP addresses. Furthermore, the
geo-location may not be the key distinguishing metric to be
considered, particularly if geographic co-location does not
necessarily mean network topology co-location. Also, "closer
geographically" does not consider the computing load of possible
closer yet more loaded nodes, consequently leading to possibly worse
performance for the end user.
Solutions may also perform 'health checks' on an infrequent base
(>1s) to reflect the service node status and switch in fail-over
situations. Health checks, however, inadequately reflect an overall
computing status of a service instance. It may therefore not reflect
at all the decision basis a suitable service instance, e.g., based on
the number of ongoing sessions as an indicator of load. Infrequent
checks may also be too coarse in granularity, e.g., for supporting
mobility-induced dynamics such as the connected car scenario of
Section 3.2.
Service brokers may use richer computing metrics (such as load) but
may lack the necessary network metrics.
Existing solutions lack the necessary information to make the right
decision on the selection of the suitable service instance due to the
limited semantic or due to information not being exposed across
boundaries between, e.g., service and network provider.
4.5. Security
Resolution systems opens up two vectors of attack, namely attacking
the mapping system itself, as well as attacking the service instance
directly after having been resolved. The latter is particularly an
issue for a service provider who may deploy significant service
infrastructure since the resolved IP addresses will enable the client
to directly attack the service instance but also infer (over time)
information about available service instances in the service
infrastructure with the possibility of even wider and coordinated
Denial-of-Service (DoS) attacks.
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Broker systems may prevent this ability by relying on a pure service
identifier only for the client to broker communication, thereby
hiding the direct communication to the service instance albeit at the
expense of the additional latency and inefficiencies discussed in
Section 4.1 and 4.2. DoS attacks here would be entirely limited to
the broker system only since the service instance is hidden by the
broker.
Existing solutions may expose control as well as data plane to the
possibility of a distributed Denial-of-Service attack on the
resolution system as well as service instance. Localizing the attack
to the data plane ingress point would be desirable from the
perspective of securing service request routing, which is not
achieved by existing solutions.
4.6. Changes to Infrastructure
Dedicated resolution systems, such as the DNS or broker-based
systems, require appropriate investments into their deployment.
While the DNS is an inherent part of the Internet infrastructure, its
inability to deal with the dynamicity in service instance relations,
as discussed in Section 4.1, may either require significant changes
to the DNS or the establishment of a separate infrastructure to
support the needed dynamicity. In a manner, the efforts on Multi-
Access Edge Computing [MEC], are proposing such additional
infrastructure albeit not solely for solving the problem of suitably
dispatching service requests to service instances (or application
servers, as called in [MEC]).
Existing solutions may expose control as well as data plane to the
possibility of a distributed Denial-of-Service attack on the
resolution system as well as service instance. Localizing the attack
to the data plane ingress point would be desirable from the
perspective of securing service request routing, which is not
achieved by existing solutions.
5. Conclusion
This document presents use cases in which we observe the demand for
considering the dynamic nature of service requests in terms of
requirements on the resources fulfilling them in the form of service
instances. In addition, those very service instances may themselves
be dynamic in availability and status, e.g., in terms of load or
experienced latency.
As a consequence, the problem of satisfying service-specific metrics
to allow for selecting the most suitable service instance among the
pool of instances available to the service throughout the network is
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a challenge, with a number of observed problems in existing
solutions. The use cases as well as the categorization of the
observed problems may start the process of determining how they are
best satisfied within the IETF protocol suite or through suitable
extensions to that protocol suite.
6. Security Considerations
TBD
7. IANA Considerations
No IANA action is required so far.
8. Informative References
[RFC4786] Abley, J. and K. Lindqvist, "Operation of Anycast
Services", BCP 126, RFC 4786, DOI 10.17487/RFC4786,
December 2006, <https://www.rfc-editor.org/info/rfc4786>.
[RFC1035] Mockapetris, P., "Domain names - implementation and
specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,
November 1987, <https://www.rfc-editor.org/info/rfc1035>.
[I-D.zhou-nmrg-digitaltwin-network-concepts]
Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
Q., Boucadair, M., and C. Jacquenet, "Digital Twin
Network: Concepts and Reference Architecture", Work in
Progress, Internet-Draft, draft-zhou-nmrg-digitaltwin-
network-concepts-06, 2 December 2021,
<https://www.ietf.org/archive/id/draft-zhou-nmrg-
digitaltwin-network-concepts-06.txt>.
[I-D.sarathchandra-coin-appcentres]
Trossen, D., Sarathchandra, C., and M. Boniface, "In-
Network Computing for App-Centric Micro-Services", Work in
Progress, Internet-Draft, draft-sarathchandra-coin-
appcentres-04, 26 January 2021,
<https://www.ietf.org/archive/id/draft-sarathchandra-coin-
appcentres-04.txt>.
[TR22.874] 3GPP, "Study on traffic characteristics and performance
requirements for AI/ML model transfer in 5GS (Release
18)", 2021.
[HORITA] Horita, Y., "Extended electronic horizon for automated
driving", Proceedings of 14th International Conference on
ITS Telecommunications (ITST)", 2015.
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[Industry4.0]
Industry4.0, "Details of the Asset Administration Shell,
Part 1 & Part 2", 2020.
[GAIA-X] Gaia-X, ""GAIA-X: A Federated Data Infrastructure for
Europe"", 2021.
[MEC] ETSI, ""Multi-Access Edge Computing (MEC)"", 2021.
Acknowledgements
The author would like to thank Yizhou Li, Luigi IANNONE, Mohamed
Boucadair and Christian Jacquenet for their valuable suggestions to
this document.
Authors' Addresses
Peng Liu
China Mobile
Email: liupengyjy@chinamobile.com
Peter Willis
BT
Email: peter.j.willis@bt.com
Dirk Trossen
Huawei Technologies
Email: dirk.trossen@huawei.com
Cheng Li
Huawei Technologies
Email: c.l@huawei.com
Liu, et al. Expires 22 July 2022 [Page 14]