Dynamic-Anycast (Dyncast) Use Cases and Problem Statement
draft-liu-dyncast-ps-usecases-00
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| Authors | Peng Liu , Peter Willis , Dirk Trossen | ||
| Last updated | 2021-02-01 | ||
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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
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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material or to cite them other than as "work in progress."
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This Internet-Draft will expire on July 22, 2021.
Copyright Notice
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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
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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
Liu, et al. Expires August 1, 2021 [Page 14]