dyncast P. Liu
Internet-Draft China Mobile
Intended status: Informational P. Willis
Expires: August 15, 2021 BT
D. Trossen
Huawei
February 15, 2021
Dynamic-Anycast (Dyncast) Use Cases & Problem Statement
draft-liu-dyncast-ps-usecases-01
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.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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Drafts is at https://datatracker.ietf.org/drafts/current/.
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and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
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This Internet-Draft will expire on July 22, 2021.
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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) . . . 5
3.2. Connected Car . . . . . . . . . . . . . . . . . . . . . . 6
3.3. Digital Twin . . . . . . . . . . . . . . . . . . . . . . . 7
4. Problems in Existing Solutions . . . . . . . . . . . . . . . . 7
4.1. Dynamicity of Relations . . . . . . . . . . . . . . . . . 7
4.2. Efficiency . . . . . . . . . . . . . . . . . . . . . . . . 9
4.3. Complexity . . . . . . . . . . . . . . . . . . . . . . . . 9
4.4. Metric Exposure and Use . . . . . . . . . . . . . . . . . 10
4.5. Security . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.6. Changes to Infrastructure . . . . . . . . . . . . . . . . 11
5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 12
6. Security Considerations . . . . . . . . . . . . . . . . . . . 12
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
8. Informative References . . . . . . . . . . . . . . . . . . . . 12
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
Edge computing aims to provide better response times and transfer
rate, with respect to Cloud Computing, by moving the computing
towards the edge of the network. Edge computing can be built on
industrial PCs, embedded systems, gateways and others, all being
located close to the end user. There is an emerging requirement that
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multiple edge sites (called "edges" too in this document) are
deployed at different locations to provide the service. There are
millions of home gateways, thousands of base stations and hundreds of
central offices in a city that can serve as candidate edges for
hosting service nodes. Depending on the location of the edge and its
capacity, each edge has different computing resources to be used for
a service. At peak hour, computing resources attached to a client's
closest edge site may not be sufficient to handle all the incoming
service requests. Longer response times or even dropping of requests
can be experienced by users. Increasing the computing resources
hosted on each edge site to the potential maximum capacity is neither
feasible nor economical in many cases.
Some user devices are purely battery-driven. Offloading computation
intensive processing to the edge can save battery power. Moreover the
edge may use a data set (for the computation) that may not exist on
the user device because of the size of data pool or due to data
governance reasons.
At the same time, with new technologies such as serverless computing
and container based virtual functions, the service node at an edge
can be easily created and terminated in a sub-second scale, which in
turn changes the availability of a computing resources for a service
dramatically over time, therefore impacting the possibly "best"
decision on where to send a service request from a client.
DNS-based load balancing usually configures a domain in Domain Name
System (DNS) such that client requests to the domain are distributed
across a group of servers. It usually provides several IP addresses
for a domain name. Traditional techniques to manage the overall load
balancing process of clients issuing requests include choose-
the-closest or round-robin. Those solutions are relatively static,
which may cause an unbalanced distribution in terms of network load
and computational load.
There are some dynamic ways which attempt to distribute the request
to the server that best fits a service-specific metric, such as the
best available resources and minimal load. They usually require L4-L7
handling of the packet processing. It is not an efficient approach
for a large number of short connections. At the same time, such
approaches can often not retrieve the desired metric, such as the
network status, in real time. Therefore, the choice of the service
node is almost entirely determined by the computing status, rather
than the comprehensive consideration of both computing and network
metrics.
Distributing a service request to a specific service having multiple
instances attached to multiple edge computing sites, while taking
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into account computing as well as service-specific metrics in the
distribution decision, can be seen as a dynamic anycast (or "dyncast"
for short) problem of sending service requests, without prescribing
the use of a routing solution at this stage of the discussion.
As a problem statement, this draft describes usage scenarios as well
as key areas in which current solutions lead to problems that
ultimately affect the deployment or the performance of the edge
services. Those key areas target the identification of possible
solution components, while the overall purpose of this document is to
stimulate discussions on the emerging needs outlined in our use cases
and to start the process of determining how they are best satisfied
within the IETF protocol suite or through suitable extensions to that
protocol suite.
2. Definition of Terms
Service: A service represents a defined endpoint of functionality
encoded according to the specification for said service.
Service instance: One service can have several instances running on
different nodes. Service instance is a running environment (e.g.,
a node) that makes the functionality of a service available.
Service identifier: Used to uniquely identify a service, at the same
time identifying the whole set of service instances that each
represent the same service behaviour, no matter where those
service instances are running.
Anycast: An addressing and packet sending methodology that assign an
"anycast" identifier for one or more service instances to which
requests to an "anycast" identifier could be routed, following
the definition in [RFC4786] as anycast being "the practice of
making a particular Service Address available in multiple,
discrete, autonomous locations, such that datagrams sent are
routed to one of several available locations".
Dyncast: Dynamic Anycast, taking the dynamic nature of computing
resource metrics into account to steer an anycast-like decision
in sending an incoming service request.
3. Use Cases
This section presents several typical scenarios which require
multiple edge sites to interconnect and to co-ordinate at the network
layer to meet the service requirements and ensure user experience.
The scenarios here are exemplary only for the purpose of this
document and not comprehensive.
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3.1. Cloud Virtual Reality (VR) or Augmented Reality (AR)
Cloud VR/AR introduces the concept and technology of cloud computing
to the rendering of audiovisual assets in such applications. Here,
the edge cloud helps encode/decode and render content. The end device
usually only uploads posture or control information to the edge and
then VR/AR contents are rendered in the edge cloud. The video and
audio outputs generated from the edge cloud are encoded, compressed,
and transmitted back to the end device or further transmitted to
central data center via high bandwidth networks.
Edge sites may use CPU or GPU for encode/decode. GPU usually has
better performance but CPU is simpler and more straightforward to use
as well as possibly more widespread in deployment. Available
remaining resources determines if a service instance can be started.
The instance's CPU, GPU and memory utilization has a high impact on
the processing delay on encoding, decoding and rendering. At the same
time, the network path quality to the edge site is a key for user
experience of quality of audio/ video and input command response
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
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input data is being processed by a stateful service instance that
changes, if at all, only slowly over time due to the stickiness of
the service that is being created by the client-specific data. Other
splits of rendering and input tasks can be found in [TR22.874] for
further reading.
When it comes to the service instances themselves, those may be
instantiated on-demand, e.g., driven by network or client demand
metrics, while resources may also be released, e.g., after an idle
timeout, to free up resources for other services. Depending on the
utilized node technologies, the lifetime of such "function as a
service" may range from many minutes down to millisecond scale.
Therefore computing resources across participating edges exhibit a
distributed (in terms of locations) as well as dynamic (in terms of
resource availability) nature. In order to achieve a satisfying
service quality to end users, a service request will need to be sent
to and served by an edge with sufficient computing resource and a
good network path.
3.2. Connected Car
In auxiliary driving scenarios, to help overcome the non-line-of-
sight problem due to blind spot or obstacles, the edge node can
collect comprehensive road and traffic information around the vehicle
location and perform data processing, and then vehicles with high
security risk can be warned accordingly, improving driving safety in
complicated road conditions, like at intersections. This scenario is
also called "Electronic Horizon", as explained in [HORITA].
For instance, video image information captured by, e.g., an in-car,
camera is transmitted to the nearest edge node for processing. The
notion of sending the request to the "nearest" edge node is important
for being able to collate the video information of "nearby" cars,
using, for instance, relative location information. Furthermore, data
privacy may lead to the requirement to process the data as close to
the source as possible to limit data spread across too many network
components in the network.
Nevertheless, load at specific "closest" nodes may greatly vary,
leading to the possibility for the closest edge node becoming
overloaded, leading to a higher response time and therefore a delay
in responding to the auxiliary driving request with the possibility
of traffic delays or even traffic accidents occurring as a result.
Hence, in such cases, delay-insensitive services such as in-vehicle
entertainment should be dispatched to other light loaded nodes
instead of local edge nodes, so that the delay-sensitive service is
preferentially processed locally to ensure the service availability
and user experience.
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3.3. Digital Twin
A number of industry associations, such as the Industrial Digital
Twin Association or the Digital Twin Consortium
(https://www.digitaltwinconsortium.org/), have been founded to
advocate the concept of the Digital Twin (DT) for a number of use
case areas, such as smart cities, transportation, industrial control,
among others. The core concept of the DT is the "administrative
shell" [Industry4.0], which serves as a digital representation of the
information and technical functionality pertaining to the "assets"
(such as an industrial machinery, a transportation vehicle, an object
in a smart city or others) that is intended to be managed,
controlled, and actuated.
As an example for industrial control, the programmable logic
controller (PLC) may be virtualized and the functionality aggregated
across a number of physical assets into a single administrative shell
for the purpose of managing those assets. PLCs may be virtualized in
order to move the PLC capabilities from the physical assets to the
edge cloud. Several PLC instances may exist to enable load balancing
and fail-over capabilities, while also enabling physical mobility of
the asset and the connection to a suitable "nearby" PLC instance.
With this, traffic dynamicity may be similar to that observed in the
connected car scenario in the previous sub-section. Crucial here is
high availability and bounded latency since a failure of the
(overall) PLC functionality may lead to a production line stop, while
boundary violations of the latency may lead to loosing
synchronization with other processes and, ultimately, to production
faults, tool failures or similar.
Particular attention in Digital Twin scenarios is given to the
problem of data storage. Here, decentralization, not only driven by
the scenario (such as outlined in the connected car scenario for
cases of localized reasoning over data originating from driving
vehicles) but also through proposed platform solutions, such as those
in [GAIA-X], plays an important role. With decentralization, endpoint
relations between client and (storage) service instances may
frequently change as a result.
4. Problems in Existing Solutions
There are a number of problems that may occur when realizing the use
cases in the previous section. This section suggests a classification
for those problems to aid the possible identification of solution
components for addressing them.
4.1. Dynamicity of Relations
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The mapping from a service identifier to a specific service instance
that may execute the service for a client usually happens through
resolving the service identification into a specific IP address at
which the service instance is reachable. This is the case, for
instance, when utilizing DNS [RFC1035] for this step, as utilized in
most content delivery networks or in DNS-based load balancing
solutions. This is called 'early binding' because an explicit
binding from the service identification to the network address has to
be performed before sending user data. Through this resolution, the
client creates an 'instance affinity' for the service identifier that
binds the client to the resolved service instance address.
We can foresee scenarios in which such 'instance affinity' may change
very frequently, possibly even at the level of each service request.
Systems such as the DNS are not designed for this level of
dynamicity. Firstly, updates to the mapping between service
identifier to service instance address cannot be pushed quickly
enough into the DNS to be available fast enough since it usually
takes several minutes for DNS updates to propagate. Secondly, clients
would need to frequently resolve the original binding, while also
actively flushing the local DNS cache since most client
implementations would provide cached results of previously resolved
requests. Regardless of those aspects, frequent resolving of the same
service name would likely lead to an overload of the DNS,
particularly when scaling the number of clients and service instance
relations. These issues are also discussed in section 5.4 of [I-
D.sarathchandra-coin-appcentres], outlining the significant
challenges for the flexible re-routing to appropriate service
instances out of an available pool when utilizing DNS for this
purpose.
Application layer solutions can also be foreseen, which do not rely
on the DNS but instead use an application server to resolve binding
updates. While the viability of these solutions will generally depend
on the additional latency that is being introduced by the resolution
via said application server, frequencies down to changing relations
every few (or indeed EVERY) service requests is seen as difficult to
be viable.
Message brokers, however, could be used, dispatching incoming service
requests from clients to a suitable service instance, where such
dispatching could be controlled by service-specific metrics, such as
computing load. The introduction of such brokers, however, may lead
to adverse effects on efficiency, specifically when it comes to
additional latencies due to the necessary communication with the
broker; we discuss this problem separately in the next subsection.
A solution that leaves the dispatching of service requests entirely
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to the client may be possible to achieve the needed dynamicity, but
with the drawback that the individual destinations, i.e., the network
identifiers for each service instance, must be known to the client
for doing so. While this may be viable for certain applications, it
cannot generally scale with a large number of clients. Furthermore,
it may be undesirable for every client to know all available service
instance identifiers, e.g., for reasons of not wanting to expose this
information to clients from the perspective of the service provider
but also, again, for scalability reasons if the number of service
instances is very high.
Existing solutions exhibit limitations in providing dynamic
'instance affinity', those limitations being inherently linked to
the design used for the mapping between the service identifier and
the address of the service instance. These limitations may lead to
'instance affinity' to last many requests or even for the entire
session between the client and the service, which may be
undesirable from the service provider perspective in terms of best
balance requests across many service instances.
4.2. Efficiency
The use of external resolvers, such as the DNS or application layer
repositories in general, also affects the efficiency of the overall
service request. Additional signaling is required between client and
resolver, either through the DNS or some application layer solution,
which not only leads to more messaging but also to increased latency
for the additional resolution. Accommodating smaller instance
affinities increases this additional signaling but also the latencies
experienced, overall impacting the efficiency of the overall service
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.
4.3. Complexity
As we can see from the discussion on efficiency in the previous
subsection, any additional control decision on which service instance
to choose for which incoming service request requires careful
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planning to keep potential inefficiencies, caused by additional
latencies and path stretch, at a minimum. Additional control plane
elements, such as DNS resolvers or brokers, are usually neither well
nor optimally placed in relation to the data path that the service
request will ultimately traverse. Solutions like EIGRP [RFC7868] are
realized at the data plane and therefore remove those inefficiencies
but suffer (as discussed in Section 4.4) from other limitations.
Existing solutions require careful planning for the placement of
necessary control plane functions in relation to the resulting
data plane traffic; a problem often intractable in scenarios of
varying service demand.
4.4. Metric Exposure and Use
Solutions such as EIGRP [RFC7868] do allow for a number of metrics
being used for a routing decision although EIGRP has no notion of
computing load as a metric since computing information is not being
exposed to the routing layer realized by the network provider. In
addition, EIGRP does not enforce instance affinity, which may lead to
problems in our use cases of Section 3 in service requests may be
sent mid-request to other service instances.
Other systems may use the geographical location, as deduced from IP
prefix, to pick the closest edge. The issue here may be that edges
may not be far apart in edge computing deployments, while it may also
be hard to deduce geo-location from IP addresses. Furthermore, the
geo-location may not be the key distinguishing metric to be
considered, particularly if geographic co-location does not
necessarily mean network topology co-location. Also, "closer
geographically" does not consider the computing load of possible
closer yet more loaded nodes, consequently leading to possibly worse
performance for the end user.
Solutions may also perform 'health checks' on an infrequent base
(>1s) to reflect the service node status and switch in fail-over
situations. Health checks, however, inadequately reflect an overall
computing status of a service instance. It may therefore not reflect
at all the decision basis a suitable service instance, e.g., based on
the number of ongoing sessions as an indicator of load. Infrequent
checks may also be too coarse in granularity, e.g., for supporting
mobility-induced dynamics such as the connected car scenario of
Section 3.2.
Resolution systems such as the DNS do often not allow for
constraining requests to resolve a service name at all, while service
brokers may use richer computing metrics (such as load) but may lack
the necessary network metrics.
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Existing solutions lack the necessary information to make the
right decision on the selection of the suitable service instance
due to the limited semantic or due to information not being
exposed across boundaries between, e.g., service and network
provider.
4.5. Security
Resolution systems, such as the DNS, open up two vectors of attack,
namely attacking the mapping system itself, i.e., the DNS, as well as
attacking the service instance directly after having been resolved.
The latter is particularly an issue for a service provider who may
deploy significant service infrastructure since the resolved IP
addresses will enable the client to directly attack the service
instance but also infer (over time) information about available
service instances in the service infrastructure with the possibility
of even wider and coordinated Denial-of-Service (DoS) attacks.
Broker systems may prevent this ability by relying on a pure service
identifier only for the client to broker communication, thereby
hiding the direct communication to the service instance albeit at the
expense of the additional latency and inefficiencies discussed in
Section 4.1 and 4.2. DoS attacks here would be entirely limited to
the broker system only since the service instance is hidden by the
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]).
The support for network layer solutions such as EIGRP [RFC7868]
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requires suitable router upgrades, while still lacking a number of
aspects important for the realization of the use cases in Section 3,
including the support for instance affinity in the routing decision.
Existing solutions require changes to either service and/or
network infrastructure, with no solution limiting the necessary
changes to the very ingress point of the network where the demand
for more flexible service request routing initiates from in the
form of the client initiating the service request.
5. Conclusions
This document presents use cases in which we observe the demand for
considering the dynamic nature of service requests in terms of
requirements on the resources fulfilling them in the form of service
instances. In addition, those very service instances may themselves
be dynamic in availability and status, e.g., in terms of load or
experienced latency.
As a consequence, the problem of satisfying service-specific metrics
to allow for selecting the most suitable service instance among the
pool of instances available to the service throughout the network is
a challenge, with a number of observed problems in existing
solutions. The use cases as well as the categorization of the
observed problems may start the process of determining how they are
best satisfied within the IETF protocol suite or through suitable
extensions to that protocol suite.
6. Security Considerations
TBD
7. IANA Considerations
No IANA action is required so far.
8. Informative References
[RFC7868] D. Davage et al. , "Cisco's Enhanced Interior Gateway
Routing Protocol (EIGRP)", RFC 7868, May 2016,
https://tools.ietf.org/html/rfc7868
[RFC4786] J. Abley, K. Lingqvist, "Operation of Anycast Services",
RFC4786, December 2006, https://tools.ietf.org/html/rfc4786
[RFC1035] P. Mockapetris, "DOMAIN NAMES - IMPLEMENTATION AND
SPECIFICATION", RFC1035, November 1987,
https://tools.ietf.org/html/rfc1035
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[I-D.sarathchandra-coin-appcentres] Trossen, D., Sarathchandra, C.,
and M. Boniface, "In-Network Computing for App-Centric Micro-
Services", draft-sarathchandra-coin-appcentres-03 (work in
progress), October 2020.
[TR22.874] 3GPP, "Study on traffic characteristics and performance
requirements for AI/ML model transfer in 5GS (Release 18)", TR
22.874 V0.2.0, November 2020
[Industry4.0] Industry4.0, "Details of the Asset Administration
Shell, Part 1 & Part 2", November 2020, https://www.plattform-
i40.de/PI40/Redaktion/EN/Standardartikel/specification-
administrationshell.html.
[GAIA-X] Gaia-X, "GAIA-X: A Federated Data Infrastructure for
Europe", accessed January 2021, https://www.data-
infrastructure.eu/GAIAX/Navigation/EN/Home/home.html
[HORITA] Y. Horita et al., "Extended electronic horizon for automated
driving", Proceedings of 14th International Conference on ITS
Telecommunications (ITST), 2015
[MEC] ETSI, "Multi-Access Edge Computing (MEC)", accessed January
2021, https://www.etsi.org/technologies/multi-access-edge-
computing
Acknowledgements
The author would like to thank Yizhou Li, Luigi IANNONE and Geng
Liang for their valuable suggestions to this document.
Authors' Addresses
Peng Liu
China Mobile
Email: liupengyjy@chinamobile.com
Peter Willis
BT
Email: peter.j.willis@bt.com
Dirk Trossen
Huawei
Email: dirk.trossen@huawei.com
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