Internet DRAFT - draft-du-cats-computing-modeling-description
draft-du-cats-computing-modeling-description
CATS Z. Du
Internet-Draft Y. Fu
Intended status: Informational China Mobile
Expires: 7 September 2023 C. Li
Huawei Technologies
G. Huang
ZTE
6 March 2023
Computing Information Description in Computing-Aware Traffic Steering
draft-du-cats-computing-modeling-description-00
Abstract
This document describes the considerations and the potential
architecture of the computing information that needs to be notified
in the network in Computing-Aware Traffic Steering (CATS).
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 3
3. Problem Statement in Computing Resource Modeling . . . . . . 4
3.1. Heterogeneous Chips and Different Computing Types . . . . 4
3.2. Multi-dimensional Modeling . . . . . . . . . . . . . . . 4
3.3. Support to be used for Further Representation . . . . . . 4
4. Usage of Computing Resource Modeling of CATS . . . . . . . . 5
4.1. Modeling Based on CATS-defined Format . . . . . . . . . . 5
4.2. Modeling Based on Application-defined Method . . . . . . 6
5. Computing Resource Modeling . . . . . . . . . . . . . . . . . 6
5.1. Consideration of Using in CATS . . . . . . . . . . . . . 7
6. Network Resource Modeling . . . . . . . . . . . . . . . . . . 8
6.1. Consideration of Using in CATS . . . . . . . . . . . . . 8
7. Application Demands Modeling . . . . . . . . . . . . . . . . 9
7.1. Consideration of Using in CATS . . . . . . . . . . . . . 9
8. Security Considerations . . . . . . . . . . . . . . . . . . . 9
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
11. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 9
12. Informative References . . . . . . . . . . . . . . . . . . . 10
Appendix A. Related Works on Computing Capacity Modeling . . . . 11
Appendix B. Architecture of Computing Modeling . . . . . . . . . 12
B.1. Computing Capacity . . . . . . . . . . . . . . . . . . . 13
B.1.1. Types of Chips . . . . . . . . . . . . . . . . . . . 13
B.1.2. Type of Computing . . . . . . . . . . . . . . . . . . 14
B.1.3. Relation of Computing Types and Chips . . . . . . . . 15
B.2. Communication, Cache and Storage Capacity . . . . . . . . 15
B.3. Comprehensive Computing Capability Evaluation . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
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1. Introduction
Computing-Aware Traffic Steering (CATS) is proposed to support
steering the traffic among different edge sites according to both the
real-time network and computing resource status as mentioned in
[I-D.yao-cats-ps-usecases] and [I-D.yao-cats-gap-reqs]. It requires
the network to be aware of computing resource information and select
a service instance based on the joint metric of computing and
networking.
In order to generate steering strategies, the modeling of computing
capacity is required. Different from the network, computing capacity
is more complex to be measured. For instance, it is hard to predict
how long will be used to process a specific computing task based on
the different computing resource. It is hard to calculate and will
be influenced by the whole internal environments of computing nodes.
But there are some indicators has been used to describe the computing
capacity of hardware and computing service, as mentioned in
Appendix A.
Based on the related works and the demand of CATS traffic steering,
this document analyzes the types of computing resources and tasks,
providing the factors to be considered when modeling and evaluating
the computing resource capacity. The detailed modeling job of the
computing resource is not the object of this document.
2. Definition of Terms
This document makes use of the following terms:
Computing-Aware Traffic Steering (CATS): Aiming at computing and
network resource optimization by steering traffic to appropriate
computing resources considering not only routing metric but also
computing resource metric.
Service: A monolithic functionality that is provided by an endpoint
according to the specification for said service. A composite
service can be built by orchestrating monolithic services.
Service instance: Running environment (e.g., a node) that makes the
functionality of a service available. One service can have several
instances running at different network locations.
Service identifier: Used to uniquely identify a service, at the same
time identifying the whole set of service instances that each
represents the same service behavior, no matter where those service
instances are running.
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Service transaction: Has one or more service request that has
several flows which require the affinity because of the transaction
related state.
Computing Capacity: The ability of nodes with computing resource
achieve specific result output through data processing, including
but not limited to computing, communication, memory and storage
capacity.
3. Problem Statement in Computing Resource Modeling
3.1. Heterogeneous Chips and Different Computing Types
Different heterogeneous computing resources have different
characteristics. For example, CPUs usually deal with pervasive
computing and are most widely used. GPUs usually handle parallel
computing, such as rendering of display tasks, and are widely used in
artificial intelligence and neural network algorithm computing. FPGA
and ASIC are usually used to handle customized computing. At the
same time, different computing tasks need to call different
calculation types, such as integer calculation, floating-point
calculation, hash calculation, etc.
3.2. Multi-dimensional Modeling
The network and computing have multi-dimensional and hierarchical
resources, such as cache, storage, communication, etc., and these
dimensions will affect each other and further affect the overall
level of computing capacity. Other factors besides the computing
itself need to be considered in modeling. At the same time, the form
of computing resources is also hierarchical, such as computing type,
chip type, hardware type, and converging with the network. For
different computing forms, such as gateway, all-in-one machine, edge
cloud and central cloud, the computing capacity, and types provided
are also different. It is necessary to comprehensively consider
multi-dimensional and multi-modal resources, and provide multi-level
modeling according to application demands.
3.3. Support to be used for Further Representation
Modeling itself provides a general method to evaluate the capacities
of computing resource. For CATS, modeling-based computing resource
representation is the basis for subsequent traffic steering. In
addition, for different applications, it may be optimized based on
general modeling methods to establish a set of models that conform to
their own characteristics, so as to generate corresponding
representation methods. Moreover, in order to use computing resource
status more efficiently and protect privacy, modeling for the further
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representation of resource information needs to support the necessary
simplification and obfuscation.
4. Usage of Computing Resource Modeling of CATS
4.1. Modeling Based on CATS-defined Format
Figure 1 shows the case of modeling based on CATS-defined Format.
CATS provides the modeling format to the computing domain to evaluate
the computing resource capacity of computing domain and then get the
result based on the unified interface, which will define the
properties should be notified to CATS. Then CATS could select the
specific service instance based on the computing resource and network
resource status.
In this way, the CATS domain and computing domain has the relative
loose boundary based on the situation that the CATS service and
computing resource belongs to the same provider, CATS could be aware
of computing resource more or less, depending on the privacy
preserving demand of the computing domain at the same time. The
exposed computing capacity includes the static information of
computing node category/level and the dynamic capabilities
information of computing node.
Based on the static information, some visualization functions can be
implemented on the management plane to know the global view of
computing resources, which could also help the deployment of
applications considering the overall distributed status of computing
and network resource. Based on the dynamic information, CATS could
steer category-based applications traffic based on the unified
modeling format and interface.
|
CATS Domain | Computing Domain
+--------+ ---------------------->-------------------> +-------------+
|visuali-| Modeling Format | Computing |
|zation | | | |
+--------+ <--------------------<--------------------- | Resource |
|Traffic | Static level/category of computing node | |
|Steering| | | Modeling |
+--------+ <--------------------<--------------------- +-------------+
Dynamic capability of computing node
|
|
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Figure 1: Modeling Based on CATS-defined Format
4.2. Modeling Based on Application-defined Method
Figure 2 shows the case of modeling based on application-defined
method. Computing resource of the specific application evaluates its
computing capacity by itself, and then notifies the result which
might be the index of real time computing level to CATS. Then CATS
selects the specific service instance based on the computing index.
In this way, the CATS domain and computing domain has the strict
boundary based on the situation that the CATS service and computing
resource belongs to the different providers. CATS is just aware of
the index of computing resource which is defined by application,
don't know the real status of computing domain, and the traffic
steering right is potentially controlled under application itself.
If CATS is authorized by application, it could steer traffic based on
network status at the same time.
| |
| |
CATS Domain | | Computing Domain
| |
| | +-------------+
+--------+ | | | Computing |
|Traffic | | | | |
| | <---------------------<---------- ---------- | Resource |
|Steering| dynamic index of computing capacity level | |
+--------+ | | | Modeling |
| | +-------------+
| |
| |
| |
| |
Figure 2: Modeling Based on Application-defined Method
5. Computing Resource Modeling
To support a computing service, we need to evaluate the comprehensive
service performance in a service point, which is influenced by the
coordination of chip, storage, network, platform software, etc. It
is to say that the service support capabilities are influenced by
multidimensional factors. Therefore, in the modeling of the
computing metric, we can provide not only the specification computing
values provided by the manufacturer, such as FLOPS, but also some
integrated index values that can comprehensively reflect the service
support capabilities.
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We can build up a computing resource modeling system which contains
three levels of indicators, and the architecture can refer to
Appendix B.
The first level is about the heterogeneous hardware computing
capability. The indexes of this level can be the performance
parameters provided by the manufacturer, such as CPU model, main
frequency, number of cores, GPU model, single-precision floating-
point performance, etc. Meanwhile, the indexes can also be the test
values of commonly used benchmark programs.
The second-level indexes are abstracted from the first-level indexes,
which are mainly used for the comprehensive evaluation of node's
computing capability. They can provide the ability of a certain
aspect of the node, such as in the aspect of computing,
communication, cache, and storage, or a general comprehensive service
ability of the node.
Level 3 indexes are related to the services deployed on the nodes.
They mainly provide service-related evaluation parameters, such as
the actual processing throughput that nodes can provide for a
specific computing service. It can also be a test value, but it is
generated by running the real service.
5.1. Consideration of Using in CATS
It is assumed that the same service can be provided in multiple
places in the CATS. In the different service points, it is common
that they have different kinds of computing resources, and different
utilization rate of the computing resources.
In the CATS, the decision point, which should be a node in the
network, should be aware of the network status and the computing
status, and accordingly choose a proper service point for the client.
In fact, the decision would influenced more by the dynamic indexes.
An example of the decision process is described below.
Firstly, the decision point needs to make sure the candidate service
points can still access a new session. If a service point claims
that it is busy, no packet of new clients should be steered to it.
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Secondly, the decision point can select a service point with a higher
comprehensive performance evaluation value for the service. If Level
3 indexes for the service are available in the decision point, the
decision point can use it directly because it is most
straightforward. If not, for example the service is not tested in
the service point yet, Level 2 indicators can be used instead with
configurable weight values for the four aspects as mentioned in
Appendix B.
In this example, the index in the first step is dynamic, and is
related to the service status. The index in the second step is
relatively static, and is related to computing efficiency for the
service.
For a specific service, more indicators can still be provided. It is
to say that the computing information could be customized for
different services. Additionally, besides the computing information,
the energy consumption index can also be included if it is considered
necessary when making a decision.
Therefore, in this example, CATS needs two indexes at least, one for
the service status, and another for service ability. Optionally,
other information can also be provided if it is subscribed by the
decision point. The detailed decision process in the decision point
is out of scope of this document.
6. Network Resource Modeling
The modeling of the network resource is optional, which depends on
how to select the service instance and network path. For some
applications which care both network and computing resource, the CATS
service provider also need to consider the modeling of network and
computing together.
The network structure can be represented as graphs, where the nodes
represent the network devices and the edges represent the network
path. It should evaluate the single node, the network links and the
E2E performance.
6.1. Consideration of Using in CATS
When to consider both the computing and network status at the same
time, the comprehensive modeling of computing and network might be
used. For example, to measure all the resource in a unified
dimension, such as latency, reliability, etc.
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If there is no strict demand of consider them at same time, for
instance, consider computing status first and then network status.
CATS could select the service instance at first, then to mark
identifier for network path selection of network itself. In this
situation, the network modeling is not that needed. Existing
mechanisms on the control plane or the management plane in the
network can be used to obtain the network metrics.
7. Application Demands Modeling
The application is usually composed of several sub service that
complete different functions, and the service is usually composed of
several sub transactions, which would be the smallest schedulable
unit.
The application always has its own demands for network and computing
resource, for instance we can see the HD video always requires the
high bandwidth and the PC game always requires the better GPU and
memory.
7.1. Consideration of Using in CATS
The modeling of the application demand is optional, which depends on
whether the application could tell the demands to the network, or
what it could tell. Once the CATS knows the application's demand,
there should be a mapping between application demand and the modeling
of the computing and/or network resource.
8. Security Considerations
TBD.
9. IANA Considerations
TBD.
10. Acknowledgements
The author would like to thank Thomas Fossati, Dirk Trossen, Linda
Dunbar for their valuable suggestions to this document.
11. Contributors
The following people have substantially contributed to this document:
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Jing Wang
China Mobile
wangjingjc@chinamobile.com
Peng Liu
China Mobile
liupengyjy@chinamobile.com
Wenjing Li
Beijing University of Posts and Telecommunications
wjli@bupt.edu.cn
Lanlan Rui
Beijing University of Posts and Telecommunications
llrui@bupt.edu.cn
12. Informative References
[I-D.yao-cats-ps-usecases]
Yao, K., Eardley, P., Trossen, D., Boucadair, M.,
Contreras, L. M., Li, C., Li, Y., and P. Liu, "Computing-
Aware Traffic Steering (CATS) Problem Statement and Use
Cases", Work in Progress, Internet-Draft, draft-yao-cats-
ps-usecases-00, 3 March 2023,
<https://datatracker.ietf.org/doc/html/draft-yao-cats-ps-
usecases-00>.
[I-D.yao-cats-gap-reqs]
Yao, K., Jiang, T., Eardley, P., Trossen, D., Li, C., and
D. Huang, "Computing-Aware Traffic Steering (CATS) Gap
Analysis and Requirements", Work in Progress, Internet-
Draft, draft-yao-cats-gap-reqs-00, 3 March 2023,
<https://datatracker.ietf.org/doc/html/draft-yao-cats-gap-
reqs-00>.
[One-api] One-api, "http://www.oneapi.net.cn/", 2020.
[Amazon] Amaozn,
"https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-
scaling-target-tracking.html#available-metrics", 2022.
[Aliyun] Aliyun, "https://help.aliyun.com/?spm=a2c4g.11186623.6.538
.34063af89EIb5v", 2022.
[Tencent-cloud]
Tencent-cloud, "https://buy.cloud.tencent.com/pricing",
2022.
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[cloud-network-edge]
cloud-network-edge, "A new edge computing scheme based on
cloud, network and edge fusion", 2020.
[heterogeneous-multicore-architectures]
access, I., "Towards energy-efficient heterogeneous
multicore architectures for edge computing", 2019.
[ARM-based]
Guide, S., "A heterogeneous CPU-GPU cluster scheduling
model based on ARM", 2017.
Appendix A. Related Works on Computing Capacity Modeling
Some related work has been proposed to measurement and evaluate the
computing capacity, which could be the basis of computing capacity
modeling.
[cloud-network-edge] proposed to allocate and adjust corresponding
resources to users according to the demands of computing, storage and
network resources.
[heterogeneous-multicore-architectures] proposed to design
heterogeneous multi-core architectures according to different
customization, such as CPU microprocessors with ultra-low power
consumption and high code density, low power microprocessor with FPU,
and a high-performance application processor with FPU and MMU support
based on a completely unordered multi problem architecture.
[ARM-based] proposed the cluster scheduling model that is combined
with GPU virtualization and designed a hierarchical cluster resource
management framework, which can make the heterogeneous CPU-GPU
cluster be effectively used.
The hardware cloud service providers have also disclosed their
parameter indicators for computing services:
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[One-api] provides a collection of programming languages and cross
architecture libraries across different architectures, to be
compatible with heterogeneous computing resources, including CPU,
GPU, FPGA, and others. [Amazon] uses the computing resource
parameters when evaluating the performance, including the average CPU
utilization, average number of bytes received and sent out, and
average application load balancer. Alibaba cloud [Aliyun] gives the
indicators including vcpu, memory, local storage, network basic and
burst bandwidth capacity, network receiving and contracting capacity,
etc., when providing cloud servers service. [Tencent-cloud] uses
vcpu, memory (GB), network receiving and sending (PPS), number of
queues, intranet bandwidth capacity (Gbps), dominant frequency, etc.
Appendix B. Architecture of Computing Modeling
This Appendix describes the potential architecture of computing
resource modeling, regardless of any ways of the further usage of
traffic steering of CATS, neither of the usage ways described in
Section 4.
According to the computing indicators and related work described in
Section 2, computing capacity includes the types of computing
resources and tasks, and also need to consider multi-dimensional
capabilities such as communication, memory, and storage. Because
every factor will affect each others. For instance, with the rapid
growth of modern computer CPU performance, the communication
bottleneck between CPU and cache has become increasingly prominent.
Moreover, the storage capacity greatly affects the processing speed
of a computer. So the architecture of computing capacity modeling
could be seen in Figure 3.
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+-------+ +-------+
+--| CPU | +---| GPU |
+-------------+ | +-------+ | +-------+
| Chips |--+-------------+
+--| Category | | +-------+ | +-------+
| +-------------+ +--| FPGA | +---| ASIC |
+-------------+ | +-------+ +-------+
| Computing |--+
+--| Capacity |--+ +----------------------+
| +-------------+ | +--| intCalculationRate |
| +-------------+ | +-------------+ | +----------------------+
+--|Communication| +--| Computing | | +----------------------+
+-------------+ | | Capacity | | Types |--+--| floatCalculationRate |
| Computing | | +-------------+ +-------------+ | +----------------------+
| Resource |-+ +-------------+ | +----------------------+
| Modeling | | | Cache | +--| hashCalculationRate |
+-------------+ +--| Capacity | +----------------------+
| +-------------+
| +-------------+
+--| Storage |
| Capacity |
+-------------+
Figure 3: Referecen Architecture of Computing Modeling Format
B.1. Computing Capacity
The computing capacity includes the chips category and computing
types. Common chip types include CPU, GPU, FPGA and ASIC. CPU and
GPU belong to von Neumann structure, with instruction decoding and
execution and shared memory. According to the different
characteristics and requirements of computing programs, the computing
performance can be divided into integer computing performance,
floating-point computing performance and hash computing performance.
B.1.1. Types of Chips
CPU (Central Processing Unit) is a general-purpose processor needs to
be able to handle comprehensive and complex tasks, as well as the
synchronization and coordination between tasks. Therefore, a lot of
space is required on the chip to perform branch prediction and
optimization and save various states to reduce the delay during task
switching. This also makes it more suitable for logic control,
serial operation and universal type data operation.
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GPU (Graphics Processing Unit) has a large-scale parallel computing
framework composed of thousands of smaller and more efficient Alu
cores. Most transistors are mainly used to build control circuits
and caches, and the control circuits are relatively simple.
FPGA (Field Programmable Gate Array) is essentially an architecture
without instructions and shared memory, which is more efficient than
GPU and CPU. The main advantage of FPGA in data processing tasks is
its stability and extremely low latency, which is suitable for
streaming computing intensive tasks and communication intensive
tasks.
ASIC (Application Specific Integrated Circuit) is a special
integrated circuit, and its performance is actually better than FPGA.
However, for customized customers, its cost is much higher than FPGA.
On this basis, according to different computing task requirements,
chip manufacturers have also developed various "xpus", including APU
(Accelerated Processing Unit), DPU (Deep-learning Processing Unit),
TPU (Tensor Processing Unit), NPU (Neural-network Processing Unit)
and BPU (Brain Processing Unit), which are made based on the CPU,
GPU, FPGA and ASIC.
B.1.2. Type of Computing
At present, the computing type in computer mainly includes integer
calculation, floating-point calculation, and hash calculation.
The integer calculation rate is expressed as the calculation rate of
the integer data operation benchmark program running on the CPU.
Integer computing capability has its specific application scenarios,
such as discrete-time processing, data compression, search, sorting
algorithm, encryption algorithm, decryption algorithm, etc.
Floating point calculation rate is expressed as the calculation rate
of the floating-point data operation benchmark program running on the
CPU. There are many kinds of benchmark programs, each of which can
reflect the floating-point computing performance of nodes from
different aspects.
The hash calculation rate refers to the output speed of the hash
function when the computer performs intensive mathematical and
encryption related operations. For example, in the process of
obtaining bitcoin through "mining", how many hash collisions can a
mining machine do per second, and the unit is hash/s.
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B.1.3. Relation of Computing Types and Chips
The differences computing capacity of the above different chip types
is summarized as figure 4 shows. CPU is good at intCalculation, GPU
and FPGA are good at floatCalculation, and ASIC is good at
intCalculation.
+-----+------------------+------------------+------------------+
| | intCalculation | floatCalculation | hashCalculation |
+-----+------------------+------------------+------------------+
| CPU | good | Ordinary | Ordinary |
+-----+------------------+------------------+------------------+
| GPU | Ordinary | good | Ordinary |
+-----+------------------+------------------+------------------+
| FPGA| Ordinary | good | Ordinary |
+-----+------------------+------------------+------------------+
| ASIC| Ordinary | good | good |
+-----+------------------+------------------+------------------+
Figure 4: Relation of Computing Types and Chips
B.2. Communication, Cache and Storage Capacity
Besides the computing capacity, the communication, cache, and storage
capacity should also be considered because each of them can
potentially influence the comprehensive capacity of computing
resource nodes.
The communication capacity is the external communication rate of
computing nodes. From the point of view of a single node, the
communication capability indicator of a node mainly includes the
network bandwidth. Moreover, it is often to have cluster of service
instances for one task (like Hadoop architecture). Therefore the
network capacity among those instances are also important factor in
assessing the capability of the cluster of the service nodes for one
task.
The cache(memory) capacity describers the amount of of the cache unit
on a node. The memory (CACHE) indicator mainly includes the
cache(memory) capacity and cache(memory) bandwidth.
The storage capacity is the external storage (for example, hard disk)
of the computing node. The storage indicators of a node mainly
includes the storage capacity, storage bandwidth, operations per
second (IOPs) and response time of the node.
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B.3. Comprehensive Computing Capability Evaluation
Based on the architecture of computing resource modeling, this
Section proposes the comprehensive performance evaluation methods
based on the vectors to represent each capability of computing,
communication, cache, and storage.
Figure 5~8 shows the vector of computing node(i) including each
aspects.
+- -+
A(i)=| Computing Capacity(i) |
+- -+
Figure 5: Computing Performance Vector
+- -+
B(i)=| Communication Capacity(i) |
+- -+
Figure 6: Comunication Performance Vector
+- -+
C(i)=| Cache Capacity(i) |
+- -+
Figure 7: Cache Performance Vector
+- -+
D(i)=| Storage Capacity(i) |
+- -+
Figure 8: Storage Performance Vector
The vector of computing capacity, communication capacity, cache
capacity and storage capacity could be further weighted to a
comprehensive vector.
V = aA+bB+cC+dD
Figure 9: Comprehensive Vector
Where, a, b, c and d are the weight coefficients corresponding to the
evaluation indicators of computing capacity, communication capacity,
cache capacity and storage capacity respectively, and a+b+c+d=1.
Authors' Addresses
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Zongpeng Du
China Mobile
No.32 XuanWuMen West Street
Beijing
100053
China
Email: duzongpeng@foxmail.com
Yuexia Fu
China Mobile
No.32 XuanWuMen West Street
Beijing
100053
China
Email: fuyuexia@chinamobile.com
Cheng Li
Huawei Technologies
Email: c.l@huawei.com
Guangping Huang
ZTE
Email: huang.guangping@zte.com.cn
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