Internet DRAFT - draft-liu-can-computing-resource-modeling
draft-liu-can-computing-resource-modeling
rtgwg P. Liu
Internet-Draft Z. Du
Intended status: Informational China Mobile
Expires: 12 January 2023 L. Rui
W. Li
Beijing University of Posts and Telecommunications
C. Li
Huawei Technologies
G. Huang
ZTE
11 July 2022
Computing Resource Modeling for CAN
draft-liu-can-computing-resource-modeling-00
Abstract
This document describes the considerations and potential architecture
of modeling the computing resource in the Computing-Aware
Network(CAN).
Moreover, the network and application based modeling are also
presented in this document to meet the potential requirements of
integrated and hierarchical modeling.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
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This Internet-Draft will expire on 12 January 2023.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 4
3. Requirements of Computing Resource Modeling . . . . . . . . . 5
3.1. Support Classification of Chips and Computing Types . . . 5
3.2. Support Multi-level Modeling . . . . . . . . . . . . . . 5
3.3. Support to be used for Further Representation . . . . . . 5
4. Usage of Computing Resource Modeling of CAN . . . . . . . . . 6
4.1. Modeling Based on CAN-defined Format . . . . . . . . . . 6
4.2. Modeling Based on Application-defined Method . . . . . . 7
5. Architecture of Computing Modeling . . . . . . . . . . . . . 8
5.1. Computing Capacity . . . . . . . . . . . . . . . . . . . 9
5.1.1. Types of Chips . . . . . . . . . . . . . . . . . . . 9
5.1.2. Type of Computing . . . . . . . . . . . . . . . . . . 10
5.1.3. Relation of Computing Types and Chips . . . . . . . . 11
5.1.4. Consideration of Using in CAN . . . . . . . . . . . . 11
5.2. Communication, Cache and Storage Capacity . . . . . . . . 12
5.3. Comprehensive Computing Capability Evaluation . . . . . . 12
5.4. Consideration of Using in CAN . . . . . . . . . . . . . . 13
6. Network Resource Modeling . . . . . . . . . . . . . . . . . . 14
6.1. Consideration of Using in CAN . . . . . . . . . . . . . . 14
7. Application Demands Modeling . . . . . . . . . . . . . . . . 14
7.1. Consideration of Using in CAN . . . . . . . . . . . . . . 14
8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 15
9. Security Considerations . . . . . . . . . . . . . . . . . . . 15
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 15
12. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 15
13. Informative References . . . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
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1. Introduction
Computing-Aware Networking (CAN) is proposed to support steering the
traffic among different edge sites according to both the real-time
network and computing resource status. This requires the network to
be aware of computing resource information and select a service
instance based on the joint metric of computing and networking.[I-D.l
iu-dyncast-ps-usecases][I-D.liu-dyncast-gap-reqs][I-D.li-dyncast-arch
itecture] proposed Dyncast to meet the traffic steering requirements
in CAN.
In order to generate steering strategies, the modeling of computing
capacity is required. Different from the network, computing capacity
is more complex to be measurement. For instance, it is hard to
predict how long will be used to process a specific computing task
based on the different computing resource, which 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,
moreover, 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 indicator for computing services:
[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
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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.
Based on those and the demand of CAN 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. This document doesn't specify the specific using
way of the modeling, including who will model the computing resource,
what factors must be considered and the form of the representing
results based on modeling. A proposed vector of modeling result
could be further weighted into a group of indicators or a single
indicator according to the specific demand of applications.
2. Definition of Terms
This document makes use of the following terms:
Computing-Aware Networking(CAN): Aiming at computing and network
resource optimization by steering traffic to appropriate computing
resources considering not only routing metric but also computing
resource metric and service affiliation.
Service: A monolithic functionality that is provided by an endpoint
according to the specification for said service. A composite
service can be built by orchestrating monolithic services.
Service instance: Running environment (e.g., a node) that makes the
functionality of a service available. One service can have several
instances running at different network locations.
Service identifier: Used to uniquely identify a service, at the same
time identifying the whole set of service instances that each
represent the same service behavior, no matter where those service
instances are running.
Service transaction: Has one or more several 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.
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3. Requirements of Computing Resource Modeling
3.1. Support Classification of Chips and 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 is widely used in
artificial intelligence and neural network algorithm computing. FPGA
and ASCI 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. Therefore:
MUST support the classification of various heterogeneous chips for
different kinds of computing tasks.
MUST support the classification of the computing types required by
the task.
3.2. Support Multi-level Modeling
Because the network and computing have multi-dimensional and
hierarchical resources, such as cache, storage, communication, etc.,
these dimensions will affect each other and further affect the
overall level of computing capacity. Other factors other than 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 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. Therefore:
MUST support modeling computing nodes, including computing, storage,
communication,etc..
SHOULD support the integrated modeling of the converged network.
3.3. Support to be used for Further Representation
Modeling itself provides a general method to evaluate the capacities
of computing resource. For CAN, 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
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representation methods. Moreover, in order to use computing resource
status more efficiently and protect privacy, modeling for the further
representation of resource information needs to support the necessary
simplification and obfuscation.
MUST support different modeling methods according to specific
representation demands.
MUST support Application-oriented modeling methods.
MUST support obscuring the computing Information on demand of the
application.
4. Usage of Computing Resource Modeling of CAN
4.1. Modeling Based on CAN-defined Format
Figure 1 shows the case of modeling based on CAN-defiend Format. CAN
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 CAN. Then CAN could select the
specific service instance based on the computing resource and network
resource status.
In this way, the CAN domain and computing domain has the relative
loose boundary based on the situation that the CAN service and
computing resource belongs to the same provider, CAN 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 including 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, CAN could
steer category-based applications traffic based on the unified
modeling format and interface.
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|
CAN Domain | Computing Domain
+--------+ ---------------------->-------------------> +-------------+
|visuali-| Modeling Format | Computing |
|zation | | | |
+--------+ <--------------------<--------------------- | Resource |
|Traffic | Stastic level/category of computing node | |
|Steering| | | Modeling |
+--------+ <--------------------<--------------------- +-------------+
Dynamic capability of computing node
|
|
Figure 1: Modeling Based on CAN-defined Format
4.2. Modeling Based on Application-defined Method
Figure 2 shows the case of modeling based on application-defiend
method. Computing resource of the specific application evaluates
it's computing capacity by itself, and then notifies the result which
might be the index of real time computing level to CAN. Then CAN
selects the specific service instance based on the computing index.
In this way, the CAN domain and computing domain has the strict
boundary based on the situation that the CAN service and computing
resource belongs to the different providers. CAN 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 CAN is authorized by application, it could steer traffic based on
network status at the same time.
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| |
| |
CAN Domain | | Computing Domain
| |
| | +-------------+
+--------+ | | | Computing |
|Traffic | | | | |
| | <---------------------<---------- ---------- | Resource |
|Steering| dynamic index of computing capacity level | |
+--------+ | | | Modeling |
| | +-------------+
| |
| |
| |
| |
Figure 2: Modeling Based on Application-defined Method
5. Architecture of Computing Modeling
This Section describes the potential architecture of computing
resource modeling, regardless of any ways of the further usage of
traffic steering of CAN, 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
5.1. 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.
5.1.1. 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.
5.1.2. 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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5.1.3. 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
5.1.4. Consideration of Using in CAN
For the CAN-defined modeling way, CAN could get the computing
information of edge sites/service instance more or less, and we
assume that the CAN system also could get the
characteristics/demands/identifier of service transaction, then
select the service instance among different edge sites. For example,
there is a service transaction with the task of image processing,
which could consider the identifier for service category of service
demand, then the CAN system could find the suitable edge sites/
service instance which has the computing resource of float
calculation or GPU.
When using in the network, it could use 00,01,10 to represent the
different computing chips or computing task, then it could be
recorded in the control plane to support the mapping and further
selection to the computing resource. In some cases, there will be
more factors of computing resource, so some processing of obscuring
and weighting are needed, the representation or signaling of the
computing status might not be so direct.
For the application-defined modeling way, CAN might not know any
explicit calculation information of computing types or chips
category, even might not what kind of index is.
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5.2. 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.
5.3. 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)=| Comunication Capacity(i) |
+- -+
Figure 6: Comunication Performance Vector
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+- -+
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: Storage Performance 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.
5.4. Consideration of Using in CAN
The vector gives the overall view of the evaluation result of
computing resource, but no specific expression is specified, that is,
just to model the computing resource including the computing,
communication, cache, and storage capability, while the result could
be weighted into any of the following form to be used under different
demands:
o a group of vectors to represent the weighted level of computing,
bandwidth, cache, storage capacity.
o a single vector to represent the single comprehensive and weighted
level of overall capability.
Then the CAN system could select the service instance based on the
processed vector. To expose the computing status, some existing
protocol could be extended, which is out of the scope of this
document.
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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 CAN
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 deivces 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 CAN
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, measurement all the resource in a unified
dimension, such as latency, reliability, etc.
If there is no strict demand of consider them at same time, for
instance, consider computing status first and then network status.
CAN 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 really needed.
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 CAN
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 CAN knows the application's demand,
there should be a mapping between application demand and the modeling
of the computing and/or network resource.
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8. Conclusion
This document presents the potential modeling methods for CAN to
steer the traffic to the appropriate edge sites accurately. The
modeling algorithm and modeling processing might belong to computing
domain, while the further representation and signaling of the
weighted computing information based on the modeling could be the
basis of traffic steering. Moreover, the visualization of computing
resources and more functions could be realized to support the
computing and network joint optimization.
9. Security Considerations
TBD.
10. IANA Considerations
TBD.
11. Acknowledgements
The author would like to thank Thomas Fossati, Dirk Trossen, Linda
Dunbar for their valuable suggestions to this document.
12. Contributors
The following people have substantially contributed to this document:
Jing Wang
China Mobile
wangjingjc.chinamobile.com
13. Informative References
[I-D.liu-dyncast-ps-usecases]
Liu, P., Eardley, P., Trossen, D., Boucadair, M.,
Contreras, L. M., and C. Li, "Dynamic-Anycast (Dyncast)
Use Cases and Problem Statement", Work in Progress,
Internet-Draft, draft-liu-dyncast-ps-usecases-03, 7 March
2022, <https://www.ietf.org/archive/id/draft-liu-dyncast-
ps-usecases-03.txt>.
[I-D.liu-dyncast-gap-reqs]
Liu, P., Jiang, T., Eardley, P., Trossen, D., and C. Li,
"Dynamic-Anycast (Dyncast) Gap analysis and Requirements",
Work in Progress, Internet-Draft, draft-liu-dyncast-gap-
reqs-00, 8 July 2022, <https://www.ietf.org/archive/id/
draft-liu-dyncast-gap-reqs-00.txt>.
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[I-D.li-dyncast-architecture]
Li, Y., Iannone, L., Trossen, D., Liu, P., and C. Li,
"Dynamic-Anycast Architecture", Work in Progress,
Internet-Draft, draft-li-dyncast-architecture-04, 10 July
2022, <https://www.ietf.org/archive/id/draft-li-dyncast-
architecture-04.txt>.
[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.
[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.
Authors' Addresses
Peng Liu
China Mobile
No.32 XuanWuMen West Street
Beijing
100053
China
Email: liupengyjy@chinamobile.com
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Zongpeng Du
China Mobile
No.32 XuanWuMen West Street
Beijing
100053
China
Email: duzongpeng@chinamobile.com
Lanlan Rui
Beijing University of Posts and Telecommunications
No.10 XiTuCheng Road, Haidian District
Beijing
100876
China
Email: llrui@bupt.edu.cn
Wenjing Li
Beijing University of Posts and Telecommunications
No.10 XiTuCheng Road, Haidian District
Beijing
100876
China
Email: wjli@bupt.edu.cn
Cheng Li
Huawei Technologies
Email: c.l@huawei.com
Guangping Huang
ZTE
Email: huang.guangping@zte.com.cn
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