TEAS Working Group A. Wang
Internet-Draft China Telecom
Intended status: Informational X. Huang
Expires: April 11, 2020 C. Kou
BUPT
Z. Li
China Mobile
P. Mi
Huawei Technologies
October 9, 2019
Scenarios and Simulation Results of PCE in Native IP Network
draft-ietf-teas-native-ip-scenarios-10
Abstract
Requirements for providing the End to End(E2E) performance assurance
are emerging within the service provider network. While there are
various technology solutions, there is no one solution which can
fulfill these requirements for a native IP network. One universal
(E2E) solution which can cover both intra-domain and inter-domain
scenarios is needed.
One feasible E2E traffic engineering solution is the addition of
central control in a native IP network. This document describes
various complex scenarios and simulation results when applying the
Path Computation Element (PCE) in a native IP network. This
solution, referred to as Centralized Control Dynamic Routing (CCDR),
integrates the advantage of using distributed protocols and the power
of a centralized control technology.
Status of This Memo
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This Internet-Draft will expire on April 11, 2020.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. CCDR Scenarios . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. QoS Assurance for Hybrid Cloud-based Application . . . . 4
3.2. Link Utilization Maximization . . . . . . . . . . . . . . 5
3.3. Traffic Engineering for Multi-Domain . . . . . . . . . . 6
3.4. Network Temporal Congestion Elimination . . . . . . . . . 7
4. CCDR Simulation . . . . . . . . . . . . . . . . . . . . . . . 7
4.1. Case Study for CCDR algorithm . . . . . . . . . . . . . . 8
4.2. Topology Simulation . . . . . . . . . . . . . . . . . . . 10
4.3. Traffic Matrix Simulation . . . . . . . . . . . . . . . . 10
4.4. CCDR End-to-End Path Optimization . . . . . . . . . . . . 11
4.5. Network Temporal Congestion Elimination . . . . . . . . . 12
5. CCDR Deployment Consideration . . . . . . . . . . . . . . . . 13
6. Security Considerations . . . . . . . . . . . . . . . . . . . 14
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 15
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 15
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1. Normative References . . . . . . . . . . . . . . . . . . 15
10.2. Informative References . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
1. Introduction
A service provider network is composed of thousands of routers that
run distributed protocols to exchange the reachability information.
The path for the destination network is mainly calculated, and
controlled, by the distributed protocols. These distributed
protocols are robust enough to support most applications, but have
some difficulties supporting the complexities needed for traffic
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engineering applications, e.g. E2E performance assurance, or
maximizing the link utilization within an IP network.
Multiprotocol Label Switching (MPLS) using Traffic Engineering (TE)
technology (MPLS-TE)[RFC3209]is one solution for traffic engineering
network but it introduces an MPLS network and related technology
which would be an overlay of the IP network. MPLS-TE technology is
often used for Label Switched Path (LSP) protection and complex path
set-up within a domain.
It has not been widely deployed for meeting E2E (especially in inter-
domain) dynamic performance assurance requirements for an IP network.
Segment Routing [RFC8402] is another solution that integrates some
advantages of using a distributed protocol and a centrally control
technology, but it requires the underlying network, especially the
provider edge router, to do a label push and pop action in-depth, and
adds complexity, when coexisting with the Non-Segment Routing
network. Additionally, it can only maneuver the E2E paths for MPLS
and IPv6 traffic via different mechanisms.
Deterministic Networking (DetNet)[RFC8578] is another possible
solution. It is primarily focused on providing bounded latency for a
flow and introduces additional requirements on the domain edge
router. The current DetNet scope is within one domain. The use
cases defined in this document do not require the additional
complexity of deterministic properties and so differ from the DetNet
use cases.
This draft describes scenarios for a native IP network that a
Centralized Control Dynamic Routing (CCDR) framework can easily
solve, without requiring a change of the data plane behavior on the
router. It also provides path optimization simulation results to
illustrate the applicability of the CCDR framework.
This draft is the base document of the following two drafts: the
universal solution draft, which is suitable for intra-domain and
inter-domain TE scenario, is described in
[I-D.ietf-teas-pce-native-ip]; the related protocol extension
contents is described in [I-D.ietf-pce-pcep-extension-native-ip]
2. Terminology
This document uses the following terms defined in [RFC5440]: PCE.
The following terms are defined in this document:
o BRAS: Broadband Remote Access Server
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o CD: Congestion Degree
o CR: Core Router
o CCDR: Centralized Control Dynamic Routing
o E2E: End to End
o IDC: Internet Data Center
o MAN: Metro Area Network
o QoS: Quality of Service
o SR: Service Router
o TE: Traffic Engineering
o UID: Utilization Increment Degree
o WAN: Wide Area Network
3. CCDR Scenarios
The following sections describe various deployment scenarios for
applying the CCDR framework.
3.1. QoS Assurance for Hybrid Cloud-based Application
With the emergence of cloud computing technologies, enterprises are
putting more and more services on a public oriented cloud
environment, but keeping core business within their private cloud.
The communication between the private and public cloud sites will
span the Wide Area Network (WAN) network. The bandwidth requirements
between them are variable and the background traffic between these
two sites varies over time. Enterprise applications require
assurance of the E2E Quality of Service(QoS) performance on demand
for variable bandwidth services.
CCDR, which integrates the merits of distributed protocols and the
power of centralized control, is suitable for this scenario. The
possible solution framework is illustrated below:
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+------------------------+
| Cloud Based Application|
+------------------------+
|
+-----------+
| PCE |
+-----------+
|
|
//--------------\\
///// \\\\\
Private Cloud Site || Distributed |Public Cloud Site
| Control Network |
\\\\\ /////
\\--------------//
Figure 1: Hybrid Cloud Communication Scenario
As illustrated in Figure 1, the source and destination of the "Cloud
Based Application" traffic are located at "Private Cloud Site" and
"Public Cloud Site" respectively.
By default, the traffic path between the private and public cloud
site is determined by the distributed control network. When
application requires the E2E QoS assurance, it can send these
requirements to the PCE, and let the PCE compute one E2E path which
is based on the underlying network topology and the real traffic
information, to accommodate the application's QoS requirements.
Section 4.4 of this document describes the simulation results for
this use case.
3.2. Link Utilization Maximization
Network topology within a Metro Area Network (MAN) is generally in a
star mode as illustrated in Figure 2, with different devices
connected to different customer types. The traffic from these
customers is often in a tidal pattern, with the links between the
Core Router(CR)/Broadband Remote Access Server(BRAS) and CR/Service
Router(SR), experiencing congestion in different periods, because the
subscribers under BRAS, often use the network at night, and the
dedicated line users under SR, often use the network during the
daytime. The link between BRAS/SR and CR must satisfy the maximum
traffic volume between them respectively and this causes these links
often to be under-utilized.
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+--------+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS| |SR| |BRAS| |SR|
+----+ +--+ +----+ +--+
Figure 2: Star-mode Network Topology within MAN
If we consider connecting the BRAS/SR with a local link loop (which
is usually lower cost), and control the overall MAN topology with the
CCDR framework, we can exploit the tidal phenomena between the BRAS/
CR and SR/CR links, maximizing the utilization of these links (which
are usually higher cost).
+-------+
----- PCE |
| +-------+
+----|---+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS-----SR| |BRAS-----SR|
+----+ +--+ +----+ +--+
Figure 3: Link Utilization Maximization via CCDR
3.3. Traffic Engineering for Multi-Domain
Service provider networks are often comprised of different domains,
interconnected with each other,forming a very complex topology as
illustrated in Figure 4. Due to the traffic pattern to/from the MAN
and IDC, the utilization of the links between them are often
asymmetric. It is almost impossible to balance the utilization of
these links via a distributed protocol, but this unbalance can be
overcome utilizing the CCDR framework.
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+---+ +---+
|MAN|-----------------IDC|
+-|-| | +-|-+
| ---------| |
------|BackBone|------
| ----|----| |
| | |
+-|-- | ----+
|IDC|----------------|MAN|
+---| |---+
Figure 4: Traffic Engineering for Complex Multi-Domain Topology
A solution for this scenario requires the gathering of NetFlow
information, analysis of the source/destination AS, and determining
what is the main cause of the congested link. After this, the
operator can use the external Border Gateway Protocol(eBGP) sessions
to schedule the traffic among the different domains according to the
solution described in CCDR framework.
3.4. Network Temporal Congestion Elimination
In more general situations, there are often temporal congestion
within the service provider's network. Such congestion phenomena
often appear repeatedly, and if the service provider has methods to
mitigate it, it will certainly improve their network operations
capabilities and increase satisfaction for their customers. CCDR is
also suitable for such scenarios, as the controller can schedule
traffic out of the congested links, lowering the utilization of them
during these times. Section 4.5 describes the simulation results of
this scenario.
4. CCDR Simulation
The following sections describe one case study to illustrate CCDR
algorithm, the topology and traffic matrix generation process and the
optimization results for E2E QoS assured path and congestion
elimination in applied scenarios.
The structure and scale of the simulated topology is similar with the
real network. Several amounts of traffic matrix are generated to
simulate the different congestion condition in network, only one of
them is illustrated.
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4.1. Case Study for CCDR algorithm
Figure 5 depicts the topology of the network for the case study of
CCDR algorithm. There are 8 forwarding devices in the network. The
original cost and utilization are marked on it, as shown in the
figure. For example, the original cost and utilization for the link
(1,2) are 3 and 50% respectively. There are two flows: f1 and f2.
Both of these two flows are from node 1 to node 8. For simplicity,
it is assumed that the bandwidth of the link in the network is 10Mb/
s. The flow rate of f1 is 1Mb/s, and the flow rate of f2 is 2Mb/s.
The threshold of the link in congestion is 90%.
If OSPF protocol (ISIS is similar, because it also use the Dijstra's
algorithm) is applied in the network, which adopts Dijkstra's
algorithm, the two flows from node 1 to node 8 can only use the OSPF
path (p1: 1->2->3->8). It is because Dijkstra's algorithm mainly
considers original cost of the link. Since CCDR considers cost and
utilization simultaneously, the same path with OSPF will not be
selected due to the severe congestion of the link (2,3). In this
case, f1 will select the path (p2: 1->5->6->7->8) since the new cost
of this path is better than that of OSPF path. Moreover, the path p2
is also better than the path (p3: 1->2->4->7->8) for for flow f1.
However, f2 will not select the same path since it will cause the new
congestion in the link (6,7). As a result, f2 will select the path
(p3: 1->2->4->7->8).
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+-------+ +-------+
+---------+ f1 +--------->| | ----------> | |
| |---------------+ | +--------| 3 |-------------| 8 |
|Edge Node|-------------+ | | | +----->| | ----------> | |
| | | | | | | +-------+ 6/50% +-------+
+---------+ | | 4/95% | | | |
| | | | | 5/60% |
| v | | | |
+---------+ +-------+ +-------+ +-------+ +-------+
| | | |---------> | | | | | |
|Edge Node|-------| 1 |---------- | 2 |---------| 4 |--------| 7 |
| |-----> | |---------> | | 7/60% | | 5/45% | |
+---------+ f2 +-------+ 3/50% +-------+ +-------+ +-------+
| |
| |
| +-------+ +-------+ |
| 3/60% | | 5/55% | | 3/75%|
+---------------| 5 |-----------| 6 |----------+
| | | |
+-------+ +-------+
(a) Dijkstra's Algorithm(OSPF/ISIS)
+-------+ +-------+
+---------+ f1 | | | |
| |---------------+ +--------| 3 |-------------| 8 |
|Edge Node|-------------+ | | | | | |
| | | | | +-------+ 6/50% +-------+
+---------+ | | 4/95%| ^ | ^
| | | 5/60% | | |
| v | | | |
+---------+ +-------+ +-------+ +-------+ +-------+
| | | |---------> | |-------> | | -----> | |
|Edge Node|-------| 1 |---------- | 2 |---------| 4 |--------| 7 |
| |-----> | | | | 7/60% | | 5/45% | |
+---------+ f2 +-------+ 3/50% +-------+ +-------+ +-------+
| | | ^
| | | |
| | +-------+ +-------+ | |
| | 3/60% | | 5/55% | | 3/75%| |
| +---------------| 5 |-----------| 6 |----------+ |
+--------------> | |---------> | |------------+
+-------+ +-------+
(b) CCDR Algorithm
Figure 5: Case Study for CCDR's Algorithm
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4.2. Topology Simulation
The network topology mainly contains nodes and links information.
Nodes used in the simulation have two types: core node and edge node.
The core nodes are fully linked to each other. The edge nodes are
connected only with some of the core nodes. Figure 6 is a topology
example of 4 core nodes and 5 edge nodes. In this CCDR simulation,
100 core nodes and 400 edge nodes are generated.
+----+
/|Edge|\
| +----+ |
| |
| |
+----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+
+----+ +----+ +----+ |
/ | \ / | |
+----+ | \ / | |
|Edge| | X | |
+----+ | / \ | |
\ | / \ | |
+----+ +----+ +----+ |
|Edge|----|Core|-----|Core| |
+----+ +----+ +----+ |
| | |
| +------\ +----+
| ---|Edge|
+-----------------/ +----+
Figure 6: Topology of Simulation
The number of links connecting one edge node to the set of core nodes
is randomly between 2 to 30, and the total number of links is more
than 20000. Each link has a congestion threshold.
4.3. Traffic Matrix Simulation
The traffic matrix is generated based on the link capacity of
topology. It can result in many kinds of situations, such as
congestion, mild congestion and non-congestion.
In the CCDR simulation, the dimension of the traffic matrix is
500*500. About 20% links are overloaded when the Open Shortest Path
First (OSPF) protocol is used in the network.
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4.4. CCDR End-to-End Path Optimization
The CCDR E2E path optimization is to find the best path which is the
lowest in metric value and each link of the path is far below link's
threshold. Based on the current state of the network, the PCE within
CCDR framework combines the shortest path algorithm with a penalty
theory of classical optimization and graph theory.
Given a background traffic matrix, which is unscheduled, when a set
of new flows comes into the network, the E2E path optimization finds
the optimal paths for them. The selected paths bring the least
congestion degree to the network.
The link Utilization Increment Degree(UID), when the new flows are
added into the network, is shown in Figure 7. The first graph in
Figure 7 is the UID with OSPF and the second graph is the UID with
CCDR E2E path optimization. The average UID of the first graph is
more than 30%. After path optimization, the average UID is less than
5%. The results show that the CCDR E2E path optimization has an eye-
catching decrease in UID relative to the path chosen based on OSPF.
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+-----------------------------------------------------------+
| * * * *|
60| * * * * * *|
|* * ** * * * * * ** * * * * **|
|* * ** * * ** *** ** * * ** * * * ** * * *** **|
|* * * ** * ** ** *** *** ** **** ** *** **** ** *** **|
40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **|
UID(%)|* * ******* ** *** *** ******* **** ** *** ***** *********|
|*** ******* ** **** *********** *********** ***************|
|******************* *********** *********** ***************|
20|******************* ***************************************|
|******************* ***************************************|
|***********************************************************|
|***********************************************************|
0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000
+-----------------------------------------------------------+
| |
60| |
| |
| |
| |
40| |
UID(%)| |
| |
| |
20| |
| *|
| * *|
| * * * * * ** * *|
0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000
Flow Number
Figure 7: Simulation Result with Congestion Elimination
4.5. Network Temporal Congestion Elimination
Different degrees of network congestion were simulated. The
Congestion Degree (CD) is defined as the link utilization beyond its
threshold.
The CCDR congestion elimination performance is shown in Figure 8.
The first graph is the CD distribution before the process of
congestion elimination. The average CD of all congested links is
about 20%. The second graph shown in Figure 8 is the CD distribution
after using the congestion elimination process. It shows only 12
links among totally 20000 links exceed the threshold, and all the CD
values are less than 3%. Thus, after scheduling of the traffic away
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from the congested paths, the degree of network congestion is greatly
eliminated and the network utilization is in balance.
Before congestion elimination
+-----------------------------------------------------------+
| * ** * ** ** *|
20| * * **** * ** ** *|
|* * ** * ** ** **** * ***** *********|
|* * * * * **** ****** * ** *** **********************|
15|* * * ** * ** **** ********* *****************************|
|* * ****** ******* ********* *****************************|
CD(%) |* ********* ******* ***************************************|
10|* ********* ***********************************************|
|*********** ***********************************************|
|***********************************************************|
5|***********************************************************|
|***********************************************************|
|***********************************************************|
0+-----------------------------------------------------------+
0 0.5 1 1.5 2
After congestion elimination
+-----------------------------------------------------------+
| |
20| |
| |
| |
15| |
| |
CD(%) | |
10| |
| |
| |
5 | |
| |
| * ** * * * ** * ** * |
0 +-----------------------------------------------------------+
0 0.5 1 1.5 2
Link Number(*10000)
Figure 8: Simulation Result with Congestion Elimination
More detailed information about the algorithm can refer to [PTCS] .
5. CCDR Deployment Consideration
Above CCDR scenarios and simulation results demonstrate that it is
feasible to find one general solution to cope with various complex
situations. Integrated use of a centralized controller for the more
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complex optimal path computations in a native IP network results in
significant improvements without impacting the underlay network
infrastructure.
For intra-domain or inter-domain native IP TE scenario, the
deployment of CCDR solution is similar. This universal deployment
characteristic can facilitate the operator to tackle their traffic
engineering issues in one general manner. To deploy the CCDR
solution, the PCE should collect the underlay network topology
dynamically, for example via BGP-LS[RFC7752]. It also needs to
gather the network traffic information periodically from the network
management platform. The simulation results show PCE can compute the
E2E optimal path within seconds thus it can cope with the change of
underlay network in minute scale. More agile requirements needs
increase the sample rate of underlay network, also decrease the
detection and notification interval of underlay network. The methods
to gather and decrease the latency of these information are out of
the scope of this draft.
6. Security Considerations
This document considers mainly the integration of distributed
protocols and the central control capability of a PCE. While it
certainly can ease the management of network in various traffic
engineering scenarios as described in this document, the centralized
control also bring a new point that may be easily attacked.
Solutions for CCDR scenarios need to consider protection of the PCE
and communication with the underlay devices.
[RFC5440] and [RFC8253] provide additional information.
The control priority and interaction process should also be carefully
designed for the combination of distributed protocol and central
control. Generally, the central control instruction should have
higher priority than the forwarding actions determined by the
distributed protocol. When the communication between PCE and the
underlay devices is not in function, the distributed protocol should
take over the control right of the underlay network.
[I-D.ietf-teas-pce-native-ip] provide more considerations
corresponding to the solution.
7. IANA Considerations
This document does not require any IANA actions.
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8. Contributors
Lu Huang contributed to the content of this draft.
9. Acknowledgement
The author would like to thank Deborah Brungard, Adrian Farrel,
Huaimo Chen, Vishnu Beeram and Lou Berger for their support and
comments on this draft.
Thanks Benjamin Kaduk, Roman Danyliw, Alvaro Retana and Eric Vyncke
for their views and comments.
10. References
10.1. Normative References
[RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation
Element (PCE) Communication Protocol (PCEP)", RFC 5440,
DOI 10.17487/RFC5440, March 2009,
<https://www.rfc-editor.org/info/rfc5440>.
[RFC7752] Gredler, H., Ed., Medved, J., Previdi, S., Farrel, A., and
S. Ray, "North-Bound Distribution of Link-State and
Traffic Engineering (TE) Information Using BGP", RFC 7752,
DOI 10.17487/RFC7752, March 2016,
<https://www.rfc-editor.org/info/rfc7752>.
[RFC8253] Lopez, D., Gonzalez de Dios, O., Wu, Q., and D. Dhody,
"PCEPS: Usage of TLS to Provide a Secure Transport for the
Path Computation Element Communication Protocol (PCEP)",
RFC 8253, DOI 10.17487/RFC8253, October 2017,
<https://www.rfc-editor.org/info/rfc8253>.
10.2. Informative References
[I-D.ietf-pce-pcep-extension-native-ip]
Wang, A., Khasanov, B., Cheruathur, S., Zhu, C., and S.
Fang, "PCEP Extension for Native IP Network", draft-ietf-
pce-pcep-extension-native-ip-04 (work in progress), August
2019.
[I-D.ietf-teas-pce-native-ip]
Wang, A., Zhao, Q., Khasanov, B., Chen, H., and R. Mallya,
"PCE in Native IP Network", draft-ietf-teas-pce-native-
ip-04 (work in progress), August 2019.
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[PTCS] Zhang, P., Xie, K., Kou, C., Huang, X., Wang, A., and Q.
Sun, "A Practical Traffic Control Scheme With Load
Balancing Based on PCE Architecture", IEEE
Access 18526773, DOI 10.1109/ACCESS.2019.2902610, March
2019, <http://ieeexplore.ieee.org/document/8657733>.
[RFC3209] Awduche, D., Berger, L., Gan, D., Li, T., Srinivasan, V.,
and G. Swallow, "RSVP-TE: Extensions to RSVP for LSP
Tunnels", RFC 3209, DOI 10.17487/RFC3209, December 2001,
<https://www.rfc-editor.org/info/rfc3209>.
[RFC8402] Filsfils, C., Ed., Previdi, S., Ed., Ginsberg, L.,
Decraene, B., Litkowski, S., and R. Shakir, "Segment
Routing Architecture", RFC 8402, DOI 10.17487/RFC8402,
July 2018, <https://www.rfc-editor.org/info/rfc8402>.
[RFC8578] Grossman, E., Ed., "Deterministic Networking Use Cases",
RFC 8578, DOI 10.17487/RFC8578, May 2019,
<https://www.rfc-editor.org/info/rfc8578>.
Authors' Addresses
Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing, Beijing 102209
China
Email: wangaj3@chinatelecom.cn
Xiaohong Huang
Beijing University of Posts and Telecommunications
No.10 Xitucheng Road, Haidian District
Beijing
China
Email: huangxh@bupt.edu.cn
Caixia Kou
Beijing University of Posts and Telecommunications
No.10 Xitucheng Road, Haidian District
Beijing
China
Email: koucx@lsec.cc.ac.cn
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Internet-Draft CCDR Scenario and Simulation Results October 2019
Zhenqiang Li
China Mobile
32 Xuanwumen West Ave, Xicheng District
Beijing 100053
China
Email: li_zhenqiang@hotmail.com
Penghui Mi
Huawei Technologies
Tower C of Bldg.2, Cloud Park, No.2013 of Xuegang Road
Shenzhen, Bantian,Longgang District 518129
China
Email: mipenghui@huawei.com
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