Internet DRAFT - draft-ietf-teas-native-ip-scenarios
draft-ietf-teas-native-ip-scenarios
TEAS Working Group A. Wang
Internet-Draft China Telecom
Intended status: Informational X. Huang
Expires: May 1, 2020 C. Kou
BUPT
Z. Li
China Mobile
P. Mi
Huawei Technologies
October 29, 2019
Scenarios and Simulation Results of PCE in Native IP Network
draft-ietf-teas-native-ip-scenarios-12
Abstract
Requirements for providing the End to End(E2E) performance assurance
are emerging within the service provider networks. While there are
various technology solutions, there is no single solution that can
fulfill these requirements for a native IP network. In particular,
there is a need for a universal (E2E) solution that can cover both
intra- and inter-domain scenarios.
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, providing traffic engineering
for native IP networks in a manner that applies equally to intra- and
inter-domain scenarios.
Status of This Memo
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This Internet-Draft will expire on May 1, 2020.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
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 . . . . . . . . . . . . . . . . . . . 9
4.3. Traffic Matrix Simulation . . . . . . . . . . . . . . . . 10
4.4. CCDR End-to-End Path Optimization . . . . . . . . . . . . 10
4.5. Network Temporal Congestion Elimination . . . . . . . . . 12
5. CCDR Deployment Consideration . . . . . . . . . . . . . . . . 14
6. Security Considerations . . . . . . . . . . . . . . . . . . . 14
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 15
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 15
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1. Normative References . . . . . . . . . . . . . . . . . . 15
10.2. Informative References . . . . . . . . . . . . . . . . . 16
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
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protocols are robust enough to support most applications, however,
they have some difficulties supporting the complexities needed for
traffic 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
networks 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 several scenarios for a native IP network where
a Centralized Control Dynamic Routing (CCDR) framework can produce
qualitative improvement in efficiency without requiring a change of
the data-plane behavior on the router. Using knowledge of BGP(Border
Gateway Protocol) session-specific prefixes advertised by a router,
the network topology and the near real time link utilization
information from network management systems, a central PCE is able to
compute an optimal path and give the underlay routers the destination
address to use to reach the BGP nexthop, such that the distributed
routing protocol will use the computed path via traditional recursive
lookup procedure. Some results from simulations of path optimization
are also presented, to concretely illustrate a variety of scenarios
where CCDR shows significant improvement over traditional distributed
routing protocols.
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
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[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
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 where
applying the CCDR framework is intuitively expected to produce
improvements, based on the macro-scale properties of the framework
and the scenario.
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
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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:
+------------------------+
| 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 leased
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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.
+--------+
| 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 central trunk
links (which are usually higher cost than the local loops).
+-------+
----- 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(s). 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, for example due to daily or
weekly periodic bursts, or large events that are scheduled well in
advance. Such congestion phenomena often appear regularly, 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 a specific case study to illustrate
the workings of the CCDR algorithm with concrete paths/metrics, as
well as a procedure for generating topology and traffic matrices and
the results from simulations applying CCDR for E2E QoS (assured path
and congestion elimination) over the generated topologies and traffic
matrices. In all cases examined, the CCDR algorithm produces
qualitatively significant improvement over the reference (OSPF)
algorithm, suggesting that CCDR will have broad applicability.
The structure and scale of the simulated topology is similar to that
of the real networks. Multiple different traffic matrices were
generated to simulate different congestion conditions in the network.
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Only one of them is illustrated since the others produce similar
results.
4.1. Case Study for CCDR Algorithm
In this section we consider a specific network topology for case
study, examining the path selected by OSPF and CCDR and evaluating
how and why the paths differ. Figure 5 depicts the topology of the
network in this case. 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 as 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 +-------> +-----+ ----> +-----+
|Edge|-----------+ |+--------| 3 |-------| 8 |
|Node|---------+ | ||+-----> +-----+ ----> +-----+
+----+ | | 4/95%||| 6/50% |
| | ||| 5/60%|
| v ||| |
+----+ +-----+ -----> +-----+ +-----+ +-----+
|Edge|-------| 1 |--------| 2 |------| 4 |------| 7 |
|Node|-----> +-----+ -----> +-----+7/60% +-----+5/45% +-----+
+----+ f2 | 3/50% |
| |
| 3/60% +-----+ 5/55%+-----+ 3/75% |
+-----------| 5 |------| 6 |---------+
+-----+ +-----+
(a) Dijkstra's Algorithm (OSPF/ISIS)
+----+ f1 +-----+ ----> +-----+
|Edge|-----------+ +--------| 3 |-------| 8 |
|Node|---------+ | | +-----+ ----> +-----+
+----+ | | 4/95% | 6/50% ^|^
| | | 5/60%|||
| v | |||
+----+ +-----+ -----> +-----+ ---> +-----+ ---> +-----+
|Edge|-------| 1 |--------| 2 |------| 4 |------| 7 |
|Node|-----> +-----+ +-----+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
4.2. Topology Simulation
Moving on from the specific case study, we now consider a class of
networks more representative of real deployments, with a fully-linked
core network that serves to connect edge nodes, which themselves
connect to only a subset of the core. An example of such a topology
is shown in Figure 6, for the case of 4 core nodes and 5 edge nodes.
The CCDR simulations presented in this work use topologies involving
100 core nodes and 400 edge nodes. While the resulting graph does
not fit on this page, this scale of network is similar to what is
deployed in production environments.
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+----+
/|Edge|\
| +----+ |
| |
| |
+----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+
+----+ +----+ +----+ |
/ | \ / | |
+----+ | \ / | |
|Edge| | X | |
+----+ | / \ | |
\ | / \ | |
+----+ +----+ +----+ |
|Edge|----|Core|-----|Core| |
+----+ +----+ +----+ |
| | |
| +------\ +----+
| ---|Edge|
+-----------------/ +----+
Figure 6: Topology of Simulation
For the simulations, the number of links connecting one edge node to
the set of core nodes is randomly chosen between 2 to 30, and the
total number of links is more than 20000. Each link has a congestion
threshold, which can be arbitrarily set to (e.g.) 90% of the nominal
link capacity without affecting the simulation results.
4.3. Traffic Matrix Simulation
For each topology, a 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 (100 core nodes plus 400 edge nodes). About 20% of links are
overloaded when the Open Shortest Path First (OSPF) protocol is used
in the network.
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 for each link of the path, the
utilizatioin is far below link's congestion 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.
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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.
While real-world results invariably differ from simulations (for
example, real-world topologies are likely to exhibit correlation in
the attachment patterns for edge nodes to the core, which are not
reflected in these results), the dramatic nature of the improvement
in UID and the choice of simulated topology to resemble real-world
conditions suggests that real-world deployments will also experience
significant improvement in UID results.
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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
During the simulations, different degrees of network congestion were
considered. To examine the effect of CCDR on link congestion, we
consider the Congestion Degree (CD) of a link, 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 that only
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12 links among the total of 20000 links exceed the threshold, and all
the CD values are less than 3%. Thus, after scheduling of the traffic
away 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
It is clear that using an active path-computation mechanism that is
able to take into account observed link traffic/congestion, the
occurrence of congestion events can be greatly reduced. Only when a
preponderance of links in the network are near their congestion
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threshold will the central controller be unable to find a clear path,
as opposed to when a static metric-based procedure is used, which
will produce congested paths once a single bottleneck approaches its
capacity. More detailed information about the algorithm can be found
in[PTCS] .
5. CCDR Deployment Consideration
The above CCDR scenarios and simulation results demonstrate that a
single general solution can be found that copes with multiple complex
situations. The specific situations considered are not known to have
any special properties, so it is expected that the benefits
demonstrated will have general applicability. Accordingly, the
integrated use of a centralized controller for the more complex
optimal path computations in a native IP network should result in
significant improvements without impacting the underlay network
infrastructure.
For intra-domain or inter-domain native IP TE scenarios, the
deployment of a CCDR solution is similar, with the centralized
controller being able to compute paths and no changes required to the
underlying network infrastructure. This universal deployment
characteristic can facilitate a generic traffic engineering solution,
where operators do not need to differentiate between intra-domain and
inter-domain TE cases.
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
that the PCE can compute the E2E optimal path within seconds, thus it
can cope with the change of underlay network on the scale of minutes.
More agile requirements would need to increase the sample rate of
underlay network and decrease the detection and notification interval
of the 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.
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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] provides more considerations
corresponding to the solution.
7. IANA Considerations
This document does not require any IANA actions.
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 for his careful review and valuable suggestions
to this draft. Also thanks 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>.
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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.
[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
Wang, et al. Expires May 1, 2020 [Page 16]
Internet-Draft CCDR Scenario and Simulation Results October 2019
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
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
Wang, et al. Expires May 1, 2020 [Page 17]
