Scenarios and Simulation Results of PCE in a Native IP Network
RFC 8735
| Document | Type | RFC - Informational (February 2020; No errata) | |
|---|---|---|---|
| Authors | Aijun Wang , Xiaohong Huang , Caixia Qou , Zhenqiang Li , Penghui Mi | ||
| Last updated | 2020-02-28 | ||
| Replaces | draft-wang-teas-ccdr | ||
| Stream | Internet Engineering Task Force (IETF) | ||
| Formats | plain text html xml pdf htmlized bibtex | ||
| Reviews | |||
| Stream | WG state | Submitted to IESG for Publication | |
| Document shepherd | Lou Berger | ||
| Shepherd write-up | Show (last changed 2019-07-10) | ||
| IESG | IESG state | RFC 8735 (Informational) | |
| Action Holders |
(None)
|
||
| Consensus Boilerplate | Yes | ||
| Telechat date | |||
| Responsible AD | Deborah Brungard | ||
| Send notices to | Lou Berger <lberger@labn.net> | ||
| IANA | IANA review state | Version Changed - Review Needed | |
| IANA action state | No IANA Actions | ||
Internet Engineering Task Force (IETF) A. Wang
Request for Comments: 8735 China Telecom
Category: Informational X. Huang
ISSN: 2070-1721 C. Kou
BUPT
Z. Li
China Mobile
P. Mi
Huawei Technologies
February 2020
Scenarios and Simulation Results of PCE in a Native IP Network
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
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc8735.
Copyright Notice
Copyright (c) 2020 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
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the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction
2. Terminology
3. CCDR Scenarios
3.1. QoS Assurance for Hybrid Cloud-Based Application
3.2. Link Utilization Maximization
3.3. Traffic Engineering for Multi-domain
3.4. Network Temporal Congestion Elimination
4. CCDR Simulation
4.1. Case Study for CCDR Algorithm
4.2. Topology Simulation
4.3. Traffic Matrix Simulation
4.4. CCDR End-to-End Path Optimization
4.5. Network Temporal Congestion Elimination
5. CCDR Deployment Consideration
6. Security Considerations
7. IANA Considerations
8. References
8.1. Normative References
8.2. Informative References
Acknowledgements
Contributors
Authors' Addresses
1. Introduction
A service provider network is composed of thousands of routers that
run distributed protocols to exchange 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; 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 TE networks, but
it introduces an MPLS network along with related technology, which
would be an overlay of the IP network. MPLS-TE technology is often
used for Label Switched Path (LSP) protection and setting up complex
paths 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 central control
technology, but it requires the underlying network, especially the
provider edge router, to do an in-depth label push and pop action
while adding 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 document 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 to the data-plane behavior on the router. Using knowledge of
the Border Gateway Protocol (BGP) 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
underlying 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 document is the base document of the following two documents:
the universal solution document, which is suitable for intra-domain
and inter-domain TE scenario, is described in [PCE-NATIVE-IP]; and
the related protocol extension contents is described in
[PCEP-NATIVE-IP-EXT].
2. Terminology
In this document, PCE is used as defined in [RFC5440]. The following
terms are used as described here:
BRAS: Broadband Remote Access Server
CD: Congestion Degree
CR: Core Router
CCDR: Centralized Control Dynamic Routing
E2E: End to End
IDC: Internet Data Center
MAN: Metro Area Network
QoS: Quality of Service
SR: Service Router
TE: Traffic Engineering
UID: Utilization Increment Degree
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
while keeping core business within their private cloud. The
communication between the private and public cloud sites spans the
WAN. 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 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 an
application requires 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 real traffic
information, in order 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 due to
subscribers under BRAS often using the network at night and the
leased line users under SR often using the network during the
daytime. The link between BRAS/SR and CR must satisfy the maximum
traffic volume between them, respectively, which causes these links
to often be underutilized.
+--------+
| 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.
+---+ +---+
|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 autonomous system
(AS), and determining what the main cause of the congested link(s)
is. 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 the CCDR framework.
3.4. Network Temporal Congestion Elimination
In more general situations, there is 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 operation capabilities and increase satisfaction for
customers. CCDR is also suitable for such scenarios, as the
controller can schedule traffic out of the congested links, lowering
their utilization 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.
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 eight 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 10 Mb/s. The flow rate of f1 is 1 Mb/s and the flow rate
of f2 is 2 Mb/s. The threshold of the link in congestion is 90%.
If the OSPF protocol, which adopts Dijkstra's algorithm (IS-IS is
similar because it also uses Dijkstra's algorithm), is applied in the
network, the two flows from node 1 to node 8 can only use the OSPF
path (p1: 1->2->3->8). This is because Dijkstra's algorithm mainly
considers the 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 the OSPF path. Moreover, the
path p2 is also better than the path (p3: 1->2->4->7->8) for flow f1.
However, f2 will not select the same path since it will cause new
congestion in the link (6, 7). As a result, f2 will select the path
(p3: 1->2->4->7->8).
+----+ 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/IS-IS)
+----+ 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.
+----+
/|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 two and thirty, and
the total number of links is more than 20,000. Each link has a
congestion threshold, which can be arbitrarily set, for example, to
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 the 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 entails finding the best path, which
is the lowest in metric value, as well as having utilization far
below the congestion threshold for each link of the path. 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.
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 suggest that real-world deployments will also experience
significant improvement in UID results.
+-----------------------------------------------------------+
| * * * *|
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 Results 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
twelve links among the total 20,000 exceed the threshold, and all the
CD values are less than 3%. Thus, after scheduling 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 Results with Congestion Elimination
It is clear that by 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
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 underlying 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 along with no changes being
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 underlying
network topology dynamically, for example, via Border Gateway
Protocol - Link State (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 a
change to the underlying network in a matter of minutes. More agile
requirements would need to increase the sample rate of the underlying
network and decrease the detection and notification interval of the
underlying network. The methods of gathering this information as
well as decreasing its latency are out of the scope of this document.
6. Security Considerations
This document considers mainly the integration of distributed
protocols and the central control capability of a PCE. While it can
certainly simplify the management of a network in various traffic-
engineering scenarios as described in this document, the centralized
control also brings a new point that may be easily attacked.
Solutions for CCDR scenarios need to consider protection of the PCE
and communication with the underlying devices.
[RFC5440] and [RFC8253] provide additional information.
The control priority and interaction process should also be carefully
designed for the combination of the distributed protocol and central
control. Generally, the central control instructions should have
higher priority than the forwarding actions determined by the
distributed protocol. When communication between PCE and the
underlying devices is disrupted, the distributed protocol should take
control of the underlying network. [PCE-NATIVE-IP] provides more
considerations corresponding to the solution.
7. IANA Considerations
This document has no IANA actions.
8. References
8.1. 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>.
8.2. Informative References
[PCE-NATIVE-IP]
Wang, A., Zhao, Q., Khasanov, B., and H. Chen, "PCE in
Native IP Network", Work in Progress, Internet-Draft,
draft-ietf-teas-pce-native-ip-05, 9 January 2020,
<https://tools.ietf.org/html/draft-ietf-teas-pce-native-
ip-05>.
[PCEP-NATIVE-IP-EXT]
Wang, A., Khasanov, B., Fang, S., and C. Zhu, "PCEP
Extension for Native IP Network", Work in Progress,
Internet-Draft, draft-ietf-pce-pcep-extension-native-ip-
05, 17 February 2020, <https://tools.ietf.org/html/draft-
ietf-pce-pcep-extension-native-ip-05>.
[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",
DOI 10.1109/ACCESS.2019.2902610, IEEE Access 18526773,
March 2019,
<https://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>.
Acknowledgements
The authors would like to thank Deborah Brungard, Adrian Farrel,
Huaimo Chen, Vishnu Beeram, and Lou Berger for their support and
comments on this document.
Thanks to Benjamin Kaduk for his careful review and valuable
suggestions on this document. Also, thanks to Roman Danyliw, Alvaro
Retana, and Éric Vyncke for their reviews and comments.
Contributors
Lu Huang contributed to the content of this document.
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
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