TEAS Working Group A. Wang
Internet-Draft China Telecom
Intended status: Informational X. Huang
Expires: February 27, 2020 C. Kou
BUPT
Z. Li
China Mobile
P. Mi
Huawei Technologies
August 26, 2019
Scenarios and Simulation Results of PCE in Native IP Network
draft-ietf-teas-native-ip-scenarios-07
Abstract
The requirements for the End to End(E2E) performance assurance are
emerging within the service provider network, there are various
solutions to meet such demands, but there is no one solution can meet
these requirements in native IP network, especially one universal
solution can cover intra-domain and inter-domain scenarios together.
This document describes the scenarios and simulation results for Path
Computation Elements (PCE) in native IP network, which integrates the
advantage of distributed protocols, and the power of centrally
control technologies to provide one feasible traffic engineering
solution in various complex scenarios for the service provider.
Status of This Memo
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on February 27, 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. Topology Simulation . . . . . . . . . . . . . . . . . . . 7
4.2. Traffic Matrix Simulation. . . . . . . . . . . . . . . . 8
4.3. CCDR End-to-End Path Optimization . . . . . . . . . . . . 8
4.4. Network Temporal Congestion Elimination . . . . . . . . . 10
5. CCDR Deployment Consideration. . . . . . . . . . . . . . . . 11
6. Security Considerations . . . . . . . . . . . . . . . . . . . 12
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 12
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 12
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
10.1. Normative References . . . . . . . . . . . . . . . . . . 12
10.2. Informative References . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
Service provider network is composed of thousands of routers that run
distributed protocol to exchange the reachability information between
them. The path for the destination network is mainly calculated and
controlled by the distributed protocols. These distributed protocols
are robust enough to support the current evolution of Internet but
have some difficulties when application requires the E2E performance
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assurance, or in the situation that the service provider wants to
maximize the link utilization within their network.
Multiprotocol Label Switching (MPLS) for Traffic Engineering(TE)
technology [RFC3209]is one solution for finely planned network but it
mainly applies to the MPLS network. Even for MPLS network, the MPLS-
TE technology is often used for Label Switched Path (LSP) protection.
It is seldom used for dynamic performance assurance requirements
within real time traffic network.
Segment Routing [RFC8402] is another solution that integrates some
advantages of distributed protocol and centrally control mode, but it
requires the underlying network, especially the provider edge router
to do label push and pop action in-depth, and need complex mechanism
for coexisting with the Non-Segment Routing network. Additionally,
it can only maneuver the E2E path for MPLS and IPv6 traffic via
different mechanisms.
Deterministic Networking (DetNet)[RFC8578] describes use cases for
diverse industries that have a common need for "deterministic flows",
which can provide guaranteed bandwidth, bounded latency, and other
properties germane to the transport of time-sensitive data. The use
cases focus mainly on the industrial critical applications within one
centrally controlled network and are out of scope of this draft.
This draft describes scenarios in native IP network that the
Centrally Control Dynamic Routing (CCDR) framework can easily solve,
without the change of the data plane behaviour on the router. It
also gives the path optimization simulation results to illustrate the
applicability of CCDR framework.
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: Central Control Dynamic Routing
o E2E: End to End
o IDC: Internet Data Center
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o MAN: Metro Area Network
o QoS: Quality of Service
o SR: Service Router
o UID: Utilization Increment Degree
o WAN: Wide Area Network
3. CCDR Scenarios.
The following sections describe some scenarios that the CCDR
framework is suitable for deployment.
3.1. QoS Assurance for Hybrid Cloud-based Application.
With the emerge of cloud computing technologies, enterprises are
putting more and more services on the public oriented cloud
environment, but keep 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 changes from time to time. Enterprise applications just
want to exploit the network capabilities to assure the E2E Quality of
Service(QoS) performance on demand.
CCDR, which integrates the merits of distributed protocol and the
power of centrally 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
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By default, the traffic path between the private and public cloud
site will be determined by the distributed control network. When
applications require the E2E QoS assurance, it can send these
requirements to PCE, let 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. The proposed
solution can refer the draft [I-D.ietf-teas-pce-native-ip].
Section 4 describes the detail simulation process and the result.
3.2. Link Utilization Maximization
Network topology within Metro Area Network (MAN) is generally in star
mode as illustrated in Figure 2, with different devices connect
different customer types. The traffic from these customers is often
in tidal pattern that the links between the Core Router(CR)/Broadband
Remote Access Server(BRAS) and CR/Service Router(SR) will experience
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 uplink between BRAS/SR
and CR must satisfy the maximum traffic volume between them
respectively and this causes these links often in underutilization
situation.
+--------+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS| |SR| |BRAS| |SR|
+----+ +--+ +----+ +--+
Figure 2: Star-mode Network Topology within MAN
If we consider to connect the BRAS/SR with local link loop (which is
more cheaper), and control the MAN with the CCDR framework, we can
exploit the tidal phenomena between BRAS/CR and SR/CR links, maximize
the links (which is more expensive) utilization of them .
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+-------+
----- PCE |
| +-------+
+----|---+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS-----SR| |BRAS-----SR|
+----+ +--+ +----+ +--+
Figure 3: Link Utilization Maximization via CCDR
3.3. Traffic Engineering for Multi-Domain
The service provider networks are often comprised of different
domains, interconnected with each other,form very complex topology
that illustrated in Figure.4. Due to the traffic pattern to/from MAN
and IDC, the utilization of links between them are often asymmetric.
It is almost impossible to balance the utilization of these links via
the distributed protocol, but this unbalance phenomenon can be
overcome via the CCDR framework.
+---+ +---+
|MAN|-----------------IDC|
+-|-| | +-|-+
| ---------| |
------|BackBone|------
| ----|----| |
| | |
+-|-- | ----+
|IDC|----------------|MAN|
+---| |---+
Figure 4: Traffic Engineering for Complex Multi-Domain Topology
Solution for this scenario requires the gather of NetFlow
information, analysis the source/destination AS of them and determine
what is the main cause of the congested link. After this, the
operator can use the multi external Border Gateway Protocol(eBGP)
sessions described in [I-D.ietf-teas-pce-native-ip]to schedule the
traffic among different domains.
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3.4. Network Temporal Congestion Elimination.
In more general situation, there are often temporal congestions
within the service provider's network. Such congestion phenomena
often appear repeatedly and if the service provider has some methods
to mitigate it, it will certainly increase the degree of satisfaction
for their customers. CCDR is also suitable for such scenario in such
manner that the distributed protocol process most of the traffic
forwarding and the controller schedule some traffic out of the
congestion links to lower the utilization of them. Section 4
describes the simulation process and results about such scenario.
4. CCDR Simulation.
The following sections describe the topology, traffic matrix, E2E
path optimization and congestion elimination in CCDR applied
scenarios.
4.1. Topology Simulation
The network topology mainly contains nodes and links information.
Nodes used in 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 5 is a topology
example of 4 core nodes and 5 edge nodes. In CCDR simulation, 100
core nodes and 400 edge nodes are generated.
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+----+
/|Edge|\
| +----+ |
| |
| |
+----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+
+----+ +----+ +----+ |
/ | \ / | |
+----+ | \ / | |
|Edge| | X | |
+----+ | / \ | |
\ | / \ | |
+----+ +----+ +----+ |
|Edge|----|Core|-----|Core| |
+----+ +----+ +----+ |
| | |
| +------\ +----+
| ---|Edge|
+-----------------/ +----+
Figure 5: 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 its congestion threshold.
4.2. 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 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.
4.3. 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, PCE within
CCDR framework combines the shortest path algorithm with penalty
theory of classical optimization and graph theory.
Given background traffic matrix which is unscheduled, when a set of
new flows comes into the network, the E2E path optimization finds the
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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 6. The first graph in
Figure 6 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 decreasing in UID relative to the path chosen based on OSPF.
+-----------------------------------------------------------+
| * * * *|
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 6: Simulation Result with Congestion Elimination
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4.4. Network Temporal Congestion Elimination
Different degree of network congestions are simulated. The
Congestion Degree (CD) is defined as the link utilization beyond its
threshold.
The CCDR congestion elimination performance is shown in Figure 7.
The first graph is the CD distribution before the process of
congestion elimination. The average CD of all congested links is
more than 10%. The second graph shown in Figure 7 is the CD
distribution after 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 in
congestion paths, the degree of network congestion is greatly
eliminated and the network utilization is in balance.
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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 7: Simulation Result with Congestion Elimination
5. CCDR Deployment Consideration.
With the above CCDR scenarios and simulation results, we can know it
is necessary and feasible to find one general solution to cope with
various complex situations for the complex optimal path computation
in centrally manner in native IP network based on the underlay
network topology and the real time traffic.
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[I-D.ietf-teas-pce-native-ip] gives the solution for above scenarios,
such thoughts can be extended to cover requirements in other
situations in future.
6. Security Considerations
This document considers mainly the integration of distributed
protocol and the central control capability of PCE. It certainly can
ease the management of network in various traffic engineering
scenarios described in this document, but the central control manner
also bring the new point that may be easily attacked. Solutions for
CCDR scenarios should keep these in mind and consider more for the
protection of PCEand their communication with the underlay devices,
as that described in document [RFC5440] and [RFC8253]
7. IANA Considerations
This document does not require any IANA actions.
8. Contributors
Lu Huang contributes 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 supports and
comments on this draft.
10. References
10.1. Normative References
[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-03 (work in progress), April 2019.
[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>.
[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
[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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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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