TEAS Working Group A. Wang
Internet-Draft China Telecom
Intended status: Informational X. Huang
Expires: December 5, 2019 C. Kou
BUPT
Z. Li
China Mobile
P. Mi
Huawei Technologies
June 3, 2019
Scenario, Simulation and Suggestion of PCE in Native IP Network
draft-ietf-teas-native-ip-scenarios-04
Abstract
This document describes the scenarios, simulation and suggestions for
PCE in native IP network, which integrates the merit of distributed
protocols (IGP/BGP), and the power of centrally control technologies
(PCE/SDN) to provide one feasible traffic engineering solution in
various complex scenarios for the service provider.
Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 3
3. CCDR Scenarios. . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. QoS Assurance for Hybrid Cloud-based Application. . . . . 3
3.2. Link Utilization Maximization . . . . . . . . . . . . . . 4
3.3. Traffic Engineering for Multi-Domain . . . . . . . . . . 5
3.4. Network Temporal Congestion Elimination. . . . . . . . . 6
4. CCDR Simulation. . . . . . . . . . . . . . . . . . . . . . . 6
4.1. Topology Simulation . . . . . . . . . . . . . . . . . . . 6
4.2. Traffic Matrix Simulation. . . . . . . . . . . . . . . . 7
4.3. CCDR End-to-End Path Optimization . . . . . . . . . . . . 7
4.4. Network Temporal Congestion Elimination . . . . . . . . . 9
5. CCDR Deployment Consideration. . . . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 11
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 11
10. Normative References . . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
Service provider network is composed 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 IGP/BGP protocols. These distributed protocols are
robust enough to support the current evolution of Internet but have
some difficulties when application requires the end-to-end QoS
performance, or in the situation that the service provider wants to
maximize the link utilization within their network.
MPLS-TE technology is one solution for finely planned network but it
will put heavy burden on the routers when we use it to meet the
dynamic QoS assurance requirements within real time traffic network.
SR(Segment Routing) is another solution that integrates some merits
of distributed protocol and the advantages of 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
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mechanic for coexisting with the Non-SR network. Additionally, it
can only maneuver the end-to-end path for MPLS and IPv6 traffic via
different mechanisms.
DetNet[RFC8578] describes use cases for diverse industries that have
in common a 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 corporate network and are out of scope of this draft. And
as described in [I-D.ietf-detnet-dp-sol-ip], the solution for the
DetNet use cases requires the update of the network data plane, which
is not easy being deployed within the service provider network and is
out of scope that described in [I-D.ietf-teas-pce-native-ip]
This draft describes scenarios that the centrally control dynamic
routing (CCDR) framework can easily solve, without adding more extra
burden on the router. It also gives the path optimization simulation
results to illustrate the applicability of CCDR framework. Finally,
it gives some suggestions for the implementation and deployment of
CCDR.
2. Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
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 will span the 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 end-to-end 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:
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+------------------------+
| Cloud Based Application|
+------------------------+
|
+-----------+
| PCE |
+-----------+
|
|
//--------------\\
///// \\\\\
Private Cloud Site || Distributed |Public Cloud Site
| Control Network |
\\\\\ /////
\\--------------//
Fig.1 Hybrid Cloud Communication Scenario
By default, the traffic path between the private and public cloud
site will be determined by the distributed control network. When
applications require the end-to-end 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 MAN is generally in star mode as illustrated
in Fig.2, with different devices connect different customer types.
The traffic from these customers is often in tidal pattern that the
links between the CR/BRAS and CR/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.
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+--------+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS| |SR| |BRAS| |SR|
+----+ +--+ +----+ +--+
Fig.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 .
+-------+
----- PCE |
| +-------+
+----|---+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS-----SR| |BRAS-----SR|
+----+ +--+ +----+ +--+
Fig.3 Link Utilization Maximization via CCDR
3.3. Traffic Engineering for Multi-Domain
Operator's networks are often comprised by different domains,
interconnected with each other,form very complex topology that
illustrated in Fig.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.
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+---+ +---+
|MAN|-----------------IDC|
+-|-| | +-|-+
| ---------| |
------|BackBone|------
| ----|----| |
| | |
+-|-- | ----+
|IDC|----------------|MAN|
+---| |---+
Fig.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
which pair is the main cause of the congested link. After this, the
operator can use the multi eBGP sessions described in
[I-D.ietf-teas-pce-native-ip]to schedule the traffic among different
domains.
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, end-to-
end 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. Fig.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|
+-----------------/ +----+
Fig.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 end-to-end 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 end-to-end path optimization
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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 Fig.6. The first graph in Fig.6
is the UID with OSPF and the second graph is the UID with CCDR end-
to-end 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 end-to-end 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
Fig.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 Fig.7. The
first graph is the congestion degree before the process of congestion
elimination. The average CD of all congested links is more than 10%.
The second graph shown in Fig.7 is the congestion degree after
congestion elimination process. It shows only 12 links among totally
20000 links exceed the threshold, and all the congestion degree is
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)
Fig.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 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/SDN. 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 PCE/SDN controller and 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. Normative References
[I-D.ietf-detnet-dp-sol-ip]
Korhonen, J. and B. Varga, "DetNet IP Data Plane
Encapsulation", draft-ietf-detnet-dp-sol-ip-02 (work in
progress), March 2019.
[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-03 (work in progress), March
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-03 (work in progress), April 2019.
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[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[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>.
[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: wangaj.bri@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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