Internet DRAFT - draft-wang-teas-ccdr
draft-wang-teas-ccdr
TEAS Working Group A.Wang
Internet Draft China Telecom
Xiaohong Huang
BUPT
Caixia Kou
BUPT
Lu Huang
China Mobile
Penghui Mi
Tencent Company
Intended status: Experimental Track January 25, 2018
Expires: July 24, 2018
CCDR Scenario, Simulation and Suggestion
draft-wang-teas-ccdr-05.txt
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Internet-DraftCCDR Scenario, Simulation and Suggestion January 25, 2018
Abstract
This document describes the scenarios, simulation and suggestions
for the "Centrally Control Dynamic Routing (CCDR)" architecture,
which integrates the merit of traditional 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.
Traditional MPLS-TE solution is mainly used in static network
planning scenario and is difficult to meet the QoS assurance
requirements in real-time traffic network. With the emerge of SDN
concept and related technologies, it is possible to simplify the
complexity of distributed control protocol, utilize the global view
of network condition, give more efficient solution for traffic
engineering in various complex scenarios.
Table of Contents
1. Introduction ................................................ 2
2. CCDR Scenarios. ............................................. 3
2.1. Qos Assurance for Hybrid Cloud-based Application........ 3
2.2. Increase link utilization based on tidal phenomena...... 4
2.3. Traffic engineering for IDC/MAN asymmetric link......... 5
2.4. Network temporal congestion elimination. ............... 6
3. CCDR Simulation. ............................................ 6
3.1. Topology Simulation..................................... 6
3.2. Traffic Matrix Simulation............................... 7
3.3. CCDR End-to-End Path Optimization ...................... 7
3.4. Network temporal congestion elimination ................ 8
4. CCDR Deployment Consideration................................ 9
5. Security Considerations..................................... 10
6. IANA Considerations ........................................ 10
7. Conclusions ................................................ 10
8. References ................................................. 10
8.1. Normative References................................... 10
8.2. Informative References................................. 10
9. Contributors: .............................................. 11
10. Acknowledgments ........................................... 11
1. Introduction
Internet network is composed mainly tens 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 traditional IGP protocols. These
distributed protocols are robust enough to support the current
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evolution of Internet but has some difficulties when the application
requires the end-to-end QoS performance, or the service provider
wants to maximize the links utilization within their network.
MPLS-TE technology is one perfect solution for the finely planned
network but it will put heavy burden on the router when we use it to
solve the dynamic QoS assurance requirements within real time traffic
network.
SR(Segment Routing) is another prominent solution that integrates
some merits of traditional 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 some complex solutions for co-exist with the Non-
SR network. Finally, it can only maneuver the end-to-end path for
MPLS and IPv6 traffic via different mechanisms.
The advantage of MPLS is mainly for traffic isolation, such as the
L2/L3 VPN service deployments, but most of the current application
requirements are only for high performances end-to-end QoS assurance.
Without the help of centrally control architecture, the service
provider almost can't make such SLA guarantees upon the real time
traffic situation.
This draft gives some scenarios that the centrally control dynamic
routing (CCDR) architecture can easily solve, without adding more
extra burdening on the router. It also gives the PCE algorithm
results under the similar topology, traffic pattern and network size
to illustrate the applicability of CCDR architecture. Finally, it
gives some suggestions for the implementation and deployment of CCDR.
2. CCDR Scenarios.
The following sections describe some scenarios that the CCDR
architecture is suitable for deployment.
2.1. 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 service
infrastructure, but keep still some core services within their
network. The bandwidth requirements between the private cloud and
the public cloud are occasionally and the background traffic between
these two sites varied from time to time. Enterprise cloud
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applications just want to invoke the network capabilities to make
the end-to-end QoS assurance on demand. Otherwise, the traffic
should be controlled by the distributed protocol.
CCDR, which integrates the merits of distributed protocol and the
power of centrally control, is suitable for this scenario. The
possible solution architecture is illustrated below:
+------------------------+
| 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 cloud site and
public cloud site will be determined by the distributed control
network. When some 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 bandwidth
requirements. The proposed solution can refer the draft [draft-wang-
teas-pce-native-ip]. Section 4 describes the detail simulation
process and the results.
2.2. Increase link utilization based on tidal phenomena.
Currently, the network topology within MAN is generally in star mode
as illustrated in Fig.2, with the different devices connect
different customer types. The traffic pattern of these customers
demonstrates some tidal phenomena 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 pattern between them and this causes the links
underutilization.
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+--------+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS| |SR| |BRAS| |SR|
+----+ +--+ +----+ +--+
Fig.2 STAR-style network topology within MAN
If we can consider link the BRAS/SR with local loop, and control the
MAN with the CCDR architecture, we can exploit the tidal phenomena
between BRAS/CR and SR/CR links, increase the efficiency of them.
+-------+
----- PCE |
| +-------+
+----|---+
| CR |
+----|---+
|
--------|--------|-------|
| | | |
+--|-+ +-|- +--|-+ +-|+
|BRAS-----SR| |BRAS-----SR|
+----+ +--+ +----+ +--+
Fig.3 Increase the link utilization via CCDR
2.3. Traffic engineering for IDC/MAN asymmetric link
The operator's networks are often comprised by tens of 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 links between them are often in asymmetric style. 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 architecture.
+---+ +---+
|MAN|-----------------IDC|
+-|-| | +-|-+
| ---------| |
------|BackBone|------
| ----|----| |
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| | |
+-|-- | ----+
|IDC|----------------|MAN|
+---| |---+
Fig.4 TE within Complex Multi-Domain topology
2.4. Network temporal congestion elimination.
In more general situation, there are often temporal congestion
periods within part of the service provider's network. Such
congestion phenomena will appear repeatedly and if the service
provider has some methods to mitigate it, it will certainly increase
the satisfaction degree of their customer. CCDR is also suitable for
such scenario that the traditional distributed protocol will process
most of the traffic forwarding and the controller will 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.
3. CCDR Simulation.
The following sections describe the topology, traffic matrix, end-
to-end path optimization and congestion elimination in CCDR
simulation.
3.1. Topology Simulation.
The network topology mainly contains nodes and links information.
Nodes used in simulation have two types: core nodes and edge nodes.
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.
+----+
/|Edge|\
| +----+ |
| |
| |
+----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+
+----+ +----+ +----+ |
/ | \ / | |
+----+ | \ / | |
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|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.
3.2. 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 traffic matrix is 500*500. About 20% links
are overloaded when the Open Shortest Path First (OSPF) protocol is
used in the network.
3.3. CCDR End-to-End Path Optimization
The CCDR end-to-end path optimization is to find the best end-to-end
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 architecture 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
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 graph one 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.
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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
Fig.6 Simulation result with congestion elimination
3.4. Network temporal congestion elimination
Different degree of network congestion is 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 schedule 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
4. 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 most complex optimal path
computation in centrally manner based on the underlay network
topology and the real time traffic.
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[draft-wang-teas-native-ip] gives the principle solution for above
scenarios, such thoughts can be extended to cover requirements that
are more concretes in future.
5. Security Considerations
TBD
6. IANA Considerations
TBD
7. Conclusions
TBD
8. References
8.1. Normative References
[RFC5440]Vasseur, JP., Ed., and JL. Le Roux, Ed., "Path
Computation Element (PCE) Communication Protocol
(PCEP)", RFC 5440, March 2009,
<http://www.rfc-editor.org/info/rfc5440>.
[RFC8283] A.Farrel, Q.Zhao et al.," An Architecture for Use of PCE
and the PCE Communication Protocol (PCEP) in a Network with Central
Control", [RFC8283], December 2017
8.2. Informative References
[I-D. draft-ietf-teas-pcecc-use-cases]
Quintin Zhao, Robin Li, Boris Khasanov et al. "The Use Cases for
Using PCE as the Central Controller(PCECC) of LSPs
https://tools.ietf.org/html/draft-ietf-teas-pcecc-use-cases-00
March,2017
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[I-D. draft-wang-teas-pce-native-ip]
A.Wang, Quintin Zhao, Boris Khasanov, Penghui Mi,Raghavendra Mallya,
Shaofu Peng "PCE in Native IP Network"
https://tools.ietf.org/html/draft-wang-teas-pce-native-ip-03 March
13, 2017
[I-D. draft-wang-pcep-extension for native IP]
Aijun Wang, Boris Khasanov et al. "PCEP Extension for Native IP
Network" https://datatracker.ietf.org/doc/draft-wang-pce-extension-
native-ip/
9. Contributors:
Tingting Yuan
Beijing University of Posts and Telecommunications
yuantingting@bupt.edu.cn
Qiong Sun
sunqiong.bri@chinatelecom.cn
Xiaoyan Wei
China Telecom Shanghai Company
weixiaoyan@189.cn
Dingyuan Hu
Beijing University of Posts and Telecommunications
hdy@bupt.edu.cn
10. Acknowledgments
TBD
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Authors' Addresses
Aijun Wang
China Telecom
Beiqijia Town, Changping District
Beijing,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
koucx@lsec.cc.ac.cn
Lu Huang
China Mobile
32 Xuanwumen West Ave, Xicheng District
Beijing 100053
China
Email: hlisname@yahoo.com
Penghui Mi
Tencent
Tencent Building, Kejizhongyi Avenue,
Hi-techPark, Nanshan District,Shenzhen 518057, P.R.China
Email kevinmi@tencent.com
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