draft-ietf-teas-native-ip-scenarios-00.txt   draft-ietf-teas-native-ip-scenarios-01.txt 
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 February 13, 2018 TEAS Working Group A. Wang
Expires: August 12, 2018 Internet-Draft China Telecom
Intended status: Experimental X. Huang
Expires: December 28, 2018 C. Kou
BUPT
Z. Li
China Mobile
L. Huang
P. Mi
Huawei Technologies
June 26, 2018
CCDR Scenario, Simulation and Suggestion CCDR Scenario, Simulation and Suggestion
draft-ietf-teas-native-ip-scenarios-00.txt draft-ietf-teas-native-ip-scenarios-01
Abstract Abstract
This document describes the scenarios, simulation and suggestions This document describes the scenarios, simulation and suggestions for
for the "Centrally Control Dynamic Routing (CCDR)" architecture, the "Centrally Control Dynamic Routing (CCDR)" architecture, which
which integrates the merit of traditional distributed protocols integrates the merit of traditional distributed protocols (IGP/BGP),
(IGP/BGP), and the power of centrally control technologies (PCE/SDN) and the power of centrally control technologies (PCE/SDN) to provide
to provide one feasible traffic engineering solution in various one feasible traffic engineering solution in various complex
complex scenarios for the service provider. scenarios for the service provider.
Traditional MPLS-TE solution is mainly used in static network Traditional MPLS-TE solution is mainly used in static network
planning scenario and is difficult to meet the QoS assurance planning scenario and is difficult to meet the QoS assurance
requirements in real-time traffic network. With the emerge of SDN requirements in real-time traffic network. With the emerge of SDN
concept and related technologies, it is possible to simplify the concept and related technologies, it is possible to simplify the
complexity of distributed control protocol, utilize the global view complexity of distributed control protocol, utilize the global view
of network condition, give more efficient solution for traffic of network condition, give more efficient solution for traffic
engineering in various complex scenarios. engineering in various complex scenarios.
Status of this Memo Status of This Memo
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This Internet-Draft will expire on December 28, 2018.
This Internet-Draft will expire on August 12, 2018.
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Table of Contents Table of Contents
1. Introduction ................................................ 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. CCDR Scenarios. ............................................. 3 2. Conventions used in this document . . . . . . . . . . . . . . 3
2.1. Qos Assurance for Hybrid Cloud-based Application........ 3 3. CCDR Scenarios. . . . . . . . . . . . . . . . . . . . . . . . 3
2.2. Increase link utilization based on tidal phenomena...... 4 3.1. Qos Assurance for Hybrid Cloud-based Application. . . . . 3
2.3. Traffic engineering for IDC/MAN asymmetric link......... 5 3.2. Increase link utilization based on tidal phenomena. . . . 4
2.4. Network temporal congestion elimination. ............... 6 3.3. Traffic engineering for IDC/MAN asymmetric link . . . . . 5
3. CCDR Simulation. ............................................ 6 3.4. Network temporal congestion elimination. . . . . . . . . 6
3.1. Topology Simulation..................................... 6 4. CCDR Simulation. . . . . . . . . . . . . . . . . . . . . . . 6
3.2. Traffic Matrix Simulation............................... 7 4.1. Topology Simulation . . . . . . . . . . . . . . . . . . . 6
3.3. CCDR End-to-End Path Optimization ...................... 7 4.2. Traffic Matrix Simulation. . . . . . . . . . . . . . . . 7
3.4. Network temporal congestion elimination ................ 8 4.3. CCDR End-to-End Path Optimization . . . . . . . . . . . . 7
4. CCDR Deployment Consideration................................ 9 4.4. Network temporal congestion elimination . . . . . . . . . 9
5. Security Considerations..................................... 10 5. CCDR Deployment Consideration. . . . . . . . . . . . . . . . 10
6. IANA Considerations ........................................ 10 6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
7. Conclusions ................................................ 10 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
8. References ................................................. 10 8. Normative References . . . . . . . . . . . . . . . . . . . . 11
8.1. Normative References................................... 10 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
8.2. Informative References................................. 10
9. Contributors: .............................................. 11
10. Acknowledgments ........................................... 11
1. Introduction 1. Introduction
Internet network is composed mainly tens of thousands of routers that Internet network is composed mainly tens of thousands of routers that
run distributed protocol to exchange the reachability information run distributed protocol to exchange the reachability information
between them. The path for the destination network is mainly between them. The path for the destination network is mainly
calculated and controlled by the traditional IGP protocols. These calculated and controlled by the traditional IGP protocols. These
distributed protocols are robust enough to support the current distributed protocols are robust enough to support the current
evolution of Internet but has some difficulties when the application evolution of Internet but has some difficulties when the application
requires the end-to-end QoS performance, or the service provider requires the end-to-end QoS performance, or the service provider
wants to maximize the links utilization within their network. wants to maximize the links utilization within their network.
MPLS-TE technology is one perfect solution for the finely planned 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 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 solve the dynamic QoS assurance requirements within real time traffic
network. network.
SR(Segment Routing) is another prominent solution that integrates SR(Segment Routing) is another prominent solution that integrates
some merits of traditional distributed protocol and the advantages of some merits of traditional distributed protocol and the advantages of
centrally control mode, but it requires the underlying network, centrally control mode, but it requires the underlying network,
especially the provider edge router to do label push and pop action 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- 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 SR network. Finally, it can only maneuver the end-to-end path for
MPLS and IPv6 traffic via different mechanisms. MPLS and IPv6 traffic via different mechanisms.
The advantage of MPLS is mainly for traffic isolation, such as the The advantage of MPLS is mainly for traffic isolation, such as the
L2/L3 VPN service deployments, but most of the current application L2/L3 VPN service deployments, but most of the current application
requirements are only for high performances end-to-end QoS assurance. requirements are only for high performances end-to-end QoS assurance.
Without the help of centrally control architecture, the service Without the help of centrally control architecture, the service
provider almost can't make such SLA guarantees upon the real time provider almost can't make such SLA guarantees upon the real time
traffic situation. traffic situation.
This draft gives some scenarios that the centrally control dynamic This draft gives some scenarios that the centrally control dynamic
routing (CCDR) architecture can easily solve, without adding more routing (CCDR) architecture can easily solve, without adding more
extra burdening on the router. It also gives the PCE algorithm extra burdening on the router. It also gives the PCE algorithm
results under the similar topology, traffic pattern and network size results under the similar topology, traffic pattern and network size
to illustrate the applicability of CCDR architecture. Finally, it to illustrate the applicability of CCDR architecture. Finally, it
gives some suggestions for the implementation and deployment of CCDR. gives some suggestions for the implementation and deployment of CCDR.
2. CCDR Scenarios. 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 The following sections describe some scenarios that the CCDR
architecture is suitable for deployment. architecture is suitable for deployment.
2.1. Qos Assurance for Hybrid Cloud-based Application. 3.1. Qos Assurance for Hybrid Cloud-based Application.
With the emerge of cloud computing technologies, enterprises are With the emerge of cloud computing technologies, enterprises are
putting more and more services on the public oriented service putting more and more services on the public oriented service
infrastructure, but keep still some core services within their infrastructure, but keep still some core services within their
network. The bandwidth requirements between the private cloud and network. The bandwidth requirements between the private cloud and
the public cloud are occasionally and the background traffic between the public cloud are occasionally and the background traffic between
these two sites varied from time to time. Enterprise cloud these two sites varied from time to time. Enterprise cloud
applications just want to invoke the network capabilities to make applications just want to invoke the network capabilities to make the
the end-to-end QoS assurance on demand. Otherwise, the traffic end-to-end QoS assurance on demand. Otherwise, the traffic should be
should be controlled by the distributed protocol. controlled by the distributed protocol.
CCDR, which integrates the merits of distributed protocol and the CCDR, which integrates the merits of distributed protocol and the
power of centrally control, is suitable for this scenario. The power of centrally control, is suitable for this scenario. The
possible solution architecture is illustrated below: possible solution architecture is illustrated below:
+------------------------+ +------------------------+
| Cloud Based Application| | Cloud Based Application|
+------------------------+ +------------------------+
| |
+-----------+ +-----------+
| PCE | | PCE |
+-----------+ +-----------+
| |
| |
//--------------\\ //--------------\\
///// \\\\\ ///// \\\\\
Private Cloud Site || Distributed |Public Cloud Site Private Cloud Site || Distributed |Public Cloud Site
| Control Network | | Control Network |
\\\\\ ///// \\\\\ /////
\\--------------// \\--------------//
Fig.1 Hybrid Cloud Communication Scenario Fig.1 Hybrid Cloud Communication Scenario
By default, the traffic path between the private cloud site and By default, the traffic path between the private cloud site and
public cloud site will be determined by the distributed control public cloud site will be determined by the distributed control
network. When some applications require the end-to-end QoS assurance, network. When some applications require the end-to-end QoS
it can send these requirements to PCE, let PCE compute one e2e path assurance, it can send these requirements to PCE, let PCE compute one
which is based on the underlying network topology and the real e2e path which is based on the underlying network topology and the
traffic information, to accommodate the application's bandwidth real traffic information, to accommodate the application's QoS
requirements. The proposed solution can refer the draft [draft-wang- requirements. The proposed solution can refer the draft
teas-pce-native-ip]. Section 4 describes the detail simulation [I-D.ietf-teas-pce-native-ip]. Section 4 describes the detail
process and the results. simulation process and the results.
2.2. Increase link utilization based on tidal phenomena. 3.2. Increase link utilization based on tidal phenomena.
Currently, the network topology within MAN is generally in star mode Currently, the network topology within MAN is generally in star mode
as illustrated in Fig.2, with the different devices connect as illustrated in Fig.2, with the different devices connect different
different customer types. The traffic pattern of these customers customer types. The traffic pattern of these customers demonstrates
demonstrates some tidal phenomena that the links between the CR/BRAS some tidal phenomena that the links between the CR/BRAS and CR/SR
and CR/SR will experience congestion in different periods because will experience congestion in different periods because the
the subscribers under BRAS often use the network at night and the subscribers under BRAS often use the network at night and the
dedicated line users under SR often use the network during the dedicated line users under SR often use the network during the
daytime. The uplink between BRAS/SR and CR must satisfy the maximum daytime. The uplink between BRAS/SR and CR must satisfy the maximum
traffic pattern between them and this causes the links traffic pattern between them and this causes the links
underutilization. underutilization.
+--------+ +--------+
| CR | | CR |
+----|---+ +----|---+
| |
--------|--------|-------| --------|--------|-------|
| | | | | | | |
+--|-+ +-|- +--|-+ +-|+ +--|-+ +-|- +--|-+ +-|+
|BRAS| |SR| |BRAS| |SR| |BRAS| |SR| |BRAS| |SR|
+----+ +--+ +----+ +--+ +----+ +--+ +----+ +--+
Fig.2 STAR-style network topology within MAN Fig.2 STAR-style network topology within MAN
If we can consider link the BRAS/SR with local loop, and control the 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 MAN with the CCDR architecture, we can exploit the tidal phenomena
between BRAS/CR and SR/CR links, increase the efficiency of them. between BRAS/CR and SR/CR links, increase the efficiency of them.
+-------+ +-------+
----- PCE | ----- PCE |
| +-------+ | +-------+
+----|---+ +----|---+
| CR | | CR |
+----|---+ +----|---+
| |
--------|--------|-------| --------|--------|-------|
| | | | | | | |
+--|-+ +-|- +--|-+ +-|+ +--|-+ +-|- +--|-+ +-|+
|BRAS-----SR| |BRAS-----SR| |BRAS-----SR| |BRAS-----SR|
+----+ +--+ +----+ +--+ +----+ +--+ +----+ +--+
Fig.3 Increase the link utilization via CCDR Fig.3 Increase the link utilization via CCDR
2.3. Traffic engineering for IDC/MAN asymmetric link 3.3. Traffic engineering for IDC/MAN asymmetric link
The operator's networks are often comprised by tens of different The operator's networks are often comprised by tens of different
domains, interconnected with each other, form very complex topology domains, interconnected with each other, form very complex topology
that illustrated in Fig.4. Due to the traffic pattern to/from MAN 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 and IDC, the links between them are often in asymmetric style. It is
almost impossible to balance the utilization of these links via the almost impossible to balance the utilization of these links via the
distributed protocol, but this unbalance phenomenon can be overcome distributed protocol, but this unbalance phenomenon can be overcome
via the CCDR architecture. via the CCDR architecture.
+---+ +---+ +---+ +---+
|MAN|-----------------IDC| |MAN|-----------------IDC|
+-|-| | +-|-+ +-|-| | +-|-+
| ---------| | | ---------| |
------|BackBone|------ ------|BackBone|------
| ----|----| | | ----|----| |
| | | | | |
+-|-- | ----+ +-|-- | ----+
|IDC|----------------|MAN| |IDC|----------------|MAN|
+---| |---+ +---| |---+
Fig.4 TE within Complex Multi-Domain topology Fig.4 TE within Complex Multi-Domain topology
2.4. Network temporal congestion elimination. 3.4. Network temporal congestion elimination.
In more general situation, there are often temporal congestion In more general situation, there are often temporal congestion
periods within part of the service provider's network. Such periods within part of the service provider's network. Such
congestion phenomena will appear repeatedly and if the service congestion phenomena will appear repeatedly and if the service
provider has some methods to mitigate it, it will certainly increase provider has some methods to mitigate it, it will certainly increase
the satisfaction degree of their customer. CCDR is also suitable for the satisfaction degree of their customer. CCDR is also suitable for
such scenario that the traditional distributed protocol will process such scenario that the traditional distributed protocol will process
most of the traffic forwarding and the controller will schedule some most of the traffic forwarding and the controller will schedule some
traffic out of the congestion links to lower the utilization of them. traffic out of the congestion links to lower the utilization of them.
Section 4 describes the simulation process and results about such Section 4 describes the simulation process and results about such
scenario. scenario.
3. CCDR Simulation. 4. CCDR Simulation.
The following sections describe the topology, traffic matrix, end- The following sections describe the topology, traffic matrix, end-to-
to-end path optimization and congestion elimination in CCDR end path optimization and congestion elimination in CCDR simulation.
simulation.
3.1. Topology Simulation. 4.1. Topology Simulation
The network topology mainly contains nodes and links information. The network topology mainly contains nodes and links information.
Nodes used in simulation have two types: core nodes and edge nodes. 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 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 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 example of 4 core nodes and 5 edge nodes. In CCDR simulation, 100
core nodes and 400 edge nodes are generated. core nodes and 400 edge nodes are generated.
+----+ +----+
/|Edge|\ /|Edge|\
| +----+ | | +----+ |
| | | |
| | | |
+----+ +----+ +----+ +----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+ |Edge|----|Core|-----|Core|---------+
+----+ +----+ +----+ | +----+ +----+ +----+ |
/ | \ / | | / | \ / | |
+----+ | \ / | | +----+ | \ / | |
|Edge| | X | | |Edge| | X | |
+----+ | / \ | | +----+ | / \ | |
\ | / \ | | \ | / \ | |
+----+ +----+ +----+ | +----+ +----+ +----+ |
|Edge|----|Core|-----|Core| | |Edge|----|Core|-----|Core| |
+----+ +----+ +----+ | +----+ +----+ +----+ |
| | | | | |
| +------\ +----+ | +------\ +----+
| ---|Edge| | ---|Edge|
+-----------------/ +----+ +-----------------/ +----+
Fig.5 Topology of simulation Fig.5 Topology of simulation
The number of links connecting one edge node to the set of core The number of links connecting one edge node to the set of core nodes
nodes is randomly between 2 to 30, and the total number of links is is randomly between 2 to 30, and the total number of links is more
more than 20000. Each link has its congestion threshold. than 20000. Each link has its congestion threshold.
3.2. Traffic Matrix Simulation. 4.2. Traffic Matrix Simulation.
The traffic matrix is generated based on the link capacity of The traffic matrix is generated based on the link capacity of
topology. It can result in many kinds of situations, such as topology. It can result in many kinds of situations, such as
congestion, mild congestion and non-congestion. congestion, mild congestion and non-congestion.
In CCDR simulation, the traffic matrix is 500*500. About 20% links In CCDR simulation, the traffic matrix is 500*500. About 20% links
are overloaded when the Open Shortest Path First (OSPF) protocol is are overloaded when the Open Shortest Path First (OSPF) protocol is
used in the network. used in the network.
3.3. CCDR End-to-End Path Optimization 4.3. CCDR End-to-End Path Optimization
The CCDR end-to-end path optimization is to find the best end-to-end 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 path which is the lowest in metric value and each link of the path is
is far below link's threshold. Based on the current state of the far below link's threshold. Based on the current state of the
network, PCE within CCDR architecture combines the shortest path network, PCE within CCDR architecture combines the shortest path
algorithm with penalty theory of classical optimization and graph algorithm with penalty theory of classical optimization and graph
theory. theory.
Given background traffic matrix which is unscheduled, when a set of Given background traffic matrix which is unscheduled, when a set of
new flows comes into the network, the end-to-end path optimization new flows comes into the network, the end-to-end path optimization
finds the optimal paths for them. The selected paths bring the least finds the optimal paths for them. The selected paths bring the least
congestion degree to the network. congestion degree to the network.
The link utilization increment degree(UID) when the new flows are 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 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- 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 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 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- 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. catching decreasing in UID relative to the path chosen based on OSPF.
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| * * * *| | * * * *|
60| * * * * * *| 60| * * * * * *|
|* * ** * * * * * ** * * * * **| |* * ** * * * * * ** * * * * **|
|* * ** * * ** *** ** * * ** * * * ** * * *** **| |* * ** * * ** *** ** * * ** * * * ** * * *** **|
|* * * ** * ** ** *** *** ** **** ** *** **** ** *** **| |* * * ** * ** ** *** *** ** **** ** *** **** ** *** **|
40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **| 40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **|
UID(%)|* * ******* ** *** *** ******* **** ** *** ***** *********| UID(%)|* * ******* ** *** *** ******* **** ** *** ***** *********|
|*** ******* ** **** *********** *********** ***************| |*** ******* ** **** *********** *********** ***************|
|******************* *********** *********** ***************| |******************* *********** *********** ***************|
20|******************* ***************************************| 20|******************* ***************************************|
|******************* ***************************************| |******************* ***************************************|
|***********************************************************| |***********************************************************|
|***********************************************************| |***********************************************************|
0+-----------------------------------------------------------+ 0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| | | |
60| | 60| |
| | | |
| | | |
| | | |
40| | 40| |
UID(%)| | UID(%)| |
| | | |
| | | |
20| | 20| |
| *| | *|
| * *| | * *|
| * * * * * ** * *| | * * * * * ** * *|
0+-----------------------------------------------------------+ 0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000
Flow Number Flow Number
Fig.6 Simulation result with congestion elimination Fig.6 Simulation result with congestion elimination
3.4. Network temporal congestion elimination 4.4. Network temporal congestion elimination
Different degree of network congestion is simulated. The congestion Different degree of network congestion is simulated. The congestion
degree (CD) is defined as the link utilization beyond its threshold. degree (CD) is defined as the link utilization beyond its threshold.
The CCDR congestion elimination performance is shown in Fig.7. The The CCDR congestion elimination performance is shown in Fig.7. The
first graph is the congestion degree before the process of first graph is the congestion degree before the process of congestion
congestion elimination. The average CD of all congested links is elimination. The average CD of all congested links is more than 10%.
more than 10%. The second graph shown in Fig.7 is the congestion The second graph shown in Fig.7 is the congestion degree after
degree after congestion elimination process. It shows only 12 links congestion elimination process. It shows only 12 links among totally
among totally 20000 links exceed the threshold, and all the 20000 links exceed the threshold, and all the congestion degree is
congestion degree is less than 3%. Thus, after schedule of the less than 3%. Thus, after schedule of the traffic in congestion
traffic in congestion paths, the degree of network congestion is paths, the degree of network congestion is greatly eliminated and the
greatly eliminated and the network utilization is in balance. network utilization is in balance.
Before congestion elimination Before congestion elimination
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| * ** * ** ** *| | * ** * ** ** *|
20| * * **** * ** ** *| 20| * * **** * ** ** *|
|* * ** * ** ** **** * ***** *********| |* * ** * ** ** **** * ***** *********|
|* * * * * **** ****** * ** *** **********************| |* * * * * **** ****** * ** *** **********************|
15|* * * ** * ** **** ********* *****************************| 15|* * * ** * ** **** ********* *****************************|
|* * ****** ******* ********* *****************************| |* * ****** ******* ********* *****************************|
CD(%) |* ********* ******* ***************************************| CD(%) |* ********* ******* ***************************************|
10|* ********* ***********************************************| 10|* ********* ***********************************************|
|*********** ***********************************************| |*********** ***********************************************|
|***********************************************************| |***********************************************************|
5|***********************************************************| 5|***********************************************************|
|***********************************************************| |***********************************************************|
|***********************************************************| |***********************************************************|
0+-----------------------------------------------------------+ 0+-----------------------------------------------------------+
0 0.5 1 1.5 2 0 0.5 1 1.5 2
After congestion elimination After congestion elimination
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| | | |
20| | 20| |
| | | |
| | | |
15| | 15| |
| | | |
CD(%) | | CD(%) | |
10| | 10| |
| | | |
| | | |
5 | | 5 | |
| | | |
| * ** * * * ** * ** * | | * ** * * * ** * ** * |
0 +-----------------------------------------------------------+ 0 +-----------------------------------------------------------+
0 0.5 1 1.5 2 0 0.5 1 1.5 2
Link Number(*10000) Link Number(*10000)
Fig.7 Simulation result with congestion elimination Fig.7 Simulation result with congestion elimination
4. CCDR Deployment Consideration. 5. CCDR Deployment Consideration.
With the above CCDR scenarios and simulation results, we can know it With the above CCDR scenarios and simulation results, we can know it
is necessary and feasible to find one general solution to cope with is necessary and feasible to find one general solution to cope with
various complex situations for the most complex optimal path various complex situations for the most complex optimal path
computation in centrally manner based on the underlay network computation in centrally manner based on the underlay network
topology and the real time traffic. topology and the real time traffic.
[draft-wang-teas-native-ip] gives the principle solution for above [I-D.ietf-teas-pce-native-ip] gives the principle solution for above
scenarios, such thoughts can be extended to cover requirements that scenarios, such thoughts can be extended to cover requirements that
are more concretes in future. are more concretes in future.
5. Security Considerations 6. 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
[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] This document considers mainly the integration of traditional
distributed protocol and the global view of central control. It
certainly can ease the management of network in various traffic-
engineering scenarios described in this document, but the central
control manner may also bring the new point be easily attacked.
Solutions for CCDR scenarios should keep these in mind and consider
more for the protection of SDN controller and their communication
with the underlay devices, which described in document 1 and
[RFC8253]
Aijun Wang, Boris Khasanov et al. "PCEP Extension for Native IP 7. IANA Considerations
Network" https://datatracker.ietf.org/doc/draft-wang-pce-extension-
native-ip/
9. Contributors: This document does not require any IANA actions.
Tingting Yuan 8. Normative References
Beijing University of Posts and Telecommunications
yuantingting@bupt.edu.cn
Qiong Sun [I-D.ietf-teas-pce-native-ip]
sunqiong.bri@chinatelecom.cn Wang, A., Zhao, Q., Khasanov, B., and K. Mi, "PCE in
Native IP Network", draft-ietf-teas-pce-native-ip-00 (work
in progress), February 2018.
Xiaoyan Wei [I-D.ietf-teas-pcecc-use-cases]
China Telecom Shanghai Company Zhao, Q., Li, Z., Khasanov, B., Ke, Z., Fang, L., Zhou,
weixiaoyan@189.cn C., Communications, T., and A. Rachitskiy, "The Use Cases
for Using PCE as the Central Controller(PCECC) of LSPs",
draft-ietf-teas-pcecc-use-cases-01 (work in progress), May
2017.
Dingyuan Hu [RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation
Beijing University of Posts and Telecommunications Element (PCE) Communication Protocol (PCEP)", RFC 5440,
hdy@bupt.edu.cn DOI 10.17487/RFC5440, March 2009,
<https://www.rfc-editor.org/info/rfc5440>.
10. Acknowledgments [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>.
TBD [RFC8283] Farrel, A., Ed., Zhao, Q., Ed., Li, Z., and C. Zhou, "An
Architecture for Use of PCE and the PCE Communication
Protocol (PCEP) in a Network with Central Control",
RFC 8283, DOI 10.17487/RFC8283, December 2017,
<https://www.rfc-editor.org/info/rfc8283>.
Authors' Addresses Authors' Addresses
Aijun Wang Aijun Wang
China Telecom China Telecom
Beiqijia Town, Changping District Beiqijia Town, Changping District
Beijing,China Beijing, Beijing 102209
China
Email: wangaj.bri@chinatelecom.cn Email: wangaj.bri@chinatelecom.cn
Xiaohong Huang Xiaohong Huang
Beijing University of Posts and Telecommunications Beijing University of Posts and Telecommunications
No.10 Xitucheng Road, Haidian District No.10 Xitucheng Road, Haidian District
Beijing,China Beijing
China
EMail: huangxh@bupt.edu.cn Email: huangxh@bupt.edu.cn
Caixia Kou Caixia Kou
Beijing University of Posts and Telecommunications Beijing University of Posts and Telecommunications
No.10 Xitucheng Road, Haidian District No.10 Xitucheng Road, Haidian District
Beijing,China Beijing
koucx@lsec.cc.ac.cn China
Lu Huang Email: koucx@lsec.cc.ac.cn
Zhenqiang Li
China Mobile China Mobile
32 Xuanwumen West Ave, Xicheng District 32 Xuanwumen West Ave, Xicheng District
Beijing 100053 Beijing 100053
China
Email: li_zhenqiang@hotmail.com
Lu Huang
Huawei Technologies
Unit 7 NO 8.XiBinHe Road,YongDingMen
Beijing, Dongcheng District 100077
China China
Email: hlisname@yahoo.com Email: hlisname@yahoo.com
Penghui Mi Penghui Mi
Tencent Huawei Technologies
Tencent Building, Kejizhongyi Avenue, Tower C of Bldg.2, Cloud Park, No.2013 of Xuegang Road
Hi-techPark, Nanshan District,Shenzhen 518057, P.R.China Shenzhen, Bantian,Longgang District 518129
China
Email kevinmi@tencent.com Email: mipenghui@huawei.com
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