TEAS Working Group                                               A.Wang
Internet Draft                                            China Telecom
                                                         Xiaohong Huang
                                                             Caixia Kou
                                                               Lu Huang
                                                           China Mobile
                                                             Penghui Mi
                                                        Tencent Company

Intended status: Experimental Track                     January 19, 2018
Expires: July 18, 2018

                 CCDR Scenario, Simulation and Suggestion

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   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 ................................................ 3
   2. Conventions used in this document............................ 4
   3. CCDR Scenarios. ............................................. 4
      3.1. Qos Assurance for Hybrid Cloud-based Application.........4
      3.2. Increase link utilization based on tidal phenomena...... 5
      3.3. Traffic engineering for IDC/MAN asymmetric link......... 6
      3.4. Network temporal congestion elimination. ............... 6
   4. CCDR Simulation. ............................................ 7
      4.1. Topology Simulation..................................... 7
      4.2. Traffic Matrix Simulation............................... 7
      4.3. CCDR End-to-End Path Optimization ...................... 8
      4.4. Network temporal congestion elimination ................ 9

   5. CCDR Deployment Consideration............................... 10
   6. Security Considerations..................................... 10
   7. IANA Considerations ........................................ 10
   8. Conclusions ................................................ 10
   9. References ................................................. 10
      9.1. Normative References................................... 10
      9.2. Informative References................................. 11
   10. Contributors: ............................................. 12
   11. Acknowledgments ........................................... 12

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
   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

   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. Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in RFC 2119 [RFC2119].

3. CCDR Scenarios.

   The following sections describe some scenarios that the CCDR
   architecture 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 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
   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.

3.2. 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

                           |   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

3.3. 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.

                 +---+                +---+
                 +-|-|       |        +-|-+
                   |     ---------|     |
                   |     ----|----|     |
                   |         |          |
                 +-|--       |        ----+
                 +---|                |---+

            Fig.4 TE within Complex Multi-Domain topology

3.4. 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

4. CCDR Simulation.

   The following sections describe the topology, traffic matrix, end-
   to-end path optimization and congestion elimination in CCDR

4.1. 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|   |      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 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 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

   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.

        |                *                               *    *    *|
       60|                *                             * * *  *    *|
         |*      *       **     * *         *   *   *  ** * *  * * **|
         |*   * ** *   * **   *** **  *   * **  * * *  ** * *  *** **|
         |* * * ** *  ** **   *** *** **  **** ** ***  **** ** *** **|
       40|* * * ***** ** ***  *** *** **  **** ** *** ***** ****** **|
   UID(%)|* * ******* ** ***  *** ******* **** ** *** ***** *********|
         |*** ******* ** **** *********** *********** ***************|
         |******************* *********** *********** ***************|
       20|******************* ***************************************|
         |******************* ***************************************|
        0    100   200   300   400   500   600   700   800   900  1000
        |                                                           |
       60|                                                           |
         |                                                           |
         |                                                           |
         |                                                           |
       40|                                                           |

   UID(%)|                                                           |
         |                                                           |
         |                                                           |
       20|                                                           |
         |                                                          *|
         |                                     *                    *|
         |        *         *  *    *       *  **                 * *|
        0    100   200   300   400   500   600   700   800   900  1000
                              Flow Number
           Fig.6 Simulation result with congestion elimination

4.4. 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.

                       Before congestion elimination
          |                *                            ** *   ** ** *|
        20|                *                     *      **** * ** ** *|
          |*      *       **     * **       **  **** * ***** *********|
          |*   *  * *   * **** ****** *  ** *** **********************|
        15|* * * ** *  ** **** ********* *****************************|
          |* * ******  ******* ********* *****************************|
    CD(%) |* ********* ******* ***************************************|
        10|* ********* ***********************************************|
          |*********** ***********************************************|
             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 most complex optimal path
   computation in centrally manner based on the underlay network
   topology and the real time traffic.

   [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.

6. Security Considerations


7. IANA Considerations


8. Conclusions


9. References

9.1. Normative References

   [RFC4655] Farrel, A., Vasseur, J.-P., and J. Ash, "A Path

             Computation Element (PCE)-Based Architecture", RFC
             4655, August 2006,<http://www.rfc-editor.org/info/rfc4655>.

    [RFC5440]Vasseur, JP., Ed., and JL. Le Roux, Ed., "Path

             Computation Element (PCE) Communication Protocol

             (PCEP)", RFC 5440, March 2009,


    [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

9.2. 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



   [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-

10. Contributors:

   Tingting Yuan
   Beijing University of Posts and Telecommunications

   Qiong Sun

   Xiaoyan Wei
   China Telecom Shanghai Company

   Dingyuan Hu
   Beijing University of Posts and Telecommunications

11. Acknowledgments


Authors' Addresses

   Aijun Wang
   China Telecom
   Beiqijia Town, Changping District

   Email: wangaj.bri@chinatelecom.cn

   Xiaohong Huang
   Beijing University of Posts and Telecommunications
   No.10 Xitucheng Road, Haidian District

   EMail: huangxh@bupt.edu.cn
   Caixia Kou
   Beijing University of Posts and Telecommunications
   No.10 Xitucheng Road, Haidian District

   Lu Huang
   China Mobile
   32 Xuanwumen West Ave, Xicheng District
   Beijing 100053
   Email: hlisname@yahoo.com

   Penghui Mi
   Tencent Building, Kejizhongyi Avenue,
   Hi-techPark, Nanshan District,Shenzhen 518057, P.R.China

   Email kevinmi@tencent.com