TEAS Working Group                                               A. Wang
Internet-Draft                                             China Telecom
Intended status: Experimental                                   X. Huang
Expires: December 28, 2018 April 24, 2019                                           C. Kou
                                                                    BUPT
                                                                   Z. Li
                                                            China Mobile
                                                                L. Huang
                                                                   P. Mi
                                                     Huawei Technologies
                                                           June 26,
                                                        October 21, 2018

                CCDR

    Scenario, Simulation and Suggestion
                 draft-ietf-teas-native-ip-scenarios-01 of PCE in Native IP Network
                 draft-ietf-teas-native-ip-scenarios-02

Abstract

   This document describes the scenarios, simulation and suggestions for
   the "Centrally Control Dynamic Routing (CCDR)" architecture,
   PCE in native IP network, 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.

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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.  Increase link utilization based on tidal phenomena.  Link Utilization Maximization . . . . . . . . . . . . . .   4
     3.3.  Traffic engineering Engineering for IDC/MAN asymmetric link 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 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

   Internet

   Service provider 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 IGP/BGP protocols.  These
   distributed protocols are robust enough to support the current
   evolution of Internet but has have some difficulties when the application
   requires the end-to-end QoS performance, or in the situation that 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 routers when we use it to
   solve meet 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
   mechanics for co-exist with the Non-
   SR Non-SR network.  Finally,  Aditionally, 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 describes scenarios that the centrally control dynamic
   routing (CCDR) architecture framework can easily solve, without adding more extra
   burdening on the router.  It also gives the PCE algorithm path optimization
   simulation results under the similar topology, traffic pattern and network size to illustrate the applicability of CCDR architecture. 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
   architecture
   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 service
   infrastructure, cloud
   environment, but keep still some core services business within their
   network. private cloud.  The bandwidth requirements
   communication between the private cloud and
   the public cloud will span the WAN
   network.  The bandwidth requirements between them are occasionally variable and
   the background traffic between these two sites varied changes from time to
   time.  Enterprise cloud applications just want to invoke exploit the network
   capabilities to make assure the end-to-end QoS assurance performance 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 framework 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 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 results. result.

3.2.  Increase link utilization based on tidal phenomena.

   Currently, the network  Link Utilization Maximization

   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 from these customers demonstrates
   some is often in tidal phenomena pattern that the
   links between the CR/BRAS and CR/SR will experience congestion in
   different periods 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 volume between them respectively and this
   causes the these links
   underutilization. often in underutilization situation.

                              +--------+
                              |   CR   |
                              +----|---+
                                   |
                       --------|--------|-------|
                       |       |        |       |
                    +--|-+   +-|-    +--|-+   +-|+
                    |BRAS|   |SR|    |BRAS|   |SR|
                    +----+   +--+    +----+   +--+

              Fig.2 STAR-style network topology Star-mode Network Topology within MAN

   If we can consider link to connect the BRAS/SR with local loop, link loop (which is
   more cheaper), and control the MAN with the CCDR architecture, framework, we can
   exploit the tidal phenomena between BRAS/CR and SR/CR links, increase maximize
   the efficiency links (which is more expensive) utilization of them. them .

                                       +-------+
                                   -----  PCE  |
                                   |   +-------+
                              +----|---+
                              |   CR   |
                              +----|---+
                                   |
                       --------|--------|-------|
                       |       |        |       |
                    +--|-+   +-|-    +--|-+   +-|+
                    |BRAS-----SR|    |BRAS-----SR|
                    +----+   +--+    +----+   +--+

                   Fig.3 Increase the link utilization Link Utilization Maximization via CCDR

3.3.  Traffic engineering Engineering for IDC/MAN asymmetric link

   The operator's Multi-Domain

   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 utilization of links between them are often in asymmetric style. 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 architecture. framework.

                    +---+                +---+
                    |MAN|-----------------IDC|
                    +-|-|       |        +-|-+
                      |     ---------|     |
                      ------|BackBone|------
                      |     ----|----|     |
                      |         |          |
                    +-|--       |        ----+
                    |IDC|----------------|MAN|
                    +---|                |---+

        Fig.4 TE within Traffic Engineering for Complex Multi-Domain topology 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 congestion
   periods congestions
   within part of the service provider's network.  Such congestion phenomena will
   often appear repeatedly and if the service provider has some methods
   to mitigate it, it will certainly increase the satisfaction degree of satisfaction
   for their customer. customers.  CCDR is also suitable for such scenario in such
   manner 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.

4.  CCDR Simulation.

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

4.1.  Topology Simulation

   The network topology mainly contains nodes and links information.
   Nodes used in simulation have two types: core nodes node and edge nodes. 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.

                                     +----+
                                    /|Edge|\
                                   | +----+ |
                                   |        |
                                   |        |
                     +----+    +----+     +----+
                     |Edge|----|Core|-----|Core|---------+
                     +----+    +----+     +----+         |
                             /  |    \   /   |           |
                       +----+   |     \ /    |           |
                       |Edge|   |      X     |           |
                       +----+   |     / \    |           |
                             \  |    /   \   |           |
                     +----+    +----+     +----+         |
                     |Edge|----|Core|-----|Core|         |
                     +----+    +----+     +----+         |
                                 |          |            |
                                 |          +------\   +----+
                                 |                  ---|Edge|
                                 +-----------------/   +----+

                        Fig.5 Topology of simulation 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 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 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
   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+-----------------------------------------------------------+
          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 Result with congestion elimination Congestion Elimination

4.4.  Network temporal congestion elimination Temporal Congestion Elimination

   Different degree of network congestion is 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 schedule scheduling 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|* ********* ***********************************************|
           |*********** ***********************************************|
           |***********************************************************|
          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 Result with congestion elimination 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.

   [I-D.ietf-teas-pce-native-ip] gives the principle solution for above scenarios,
   such thoughts can be extended to cover requirements that
   are more concretes in other
   situations in future.

6.  Security Considerations

   This document considers mainly the integration of traditional distributed
   protocol and the global view of central control. control capability of PCE/SDN.  It certainly
   can ease the management of network in various traffic-
   engineering traffic-engineering
   scenarios described in this document, but the central control manner may
   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 SDN PCE/SDN controller and their communication with the
   underlay devices, which as that described in document 1 [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-teas-pce-native-ip]
              Wang, A., Zhao, Q., Khasanov, B., and K. Chen, H., Mi, P.,
              Mallya, R., and S. Peng, "PCE in Native IP Network", draft-ietf-teas-pce-native-ip-00
              draft-ietf-teas-pce-native-ip-01 (work in progress), February June
              2018.

   [I-D.ietf-teas-pcecc-use-cases]
              Zhao, Q., Li, Z., Khasanov, B., Ke, Z., Fang, L., Zhou,
              C., Communications, T., and A. Rachitskiy, "The Use Cases

   [RFC2119]  Bradner, S., "Key words for Using PCE as the Central Controller(PCECC) of LSPs",
              draft-ietf-teas-pcecc-use-cases-01 (work use in progress), May
              2017. 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>.

   [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

   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

   Zhenqiang Li
   China Mobile
   32 Xuanwumen West Ave, Xicheng District
   Beijing  100053
   China

   Email: li_zhenqiang@hotmail.com
   Lu Huang
   Huawei Technologies
   Unit 7 NO 8.XiBinHe Road,YongDingMen
   Beijing, Dongcheng District  100077
   China

   Email: hlisname@yahoo.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