TEAS Working Group                                               A. Wang
Internet-Draft                                             China Telecom
Intended status: Informational                                  X. Huang
Expires: February 27, March 2, 2020                                            C. Kou
                                                                    BUPT
                                                                   Z. Li
                                                            China Mobile
                                                                   P. Mi
                                                     Huawei Technologies
                                                         August 26, 30, 2019

      Scenarios and Simulation Results of PCE in Native IP Network
                 draft-ietf-teas-native-ip-scenarios-07
                 draft-ietf-teas-native-ip-scenarios-08

Abstract

   The requirements

   Requirements for providing the End to End(E2E) performance assurance
   are emerging within the service provider network, network.  While there are
   various
   solutions to meet such demands, but technology solutions, there is no one solution which can meet
   fulfill these requirements in for a native IP network, especially one network.  One universal
   (E2E) solution which can cover both intra-domain and inter-domain
   scenarios together. is needed.

   One feasible E2E traffic engineering solution is the use of a Path
   Computation Elements (PCE) in a native IP network.  This document
   describes the various complex scenarios and simulation results for Path
   Computation Elements (PCE) when
   applying a PCE in a native IP network, which network.  This solution, referred to as
   Centralized Control Dynamic Routing (CCDR), integrates the advantage
   of using distributed protocols, protocols and the power of centrally a centralized control technologies to provide one feasible traffic engineering
   solution in various complex scenarios for the service provider.
   technology.

Status of This Memo

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  CCDR Scenarios. . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  QoS Assurance for Hybrid Cloud-based Application. . . . .   4
     3.2.  Link Utilization Maximization . . . . . . . . . . . . . .   5
     3.3.  Traffic Engineering for Multi-Domain  . . . . . . . . . .   6
     3.4.  Network Temporal Congestion Elimination.  . . . . . . . .   7
   4.  CCDR Simulation.  . . . . . . . . . . . . . . . . . . . . . .   7
     4.1.  Topology Simulation . . . . . . . . . . . . . . . . . . .   7
     4.2.  Traffic Matrix Simulation.  . . . . . . . . . . . . . . .   8
     4.3.  CCDR End-to-End Path Optimization . . . . . . . . . . . .   8
     4.4.  Network Temporal Congestion Elimination . . . . . . . . .  10
   5.  CCDR Deployment Consideration.  . . . . . . . . . . . . . . .  11
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   8.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  12
   9.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  12
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  12
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  12
     10.2.  Informative References . . . . . . . . . . . . . . . . .  13  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

1.  Introduction

   Service

   A service provider network is composed of thousands of routers that
   run distributed protocol protocols to exchange the reachability information between
   them. information.
   The path for the destination network is mainly calculated calculated, and
   controlled
   controlled, by the distributed protocols.  These distributed
   protocols are robust enough to support the current evolution of Internet most applications, but have
   some difficulties when application requires supporting the complexities needed for traffic
   engineering applications, e.g.  E2E performance assurance, or in the situation that the service provider wants to
   maximize
   maximizing the link utilization within their an IP network.

   Multiprotocol Label Switching (MPLS) for using Traffic Engineering(TE) Engineering (TE)
   technology [RFC3209]is (MPLS-TE)[RFC3209]is one solution for finely planned traffic engineering
   network but it
   mainly applies to the MPLS network.  Even for introduces an MPLS network, network and related technology
   which would be an overlay of the MPLS-
   TE IP network.  MPLS-TE technology is
   often used for Label Switched Path (LSP) protection. protection and complex path
   set-up within a domain.

   It is seldom used has not been widely deployed for meeting E2E (especially in inter-
   domain) dynamic performance assurance requirements
   within real time traffic for an IP network.

   Segment Routing [RFC8402] is another solution that integrates some
   advantages of using a distributed protocol and a centrally control mode,
   technology, but it requires the underlying network, especially the
   provider edge router router, to do a label push and pop action in-depth, and need complex mechanism
   for
   adds complexity, when coexisting with the Non-Segment Routing
   network.  Additionally, it can only maneuver the E2E path paths for MPLS
   and IPv6 traffic via different mechanisms.

   Deterministic Networking (DetNet)[RFC8578] describes use cases is another possible
   solution.  It is primarily focused on providing bounded latency for
   diverse industries that have a common need for "deterministic flows",
   which can provide guaranteed bandwidth, bounded latency,
   flow and other
   properties germane to introduces additional requirements on the transport of time-sensitive data. domain edge
   router.  The current DetNet scope is within one domain.  The use
   cases focus mainly on defined in this document do not require the industrial critical applications within one
   centrally controlled network and are out of scope additional
   complexity of this draft. deterministic properties and so differ from the DetNet
   use cases.

   This draft describes scenarios in for a native IP network that the
   Centrally a
   Centralized Control Dynamic Routing (CCDR) framework can easily
   solve, without the requiring a change of the data plane behaviour on the
   router.  It also gives the provides path optimization simulation results to
   illustrate the applicability of the CCDR framework.

2.  Terminology

   This document uses the following terms defined in [RFC5440]: PCE.

   The following terms are defined in this document:

   o  BRAS: Broadband Remote Access Server

   o  CD: Congestion Degree

   o  CR: Core Router

   o  CCDR: Central Centralized Control Dynamic Routing
   o  E2E: End to End

   o  IDC: Internet Data Center

   o  MAN: Metro Area Network

   o  QoS: Quality of Service

   o  SR: Service Router

   o  UID: Utilization Increment Degree

   o  WAN: Wide Area Network

3.  CCDR Scenarios.

   The following sections describe some various deployment scenarios that for
   applying the CCDR
   framework is suitable for deployment. framework.

3.1.  QoS Assurance for Hybrid Cloud-based Application.

   With the emerge emergence of cloud computing technologies, enterprises are
   putting more and more services on the a public oriented cloud
   environment, but keep keeping core business within their private cloud.
   The communication between the private and public cloud sites will
   span the Wide Area Network (WAN) network.  The bandwidth requirements
   between them are variable and the background traffic between these
   two sites changes from time to varies over time.  Enterprise applications just
   want to exploit the network capabilities to assure require
   assurance of the E2E Quality of Service(QoS) performance on demand. demand
   for variable bandwidth services.

   CCDR, which integrates the merits of distributed protocol protocols and the
   power of centrally centralized control, is suitable for this scenario.  The
   possible solution framework is illustrated below:

                            +------------------------+
                            | Cloud Based Application|
                            +------------------------+
                                        |
                                  +-----------+
                                  |    PCE    |
                                  +-----------+
                                        |
                                        |
                               //--------------\\
                          /////                  \\\\\
     Private Cloud Site ||       Distributed          |Public Cloud Site
                         |       Control Network      |
                          \\\\\                  /////
                               \\--------------//

                  Figure 1: Hybrid Cloud Communication Scenario

   By default, the traffic path between the private and public cloud
   site will be determined by the distributed control network.  When
   applications require the E2E QoS assurance, it can send these
   requirements to the PCE, and let the 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 of this document describes the detail simulation process and the result. results for this
   use case.

3.2.  Link Utilization Maximization

   Network topology within a Metro Area Network (MAN) is generally in a
   star mode as illustrated in Figure 2, with different devices connect
   connected to different customer types.  The traffic from these
   customers is often in a tidal pattern that pattern, with the links between the
   Core Router(CR)/Broadband Remote Access Server(BRAS) and CR/Service Router(SR) will experience
   Router(SR), experiencing congestion in different periods, because the
   subscribers under BRAS BRAS, often use the network at night night, and the
   dedicated line users under SR SR, often use the network during the
   daytime.  The uplink between BRAS/SR and CR must satisfy the maximum
   traffic volume between them respectively and this causes these links
   often in underutilization
   situation. to be under-utilized.

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

              Figure 2: Star-mode Network Topology within MAN

   If we consider to connect connecting the BRAS/SR with a local link loop (which
   is
   more cheaper), usually lower cost), and control the overall MAN topology with the
   CCDR framework, we can exploit the tidal phenomena between BRAS/CR the BRAS/
   CR and SR/CR links, maximize maximizing the links (which is more expensive) utilization of them . these links (which
   are usually higher cost).

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

                   Figure 3: Link Utilization Maximization via CCDR

3.3.  Traffic Engineering for Multi-Domain

   The service

   Service provider networks are often comprised of different domains,
   interconnected with each other,form other,forming a very complex topology
   that as
   illustrated in Figure.4. Figure 4.  Due to the traffic pattern to/from the MAN
   and IDC, the utilization of the links between them are often
   asymmetric.  It is almost impossible to balance the utilization of
   these links via
   the a distributed protocol, but this unbalance phenomenon can be
   overcome via utilizing the CCDR framework.

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

        Figure 4: Traffic Engineering for Complex Multi-Domain Topology

   Solution

   A solution for this scenario requires the gather gathering of NetFlow
   information, analysis of the source/destination AS of them AS, and determine determining
   what is the main cause of the congested link.  After this, the
   operator can use the multi external Border Gateway Protocol(eBGP) sessions described in [I-D.ietf-teas-pce-native-ip]to
   to schedule the traffic among the different domains.

3.4.  Network Temporal Congestion Elimination.

   In more general situation, situations, there are often temporal congestions
   within the service provider's network.  Such congestion phenomena
   often appear repeatedly repeatedly, and if the service provider has some methods to
   mitigate it, it will certainly improve their network operations
   capabilities and increase the degree of satisfaction for their customers.  CCDR is
   also suitable for such scenario in such
   manner that the distributed protocol process most of the traffic
   forwarding and scenarios, as the controller can schedule some
   traffic out of the
   congestion links to lower congested links, lowering the utilization of them. them
   during these times.  Section 4 describes the simulation process and results about such of
   this scenario.

4.  CCDR Simulation.

   The following sections describe the topology, traffic matrix, E2E
   path optimization and congestion elimination in CCDR applied
   scenarios.

4.1.  Topology Simulation

   The network topology mainly contains nodes and links information.
   Nodes used in the simulation have two types: core node and edge node.
   The core nodes are fully linked to each other.  The edge nodes are
   connected only with some of the core nodes.  Figure 5 is a topology
   example of 4 core nodes and 5 edge nodes.  In this CCDR simulation,
   100 core nodes and 400 edge nodes are generated.

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

                        Figure 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 a 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 the 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 E2E path optimization is to find the best path which is the
   lowest in metric value and each link of the path is far below link's
   threshold.  Based on the current state of the network, the PCE within
   CCDR framework combines the shortest path algorithm with a penalty
   theory of classical optimization and graph theory.

   Given a background traffic matrix matrix, which is unscheduled, when a set
   of new flows comes into the network, the E2E 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) Degree(UID), when the new flows are
   added into the network network, is shown in Figure 6.  The first graph in
   Figure 6 is the UID with OSPF and the second graph is the UID with
   CCDR E2E path optimization.  The average UID of the first graph is
   more than 30%. After path optimization, the average UID is less than
   5%.  The results show that the CCDR E2E path optimization has an eye-
   catching decreasing decrease 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
             Figure 6: Simulation Result with Congestion Elimination

4.4.  Network Temporal Congestion Elimination

   Different degree degrees of network congestions are were simulated.  The
   Congestion Degree (CD) is defined as the link utilization beyond its
   threshold.

   The CCDR congestion elimination performance is shown in Figure 7.
   The first graph is the CD distribution before the process of
   congestion elimination.  The average CD of all congested links is
   more than 10%. The second graph shown in Figure 7 is the CD
   distribution after using the congestion elimination process.  It
   shows only 12 links among totally 20000 links exceed the threshold,
   and all the CD values are less than 3%. Thus, after scheduling of the
   traffic in
   congestion away from the congested 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)
            Figure 7: Simulation Result with Congestion Elimination

5.  CCDR Deployment Consideration.

   With the above CCDR scenarios and simulation results, we can know demonstrate
   it is necessary and feasible to find one general solution to cope with various
   complex situations situations.  Integrated use of a centralized controller for
   the more complex optimal path computation
   in centrally manner computations in a native IP network based on
   results in significant improvements without impacting the underlay
   network topology and the real time traffic.

   [I-D.ietf-teas-pce-native-ip] gives the infrastructure.  A proposed solution for above scenarios,
   such thoughts can be extended to cover requirements in other
   situations is described in future.
   draft[I-D.ietf-teas-pce-native-ip] .

6.  Security Considerations

   This document considers mainly the integration of distributed
   protocol
   protocols and the central control capability of a PCE.  While It
   certainly can ease the management of network in various traffic
   engineering scenarios as described in this document, but the central centralized
   control manner also bring the a new point that may be easily attacked.
   Solutions for CCDR scenarios should keep these in mind and need to consider more for the protection of the
   PCEand their communication with the underlay devices,
   as that described in document devices.  [RFC5440] and
   [RFC8253] provide additional information.

7.  IANA Considerations

   This document does not require any IANA actions.

8.  Contributors

   Lu Huang contributes contributed 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 support and
   comments on this draft.

10.  References

10.1.  Normative References

   [I-D.ietf-teas-pce-native-ip]
              Wang, A., Zhao, Q., Khasanov, B., Chen, H., and R. Mallya,
              "PCE in Native IP Network", draft-ietf-teas-pce-native-
              ip-03 (work in progress), April 2019.

   [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>.

10.2.  Informative References

   [I-D.ietf-teas-pce-native-ip]
              Wang, A., Zhao, Q., Khasanov, B., Chen, H., and R. Mallya,
              "PCE in Native IP Network", draft-ietf-teas-pce-native-
              ip-03 (work in progress), April 2019.

   [RFC3209]  Awduche, D., Berger, L., Gan, D., Li, T., Srinivasan, V.,
              and G. Swallow, "RSVP-TE: Extensions to RSVP for LSP
              Tunnels", RFC 3209, DOI 10.17487/RFC3209, December 2001,
              <https://www.rfc-editor.org/info/rfc3209>.

   [RFC8402]  Filsfils, C., Ed., Previdi, S., Ed., Ginsberg, L.,
              Decraene, B., Litkowski, S., and R. Shakir, "Segment
              Routing Architecture", RFC 8402, DOI 10.17487/RFC8402,
              July 2018, <https://www.rfc-editor.org/info/rfc8402>.

   [RFC8578]  Grossman, E., Ed., "Deterministic Networking Use Cases",
              RFC 8578, DOI 10.17487/RFC8578, May 2019,
              <https://www.rfc-editor.org/info/rfc8578>.

Authors' Addresses

   Aijun Wang
   China Telecom
   Beiqijia Town, Changping District
   Beijing, Beijing  102209
   China

   Email: wangaj3@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

   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