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TEAS Working Group                                               A. Wang
Internet-Draft                                             China Telecom
Intended status: Informational                                  X. Huang
Expires: May 1, 2020                                              C. Kou
                                                                    BUPT
                                                                   Z. Li
                                                            China Mobile
                                                                   P. Mi
                                                     Huawei Technologies
                                                        October 29, 2019


      Scenarios and Simulation Results of PCE in Native IP Network
                 draft-ietf-teas-native-ip-scenarios-12

Abstract

   Requirements for providing the End to End(E2E) performance assurance
   are emerging within the service provider networks.  While there are
   various technology solutions, there is no single solution that can
   fulfill these requirements for a native IP network.  In particular,
   there is a need for a universal (E2E) solution that can cover both
   intra- and inter-domain scenarios.

   One feasible E2E traffic engineering solution is the addition of
   central control in a native IP network.  This document describes
   various complex scenarios and simulation results when applying the
   Path Computation Element (PCE) in a native IP network.  This
   solution, referred to as Centralized Control Dynamic Routing (CCDR),
   integrates the advantage of using distributed protocols and the power
   of a centralized control technology, providing traffic engineering
   for native IP networks in a manner that applies equally to intra- and
   inter-domain scenarios.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."



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   This Internet-Draft will expire on May 1, 2020.

Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   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.  Case Study for CCDR Algorithm . . . . . . . . . . . . . .   8
     4.2.  Topology Simulation . . . . . . . . . . . . . . . . . . .   9
     4.3.  Traffic Matrix Simulation . . . . . . . . . . . . . . . .  10
     4.4.  CCDR End-to-End Path Optimization . . . . . . . . . . . .  10
     4.5.  Network Temporal Congestion Elimination . . . . . . . . .  12
   5.  CCDR Deployment Consideration . . . . . . . . . . . . . . . .  14
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  14
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  15
   8.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  15
   9.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  15
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  15
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  15
     10.2.  Informative References . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

1.  Introduction

   A service provider network is composed of thousands of routers that
   run distributed protocols to exchange the reachability information.
   The path for the destination network is mainly calculated, and
   controlled, by the distributed protocols.  These distributed



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   protocols are robust enough to support most applications, however,
   they have some difficulties supporting the complexities needed for
   traffic engineering applications, e.g.  E2E performance assurance, or
   maximizing the link utilization within an IP network.

   Multiprotocol Label Switching (MPLS) using Traffic Engineering (TE)
   technology (MPLS-TE)[RFC3209]is one solution for traffic engineering
   networks but it introduces an MPLS network and related technology
   which would be an overlay of the IP network.  MPLS-TE technology is
   often used for Label Switched Path (LSP) protection and complex path
   set-up within a domain.  It has not been widely deployed for meeting
   E2E (especially in inter-domain) dynamic performance assurance
   requirements for an IP network.

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

   Deterministic Networking (DetNet)[RFC8578] is another possible
   solution.  It is primarily focused on providing bounded latency for a
   flow and introduces additional requirements on the domain edge
   router.  The current DetNet scope is within one domain.  The use
   cases defined in this document do not require the additional
   complexity of deterministic properties and so differ from the DetNet
   use cases.

   This draft describes several scenarios for a native IP network where
   a Centralized Control Dynamic Routing (CCDR) framework can produce
   qualitative improvement in efficiency without requiring a change of
   the data-plane behavior on the router.  Using knowledge of BGP(Border
   Gateway Protocol) session-specific prefixes advertised by a router,
   the network topology and the near real time link utilization
   information from network management systems, a central PCE is able to
   compute an optimal path and give the underlay routers the destination
   address to use to reach the BGP nexthop, such that the distributed
   routing protocol will use the computed path via traditional recursive
   lookup procedure.  Some results from simulations of path optimization
   are also presented, to concretely illustrate a variety of scenarios
   where CCDR shows significant improvement over traditional distributed
   routing protocols.

   This draft is the base document of the following two drafts: the
   universal solution draft, which is suitable for intra-domain and
   inter-domain TE scenario, is described in



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   [I-D.ietf-teas-pce-native-ip]; the related protocol extension
   contents is described in [I-D.ietf-pce-pcep-extension-native-ip]

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: 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  TE: Traffic Engineering

   o  UID: Utilization Increment Degree

   o  WAN: Wide Area Network

3.  CCDR Scenarios

   The following sections describe various deployment scenarios where
   applying the CCDR framework is intuitively expected to produce
   improvements, based on the macro-scale properties of the framework
   and the scenario.

3.1.  QoS Assurance for Hybrid Cloud-based Application

   With the emergence of cloud computing technologies, enterprises are
   putting more and more services on a public oriented cloud
   environment, but 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



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   two sites varies over time.  Enterprise applications require
   assurance of the E2E Quality of Service(QoS) performance on demand
   for variable bandwidth services.

   CCDR, which integrates the merits of distributed protocols and the
   power of 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

   As illustrated in Figure 1, the source and destination of the "Cloud
   Based Application" traffic are located at "Private Cloud Site" and
   "Public Cloud Site" respectively.

   By default, the traffic path between the private and public cloud
   site is determined by the distributed control network.  When
   application requires 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.
   Section 4.4 of this document describes the simulation 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
   connected to different customer types.  The traffic from these
   customers is often in a tidal pattern, with the links between the
   Core Router(CR)/Broadband Remote Access Server(BRAS) and CR/Service
   Router(SR) experiencing congestion in different periods, because the
   subscribers under BRAS often use the network at night, and the leased



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   line users under SR often use the network during the daytime.  The
   link between BRAS/SR and CR must satisfy the maximum traffic volume
   between them, respectively, and this causes these links often to be
   under-utilized.

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

              Figure 2: Star-mode Network Topology within MAN

   If we consider connecting the BRAS/SR with a local link loop (which
   is usually lower cost), and control the overall MAN topology with the
   CCDR framework, we can exploit the tidal phenomena between the BRAS/
   CR and SR/CR links, maximizing the utilization of these central trunk
   links (which are usually higher cost than the local loops).

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

           Figure 3: Link Utilization Maximization via CCDR

3.3.  Traffic Engineering for Multi-Domain

   Service provider networks are often comprised of different domains,
   interconnected with each other, forming a very complex topology as
   illustrated in 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 a distributed protocol, but this unbalance can be
   overcome utilizing the CCDR framework.




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

        Figure 4: Traffic Engineering for Complex Multi-Domain Topology

   A solution for this scenario requires the gathering of NetFlow
   information, analysis of the source/destination AS, and determining
   what is the main cause of the congested link(s).  After this, the
   operator can use the external Border Gateway Protocol(eBGP) sessions
   to schedule the traffic among the different domains according to the
   solution described in CCDR framework.

3.4.  Network Temporal Congestion Elimination

   In more general situations, there are often temporal congestion
   within the service provider's network, for example due to daily or
   weekly periodic bursts, or large events that are scheduled well in
   advance.  Such congestion phenomena often appear regularly, and if
   the service provider has methods to mitigate it, it will certainly
   improve their network operations capabilities and increase
   satisfaction for their customers.  CCDR is also suitable for such
   scenarios, as the controller can schedule traffic out of the
   congested links, lowering the utilization of them during these times.
   Section 4.5 describes the simulation results of this scenario.

4.  CCDR Simulation

   The following sections describe a specific case study to illustrate
   the workings of the CCDR algorithm with concrete paths/metrics, as
   well as a procedure for generating topology and traffic matrices and
   the results from simulations applying CCDR for E2E QoS (assured path
   and congestion elimination) over the generated topologies and traffic
   matrices.  In all cases examined, the CCDR algorithm produces
   qualitatively significant improvement over the reference (OSPF)
   algorithm, suggesting that CCDR will have broad applicability.

   The structure and scale of the simulated topology is similar to that
   of the real networks.  Multiple different traffic matrices were
   generated to simulate different congestion conditions in the network.




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   Only one of them is illustrated since the others produce similar
   results.

4.1.  Case Study for CCDR Algorithm

   In this section we consider a specific network topology for case
   study, examining the path selected by OSPF and CCDR and evaluating
   how and why the paths differ.  Figure 5 depicts the topology of the
   network in this case.  There are 8 forwarding devices in the network.
   The original cost and utilization are marked on it, as shown in the
   figure.  For example, the original cost and utilization for the link
   (1,2) are 3 and 50% respectively.  There are two flows: f1 and f2.
   Both of these two flows are from node 1 to node 8.  For simplicity,
   it is assumed that the bandwidth of the link in the network is 10Mb/
   s.  The flow rate of f1 is 1Mb/s, and the flow rate of f2 is 2Mb/s.
   The threshold of the link in congestion is 90%.

   If OSPF protocol (ISIS is similar, because it also use the Dijstra's
   algorithm) is applied in the network, which adopts Dijkstra's
   algorithm, the two flows from node 1 to node 8 can only use the OSPF
   path (p1: 1->2->3->8).  It is because Dijkstra's algorithm mainly
   considers original cost of the link.  Since CCDR considers cost and
   utilization simultaneously, the same path as OSPF will not be
   selected due to the severe congestion of the link (2,3).  In this
   case, f1 will select the path (p2: 1->5->6->7->8) since the new cost
   of this path is better than that of OSPF path.  Moreover, the path p2
   is also better than the path (p3: 1->2->4->7->8) for for flow f1.
   However, f2 will not select the same path since it will cause the new
   congestion in the link (6,7).  As a result, f2 will select the path
   (p3: 1->2->4->7->8).





















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           +----+      f1                +-------> +-----+ ----> +-----+
           |Edge|-----------+            |+--------|  3  |-------|  8  |
           |Node|---------+ |            ||+-----> +-----+ ----> +-----+
           +----+         | |       4/95%|||              6/50%     |
                          | |            |||                   5/60%|
                          | v            |||                        |
           +----+       +-----+ -----> +-----+      +-----+      +-----+
           |Edge|-------|  1  |--------|  2  |------|  4  |------|  7  |
           |Node|-----> +-----+ -----> +-----+7/60% +-----+5/45% +-----+
           +----+  f2      |     3/50%                              |
                           |                                        |
                           |   3/60%   +-----+ 5/55%+-----+   3/75% |
                           +-----------|  5  |------|  6  |---------+
                                       +-----+      +-----+
                     (a) Dijkstra's Algorithm (OSPF/ISIS)


           +----+      f1                          +-----+ ----> +-----+
           |Edge|-----------+             +--------|  3  |-------|  8  |
           |Node|---------+ |             |        +-----+ ----> +-----+
           +----+         | |       4/95% |               6/50%    ^|^
                          | |             |                   5/60%|||
                          | v             |                        |||
           +----+       +-----+ -----> +-----+ ---> +-----+ ---> +-----+
           |Edge|-------|  1  |--------|  2  |------|  4  |------|  7  |
           |Node|-----> +-----+        +-----+7/60% +-----+5/45% +-----+
           +----+  f2     ||     3/50%                              |^
                          ||                                        ||
                          ||   3/60%   +-----+5/55% +-----+   3/75% ||
                          |+-----------|  5  |------|  6  |---------+|
                          +----------> +-----+ ---> +-----+ ---------+
                        (b) CCDR Algorithm

                 Figure 5: Case Study for CCDR's Algorithm

4.2.  Topology Simulation

   Moving on from the specific case study, we now consider a class of
   networks more representative of real deployments, with a fully-linked
   core network that serves to connect edge nodes, which themselves
   connect to only a subset of the core.  An example of such a topology
   is shown in Figure 6, for the case of 4 core nodes and 5 edge nodes.
   The CCDR simulations presented in this work use topologies involving
   100 core nodes and 400 edge nodes.  While the resulting graph does
   not fit on this page, this scale of network is similar to what is
   deployed in production environments.





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

                      Figure 6: Topology of Simulation

   For the simulations, the number of links connecting one edge node to
   the set of core nodes is randomly chosen between 2 to 30, and the
   total number of links is more than 20000.  Each link has a congestion
   threshold, which can be arbitrarily set to (e.g.) 90% of the nominal
   link capacity without affecting the simulation results.

4.3.  Traffic Matrix Simulation

   For each topology, a 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 (100 core nodes plus 400 edge nodes).  About 20% of links are
   overloaded when the Open Shortest Path First (OSPF) protocol is used
   in the network.

4.4.  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 for each link of the path, the
   utilizatioin is far below link's congestion 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.



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   Given a background traffic 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), when the new flows are
   added into the network, is shown in Figure 7.  The first graph in
   Figure 7 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 decrease in UID relative to the path chosen based on OSPF.

   While real-world results invariably differ from simulations (for
   example, real-world topologies are likely to exhibit correlation in
   the attachment patterns for edge nodes to the core, which are not
   reflected in these results), the dramatic nature of the improvement
   in UID and the choice of simulated topology to resemble real-world
   conditions suggests that real-world deployments will also experience
   significant improvement in UID results.































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

            Figure 7: Simulation Result with Congestion Elimination

4.5.  Network Temporal Congestion Elimination

   During the simulations, different degrees of network congestion were
   considered.  To examine the effect of CCDR on link congestion, we
   consider the Congestion Degree (CD) of a link, defined as the link
   utilization beyond its threshold.

   The CCDR congestion elimination performance is shown in Figure 8.
   The first graph is the CD distribution before the process of
   congestion elimination.  The average CD of all congested links is
   about 20%. The second graph shown in Figure 8 is the CD distribution
   after using the congestion elimination process.  It shows that only



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   12 links among the total of 20000 links exceed the threshold, and all
   the CD values are less than 3%. Thus, after scheduling of the traffic
   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 8: Simulation Result with Congestion Elimination

   It is clear that using an active path-computation mechanism that is
   able to take into account observed link traffic/congestion, the
   occurrence of congestion events can be greatly reduced.  Only when a
   preponderance of links in the network are near their congestion



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   threshold will the central controller be unable to find a clear path,
   as opposed to when a static metric-based procedure is used, which
   will produce congested paths once a single bottleneck approaches its
   capacity.  More detailed information about the algorithm can be found
   in[PTCS] .

5.  CCDR Deployment Consideration

   The above CCDR scenarios and simulation results demonstrate that a
   single general solution can be found that copes with multiple complex
   situations.  The specific situations considered are not known to have
   any special properties, so it is expected that the benefits
   demonstrated will have general applicability.  Accordingly, the
   integrated use of a centralized controller for the more complex
   optimal path computations in a native IP network should result in
   significant improvements without impacting the underlay network
   infrastructure.

   For intra-domain or inter-domain native IP TE scenarios, the
   deployment of a CCDR solution is similar, with the centralized
   controller being able to compute paths and no changes required to the
   underlying network infrastructure.  This universal deployment
   characteristic can facilitate a generic traffic engineering solution,
   where operators do not need to differentiate between intra-domain and
   inter-domain TE cases.

   To deploy the CCDR solution, the PCE should collect the underlay
   network topology dynamically, for example via BGP-LS[RFC7752].  It
   also needs to gather the network traffic information periodically
   from the network management platform.  The simulation results show
   that the PCE can compute the E2E optimal path within seconds, thus it
   can cope with the change of underlay network on the scale of minutes.
   More agile requirements would need to increase the sample rate of
   underlay network and decrease the detection and notification interval
   of the underlay network.  The methods to gather and decrease the
   latency of these information are out of the scope of this draft.

6.  Security Considerations

   This document considers mainly the integration of distributed
   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, the centralized
   control also bring a new point that may be easily attacked.
   Solutions for CCDR scenarios need to consider protection of the PCE
   and communication with the underlay devices.

   [RFC5440] and [RFC8253] provide additional information.



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   The control priority and interaction process should also be carefully
   designed for the combination of distributed protocol and central
   control.  Generally, the central control instruction should have
   higher priority than the forwarding actions determined by the
   distributed protocol.  When the communication between PCE and the
   underlay devices is not in function, the distributed protocol should
   take over the control right of the underlay network.
   [I-D.ietf-teas-pce-native-ip] provides more considerations
   corresponding to the solution.

7.  IANA Considerations

   This document does not require any IANA actions.

8.  Contributors

   Lu Huang 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 support and
   comments on this draft.

   Thanks Benjamin Kaduk for his careful review and valuable suggestions
   to this draft.  Also thanks Roman Danyliw, Alvaro Retana and Eric
   Vyncke for their views and comments.

10.  References

10.1.  Normative References

   [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>.

   [RFC7752]  Gredler, H., Ed., Medved, J., Previdi, S., Farrel, A., and
              S. Ray, "North-Bound Distribution of Link-State and
              Traffic Engineering (TE) Information Using BGP", RFC 7752,
              DOI 10.17487/RFC7752, March 2016,
              <https://www.rfc-editor.org/info/rfc7752>.

   [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>.



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10.2.  Informative References

   [I-D.ietf-pce-pcep-extension-native-ip]
              Wang, A., Khasanov, B., Cheruathur, S., Zhu, C., and S.
              Fang, "PCEP Extension for Native IP Network", draft-ietf-
              pce-pcep-extension-native-ip-04 (work in progress), August
              2019.

   [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-04 (work in progress), August 2019.

   [PTCS]     Zhang, P., Xie, K., Kou, C., Huang, X., Wang, A., and Q.
              Sun, "A Practical Traffic Control Scheme With Load
              Balancing Based on PCE Architecture", IEEE
              Access 18526773, DOI 10.1109/ACCESS.2019.2902610, March
              2019, <http://ieeexplore.ieee.org/document/8657733>.

   [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









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   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

















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