RFC 8735

Internet Engineering Task Force (IETF)                           A. Wang
Request for Comments: 8735                                 China Telecom
Category: Informational                                         X. Huang
ISSN: 2070-1721                                                   C. Kou
                                                                   Z. Li
                                                            China Mobile
                                                                   P. Mi
                                                     Huawei Technologies
                                                           February 2020

     Scenarios and Simulation Results of PCE in a Native IP Network


   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 document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This document is a product of the Internet Engineering Task Force
   (IETF).  It represents the consensus of the IETF community.  It has
   received public review and has been approved for publication by the
   Internet Engineering Steering Group (IESG).  Not all documents
   approved by the IESG are candidates for any level of Internet
   Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at

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   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction
   2.  Terminology
   3.  CCDR Scenarios
     3.1.  QoS Assurance for Hybrid Cloud-Based Application
     3.2.  Link Utilization Maximization
     3.3.  Traffic Engineering for Multi-domain
     3.4.  Network Temporal Congestion Elimination
   4.  CCDR Simulation
     4.1.  Case Study for CCDR Algorithm
     4.2.  Topology Simulation
     4.3.  Traffic Matrix Simulation
     4.4.  CCDR End-to-End Path Optimization
     4.5.  Network Temporal Congestion Elimination
   5.  CCDR Deployment Consideration
   6.  Security Considerations
   7.  IANA Considerations
   8.  References
     8.1.  Normative References
     8.2.  Informative References

   Authors' Addresses

1.  Introduction

   A service provider network is composed of thousands of routers that
   run distributed protocols to exchange reachability information.  The
   path for the destination network is mainly calculated, and
   controlled, by the distributed protocols.  These distributed
   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 TE networks, but
   it introduces an MPLS network along with related technology, which
   would be an overlay of the IP network.  MPLS-TE technology is often
   used for Label Switched Path (LSP) protection and setting up complex
   paths 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 central control
   technology, but it requires the underlying network, especially the
   provider edge router, to do an in-depth label push and pop action
   while adding 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 document 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 to the data-plane behavior on the router.  Using knowledge of
   the Border Gateway Protocol (BGP) 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
   underlying 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

   This document is the base document of the following two documents:
   the universal solution document, which is suitable for intra-domain
   and inter-domain TE scenario, is described in [PCE-NATIVE-IP]; and
   the related protocol extension contents is described in

2.  Terminology

   In this document, PCE is used as defined in [RFC5440].  The following
   terms are used as described here:

   BRAS:   Broadband Remote Access Server

   CD:     Congestion Degree

   CR:     Core Router

   CCDR:   Centralized Control Dynamic Routing

   E2E:    End to End

   IDC:    Internet Data Center

   MAN:    Metro Area Network

   QoS:    Quality of Service

   SR:     Service Router

   TE:     Traffic Engineering

   UID:    Utilization Increment Degree

   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
   while keeping core business within their private cloud.  The
   communication between the private and public cloud sites spans the
   WAN.  The bandwidth requirements between them are variable, and the
   background traffic between these two sites varies over time.
   Enterprise applications require assurance of the E2E 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 an
   application requires 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 real traffic
   information, in order 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 due to
   subscribers under BRAS often using the network at night and the
   leased line users under SR often using the network during the
   daytime.  The link between BRAS/SR and CR must satisfy the maximum
   traffic volume between them, respectively, which causes these links
   to often be underutilized.

                            |   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.

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

      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 autonomous system
   (AS), and determining what the main cause of the congested link(s)
   is.  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 the CCDR framework.

3.4.  Network Temporal Congestion Elimination

   In more general situations, there is 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 operation capabilities and increase satisfaction for
   customers.  CCDR is also suitable for such scenarios, as the
   controller can schedule traffic out of the congested links, lowering
   their utilization 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.
   Only one of them is illustrated since the others produce similar

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 eight 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 10 Mb/s.  The flow rate of f1 is 1 Mb/s and the flow rate
   of f2 is 2 Mb/s.  The threshold of the link in congestion is 90%.

   If the OSPF protocol, which adopts Dijkstra's algorithm (IS-IS is
   similar because it also uses Dijkstra's algorithm), is applied in the
   network, the two flows from node 1 to node 8 can only use the OSPF
   path (p1: 1->2->3->8).  This is because Dijkstra's algorithm mainly
   considers the 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 the OSPF path.  Moreover, the
   path p2 is also better than the path (p3: 1->2->4->7->8) for flow f1.
   However, f2 will not select the same path since it will cause new
   congestion in the link (6, 7).  As a result, f2 will select the path
   (p3: 1->2->4->7->8).

         +----+      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/IS-IS)

         +----+      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.

                                 | +----+ |
                                 |        |
                                 |        |
                   +----+    +----+     +----+
                   +----+    +----+     +----+         |
                           /  |    \   /   |           |
                     +----+   |     \ /    |           |
                     |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 two and thirty, and
   the total number of links is more than 20,000.  Each link has a
   congestion threshold, which can be arbitrarily set, for example, to
   90% of the nominal link capacity without affecting the simulation

4.3.  Traffic Matrix Simulation

   For each topology, a traffic matrix is generated based on the link
   capacity of the 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 entails finding the best path, which
   is the lowest in metric value, as well as having utilization far
   below the congestion threshold for each link of the path.  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, 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 suggest that real-world deployments will also experience
   significant improvement in UID results.

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

          Figure 7: Simulation Results 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
   twelve links among the total 20,000 exceed the threshold, and all the
   CD values are less than 3%. Thus, after scheduling 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|* ********* ***********************************************|
           |*********** ***********************************************|
              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 Results with Congestion Elimination

   It is clear that by 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
   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 underlying network

   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 along with no changes being
   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 underlying
   network topology dynamically, for example, via Border Gateway
   Protocol - Link State (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 a
   change to the underlying network in a matter of minutes.  More agile
   requirements would need to increase the sample rate of the underlying
   network and decrease the detection and notification interval of the
   underlying network.  The methods of gathering this information as
   well as decreasing its latency are out of the scope of this document.

6.  Security Considerations

   This document considers mainly the integration of distributed
   protocols and the central control capability of a PCE.  While it can
   certainly simplify the management of a network in various traffic-
   engineering scenarios as described in this document, the centralized
   control also brings a new point that may be easily attacked.
   Solutions for CCDR scenarios need to consider protection of the PCE
   and communication with the underlying devices.

   [RFC5440] and [RFC8253] provide additional information.

   The control priority and interaction process should also be carefully
   designed for the combination of the distributed protocol and central
   control.  Generally, the central control instructions should have
   higher priority than the forwarding actions determined by the
   distributed protocol.  When communication between PCE and the
   underlying devices is disrupted, the distributed protocol should take
   control of the underlying network.  [PCE-NATIVE-IP] provides more
   considerations corresponding to the solution.

7.  IANA Considerations

   This document has no IANA actions.

8.  References

8.1.  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,

   [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,

   [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,

8.2.  Informative References

              Wang, A., Zhao, Q., Khasanov, B., and H. Chen, "PCE in
              Native IP Network", Work in Progress, Internet-Draft,
              draft-ietf-teas-pce-native-ip-05, 9 January 2020,

              Wang, A., Khasanov, B., Fang, S., and C. Zhu, "PCEP
              Extension for Native IP Network", Work in Progress,
              Internet-Draft, draft-ietf-pce-pcep-extension-native-ip-
              05, 17 February 2020, <https://tools.ietf.org/html/draft-

   [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",
              DOI 10.1109/ACCESS.2019.2902610, IEEE Access 18526773,
              March 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,

   [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,


   The authors would like to thank Deborah Brungard, Adrian Farrel,
   Huaimo Chen, Vishnu Beeram, and Lou Berger for their support and
   comments on this document.

   Thanks to Benjamin Kaduk for his careful review and valuable
   suggestions on this document.  Also, thanks to Roman Danyliw, Alvaro
   Retana, and Éric Vyncke for their reviews and comments.


   Lu Huang contributed to the content of this document.

Authors' Addresses

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

   Email: wangaj3@chinatelecom.cn

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

   Email: huangxh@bupt.edu.cn

   Caixia Kou
   Beijing University of Posts and Telecommunications
   No.10 Xitucheng Road, Haidian District

   Email: koucx@lsec.cc.ac.cn

   Zhenqiang Li
   China Mobile
   32 Xuanwumen West Ave, Xicheng District

   Email: li_zhenqiang@hotmail.com

   Penghui Mi
   Huawei Technologies
   Tower C of Bldg.2, Cloud Park, No.2013 of Xuegang Road
   Bantian,Longgang District, 518129

   Email: mipenghui@huawei.com