Internet Engineering Task Force (IETF)                           A. WangRequest for Comments: 8735                                 China TelecomCategory: Informational                                         X. HuangISSN: 2070-1721                                                   C. Kou                                                                    BUPT                                                                   Z. Li                                                            China Mobile                                                                   P. Mi                                                     Huawei Technologies                                                           February 2020     Scenarios and Simulation Results of PCE in a Native IP NetworkAbstract   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   https://www.rfc-editor.org/info/rfc8735.Copyright Notice   Copyright (c) 2020 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   to this document.  Code Components extracted from this document must   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.  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   Acknowledgements   Contributors   Authors' Addresses1.  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   protocols.   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   [PCEP-NATIVE-IP-EXT].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 Network3.  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 CCDR3.3.  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.                  +---+                +---+                  |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 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   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 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 Algorithm4.2.  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|\                                 | +----+ |                                 |        |                                 |        |                   +----+    +----+     +----+                   |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 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   results.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+-----------------------------------------------------------+         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 Results with Congestion Elimination4.5.  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|* ********* ***********************************************|           |*********** ***********************************************|           |***********************************************************|          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 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   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 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.  References8.1.  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>.8.2.  Informative References   [PCE-NATIVE-IP]              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,              <https://tools.ietf.org/html/draft-ietf-teas-pce-native-              ip-05>.   [PCEP-NATIVE-IP-EXT]              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-              ietf-pce-pcep-extension-native-ip-05>.   [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,              <https://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>.Acknowledgements   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.Contributors   Lu Huang contributed to the content of this document.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