wdiff draft-ietf-teas-native-ip-scenarios-07.txt draft-ietf-teas-native-ip-scenarios-08.txt

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

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

Abstract

The requirements

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

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

Status of This Memo

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provisions of BCP 78 and BCP 79.

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This Internet-Draft will expire on February 27, March 2, 2020.

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Table of Contents

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

1. Introduction

Service

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

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

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

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

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

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

2. Terminology

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

The following terms are defined in this document:

o BRAS: Broadband Remote Access Server

o CD: Congestion Degree

o CR: Core Router

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

o IDC: Internet Data Center

o MAN: Metro Area Network

o QoS: Quality of Service

o SR: Service Router

o UID: Utilization Increment Degree

o WAN: Wide Area Network

3. CCDR Scenarios.

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

3.1. QoS Assurance for Hybrid Cloud-based Application.

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

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

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

Figure 1: Hybrid Cloud Communication Scenario

By default, the traffic path between the private and public cloud
site will be determined by the distributed control network. When
applications require the E2E QoS assurance, it can send these
requirements to the PCE, and let the PCE compute one E2E path which
is based on the underlying network topology and the real traffic
information, to accommodate the application's QoS requirements. The proposed
solution can refer the draft [I-D.ietf-teas-pce-native-ip].
Section 4 of this document describes the detail simulation process and the result. results for this
use case.

3.2. Link Utilization Maximization

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

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

Figure 2: Star-mode Network Topology within MAN

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

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

Figure 3: Link Utilization Maximization via CCDR

3.3. Traffic Engineering for Multi-Domain

The service

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

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

Figure 4: Traffic Engineering for Complex Multi-Domain Topology

Solution

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

3.4. Network Temporal Congestion Elimination.

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

4. CCDR Simulation.

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

4.1. Topology Simulation

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

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

Figure 5: Topology of Simulation

The number of links connecting one edge node to the set of core nodes
is randomly between 2 to 30, and the total number of links is more
than 20000. Each link has its a congestion threshold.

4.2. Traffic Matrix Simulation.

The traffic matrix is generated based on the link capacity of
topology. It can result in many kinds of situations, such as
congestion, mild congestion and non-congestion.

In the CCDR simulation, the dimension of the traffic matrix is
500*500. About 20% links are overloaded when the Open Shortest Path
First (OSPF) protocol is used in the network.

4.3. CCDR End-to-End Path Optimization

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

Given a background traffic matrix matrix, which is unscheduled, when a set
of new flows comes into the network, the E2E path optimization finds
the optimal paths for them. The selected paths bring the least
congestion degree to the network.

The link Utilization Increment Degree(UID) Degree(UID), when the new flows are
added into the network network, is shown in Figure 6. The first graph in
Figure 6 is the UID with OSPF and the second graph is the UID with
CCDR E2E path optimization. The average UID of the first graph is
more than 30%. After path optimization, the average UID is less than
5%. The results show that the CCDR E2E path optimization has an eye-
catching decreasing decrease in UID relative to the path chosen based on OSPF.

+-----------------------------------------------------------+
| * * * *|
60| * * * * * *|
|* * ** * * * * * ** * * * * **|
|* * ** * * ** *** ** * * ** * * * ** * * *** **|
|* * * ** * ** ** *** *** ** **** ** *** **** ** *** **|
40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **|
UID(%)|* * ******* ** *** *** ******* **** ** *** ***** *********|
|*** ******* ** **** *********** *********** ***************|
|******************* *********** *********** ***************|
20|******************* ***************************************|
|******************* ***************************************|
|***********************************************************|
|***********************************************************|
0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000
+-----------------------------------------------------------+
| |
60| |
| |
| |
| |
40| |
UID(%)| |
| |
| |
20| |
| *|
| * *|
| * * * * * ** * *|
0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000
Flow Number
Figure 6: Simulation Result with Congestion Elimination

4.4. Network Temporal Congestion Elimination

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

The CCDR congestion elimination performance is shown in Figure 7.
The first graph is the CD distribution before the process of
congestion elimination. The average CD of all congested links is
more than 10%. The second graph shown in Figure 7 is the CD
distribution after using the congestion elimination process. It
shows only 12 links among totally 20000 links exceed the threshold,
and all the CD values are less than 3%. Thus, after scheduling of the
traffic in
congestion away from the congested paths, the degree of network
congestion is greatly eliminated and the network utilization is in
balance.

Before congestion elimination
+-----------------------------------------------------------+
| * ** * ** ** *|
20| * * **** * ** ** *|
|* * ** * ** ** **** * ***** *********|
|* * * * * **** ****** * ** *** **********************|
15|* * * ** * ** **** ********* *****************************|
|* * ****** ******* ********* *****************************|
CD(%) |* ********* ******* ***************************************|
10|* ********* ***********************************************|
|*********** ***********************************************|
|***********************************************************|
5|***********************************************************|
|***********************************************************|
|***********************************************************|
0+-----------------------------------------------------------+
0 0.5 1 1.5 2

After congestion elimination
+-----------------------------------------------------------+
| |
20| |
| |
| |
15| |
| |
CD(%) | |
10| |
| |
| |
5 | |
| |
| * ** * * * ** * ** * |
0 +-----------------------------------------------------------+
0 0.5 1 1.5 2
Link Number(*10000)
Figure 7: Simulation Result with Congestion Elimination

5. CCDR Deployment Consideration.

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

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

6. Security Considerations

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

7. IANA Considerations

This document does not require any IANA actions.

8. Contributors

Lu Huang contributes contributed to the content of this draft.

9. Acknowledgement

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

10. References

10.1. Normative References

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

[RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation
Element (PCE) Communication Protocol (PCEP)", RFC 5440,
DOI 10.17487/RFC5440, March 2009,
<https://www.rfc-editor.org/info/rfc5440>.

[RFC8253] Lopez, D., Gonzalez de Dios, O., Wu, Q., and D. Dhody,
"PCEPS: Usage of TLS to Provide a Secure Transport for the
Path Computation Element Communication Protocol (PCEP)",
RFC 8253, DOI 10.17487/RFC8253, October 2017,
<https://www.rfc-editor.org/info/rfc8253>.

10.2. Informative References

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

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

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

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

Authors' Addresses

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

Email: wangaj3@chinatelecom.cn

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

Email: huangxh@bupt.edu.cn

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

Email: koucx@lsec.cc.ac.cn
Zhenqiang Li
China Mobile
32 Xuanwumen West Ave, Xicheng District
Beijing 100053
China

Email: li_zhenqiang@hotmail.com

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

Email: mipenghui@huawei.com