draft-ietf-teas-native-ip-scenarios-08.txt   draft-ietf-teas-native-ip-scenarios-09.txt 
TEAS Working Group A. Wang TEAS Working Group A. Wang
Internet-Draft China Telecom Internet-Draft China Telecom
Intended status: Informational X. Huang Intended status: Informational X. Huang
Expires: March 2, 2020 C. Kou Expires: April 1, 2020 C. Kou
BUPT BUPT
Z. Li Z. Li
China Mobile China Mobile
P. Mi P. Mi
Huawei Technologies Huawei Technologies
August 30, 2019 September 29, 2019
Scenarios and Simulation Results of PCE in Native IP Network Scenarios and Simulation Results of PCE in Native IP Network
draft-ietf-teas-native-ip-scenarios-08 draft-ietf-teas-native-ip-scenarios-09
Abstract Abstract
Requirements for providing the End to End(E2E) performance assurance Requirements for providing the End to End(E2E) performance assurance
are emerging within the service provider network. While there are are emerging within the service provider network. While there are
various technology solutions, there is no one solution which can various technology solutions, there is no one solution which can
fulfill these requirements for a native IP network. One universal fulfill these requirements for a native IP network. One universal
(E2E) solution which can cover both intra-domain and inter-domain (E2E) solution which can cover both intra-domain and inter-domain
scenarios is needed. scenarios is needed.
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Internet-Drafts are working documents of the Internet Engineering Internet-Drafts are working documents of the Internet Engineering
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This Internet-Draft will expire on March 2, 2020. This Internet-Draft will expire on April 1, 2020.
Copyright Notice Copyright Notice
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. CCDR Scenarios. . . . . . . . . . . . . . . . . . . . . . . . 4 3. CCDR Scenarios . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. QoS Assurance for Hybrid Cloud-based Application. . . . . 4 3.1. QoS Assurance for Hybrid Cloud-based Application . . . . 4
3.2. Link Utilization Maximization . . . . . . . . . . . . . . 5 3.2. Link Utilization Maximization . . . . . . . . . . . . . . 5
3.3. Traffic Engineering for Multi-Domain . . . . . . . . . . 6 3.3. Traffic Engineering for Multi-Domain . . . . . . . . . . 6
3.4. Network Temporal Congestion Elimination. . . . . . . . . 7 3.4. Network Temporal Congestion Elimination . . . . . . . . . 7
4. CCDR Simulation. . . . . . . . . . . . . . . . . . . . . . . 7 4. CCDR Simulation . . . . . . . . . . . . . . . . . . . . . . . 7
4.1. Topology Simulation . . . . . . . . . . . . . . . . . . . 7 4.1. Case Study . . . . . . . . . . . . . . . . . . . . . . . 7
4.2. Traffic Matrix Simulation. . . . . . . . . . . . . . . . 8 4.2. Topology Simulation . . . . . . . . . . . . . . . . . . . 10
4.3. CCDR End-to-End Path Optimization . . . . . . . . . . . . 8 4.3. Traffic Matrix Simulation . . . . . . . . . . . . . . . . 10
4.4. Network Temporal Congestion Elimination . . . . . . . . . 10 4.4. CCDR End-to-End Path Optimization . . . . . . . . . . . . 11
5. CCDR Deployment Consideration. . . . . . . . . . . . . . . . 11 4.5. Network Temporal Congestion Elimination . . . . . . . . . 12
6. Security Considerations . . . . . . . . . . . . . . . . . . . 12 5. CCDR Deployment Consideration . . . . . . . . . . . . . . . . 13
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12 6. Security Considerations . . . . . . . . . . . . . . . . . . . 14
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 12 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 12 8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 14
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 12 9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 14
10.1. Normative References . . . . . . . . . . . . . . . . . . 12 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.2. Informative References . . . . . . . . . . . . . . . . . 12 10.1. Normative References . . . . . . . . . . . . . . . . . . 14
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13 10.2. Informative References . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
1. Introduction 1. Introduction
A service provider network is composed of thousands of routers that A service provider network is composed of thousands of routers that
run distributed protocols to exchange the reachability information. run distributed protocols to exchange the reachability information.
The path for the destination network is mainly calculated, and The path for the destination network is mainly calculated, and
controlled, by the distributed protocols. These distributed controlled, by the distributed protocols. These distributed
protocols are robust enough to support most applications, but have protocols are robust enough to support most applications, but have
some difficulties supporting the complexities needed for traffic some difficulties supporting the complexities needed for traffic
engineering applications, e.g. E2E performance assurance, or engineering applications, e.g. E2E performance assurance, or
skipping to change at page 4, line 18 skipping to change at page 4, line 18
o MAN: Metro Area Network o MAN: Metro Area Network
o QoS: Quality of Service o QoS: Quality of Service
o SR: Service Router o SR: Service Router
o UID: Utilization Increment Degree o UID: Utilization Increment Degree
o WAN: Wide Area Network o WAN: Wide Area Network
3. CCDR Scenarios. 3. CCDR Scenarios
The following sections describe various deployment scenarios for The following sections describe various deployment scenarios for
applying the CCDR framework. applying the CCDR framework.
3.1. QoS Assurance for Hybrid Cloud-based Application. 3.1. QoS Assurance for Hybrid Cloud-based Application
With the emergence of cloud computing technologies, enterprises are With the emergence of cloud computing technologies, enterprises are
putting more and more services on a public oriented cloud putting more and more services on a public oriented cloud
environment, but keeping core business within their private cloud. environment, but keeping core business within their private cloud.
The communication between the private and public cloud sites will The communication between the private and public cloud sites will
span the Wide Area Network (WAN) network. The bandwidth requirements span the Wide Area Network (WAN) network. The bandwidth requirements
between them are variable and the background traffic between these between them are variable and the background traffic between these
two sites varies over time. Enterprise applications require two sites varies over time. Enterprise applications require
assurance of the E2E Quality of Service(QoS) performance on demand assurance of the E2E Quality of Service(QoS) performance on demand
for variable bandwidth services. for variable bandwidth services.
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+---| |---+ +---| |---+
Figure 4: Traffic Engineering for Complex Multi-Domain Topology Figure 4: Traffic Engineering for Complex Multi-Domain Topology
A solution for this scenario requires the gathering of NetFlow A solution for this scenario requires the gathering of NetFlow
information, analysis of the source/destination AS, and determining information, analysis of the source/destination AS, and determining
what is the main cause of the congested link. After this, the what is the main cause of the congested link. After this, the
operator can use the external Border Gateway Protocol(eBGP) sessions operator can use the external Border Gateway Protocol(eBGP) sessions
to schedule the traffic among the different domains. to schedule the traffic among the different domains.
3.4. Network Temporal Congestion Elimination. 3.4. Network Temporal Congestion Elimination
In more general situations, there are often temporal congestions In more general situations, there are often temporal congestions
within the service provider's network. Such congestion phenomena within the service provider's network. Such congestion phenomena
often appear repeatedly, and if the service provider has methods to often appear repeatedly, and if the service provider has methods to
mitigate it, it will certainly improve their network operations mitigate it, it will certainly improve their network operations
capabilities and increase satisfaction for their customers. CCDR is capabilities and increase satisfaction for their customers. CCDR is
also suitable for such scenarios, as the controller can schedule also suitable for such scenarios, as the controller can schedule
traffic out of the congested links, lowering the utilization of them traffic out of the congested links, lowering the utilization of them
during these times. Section 4 describes the simulation results of during these times. Section 4 describes the simulation results of
this scenario. this scenario.
4. CCDR Simulation. 4. CCDR Simulation
The following sections describe the topology, traffic matrix, E2E The following sections describe one case study to illustrate CCDR
path optimization and congestion elimination in CCDR applied algorithm, the topology and traffic matrix generation process and the
scenarios. optimization results for E2E QoS assured path and congestion
elimination in applied scenarios.
4.1. Topology Simulation 4.1. Case Study
Figure 5 depicts the topology of the network for the case study.
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 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 with 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).
+-------+ +-------+
+---------+ f1 +--------->| | ----------> | |
| |---------------+ | +--------| 3 |-------------| 8 |
|Edge Node|-------------+ | | | +----->| | ----------> | |
| | | | | | | +-------+ 6/50% +-------+
+---------+ | | 4/95% | | | |
| | | | | 5/60% |
| v | | | |
+---------+ +-------+ +-------+ +-------+ +-------+
| | | |---------> | | | | | |
|Edge Node|-------| 1 |---------- | 2 |---------| 4 |--------| 7 |
| |-----> | |---------> | | 7/60% | | 5/45% | |
+---------+ f2 +-------+ 3/50% +-------+ +-------+ +-------+
| |
| |
| +-------+ +-------+ |
| 3/60% | | 5/55% | | 3/75%|
+---------------| 5 |-----------| 6 |----------+
| | | |
+-------+ +-------+
(a) Dijkstra's Algorithm
+-------+ +-------+
+---------+ f1 | | | |
| |---------------+ +--------| 3 |-------------| 8 |
|Edge Node|-------------+ | | | | | |
| | | | | +-------+ 6/50% +-------+
+---------+ | | 4/95%| ^ | ^
| | | 5/60% | | |
| v | | | |
+---------+ +-------+ +-------+ +-------+ +-------+
| | | |---------> | |-------> | | -----> | |
|Edge Node|-------| 1 |---------- | 2 |---------| 4 |--------| 7 |
| |-----> | | | | 7/60% | | 5/45% | |
+---------+ f2 +-------+ 3/50% +-------+ +-------+ +-------+
| | | ^
| | | |
| | +-------+ +-------+ | |
| | 3/60% | | 5/55% | | 3/75%| |
| +---------------| 5 |-----------| 6 |----------+ |
+--------------> | |---------> | |------------+
+-------+ +-------+
(b) CCDR Algorithm
Figure 5: Case Study
4.2. Topology Simulation
The network topology mainly contains nodes and links information. The network topology mainly contains nodes and links information.
Nodes used in the simulation have two types: core node and edge node. 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 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 connected only with some of the core nodes. Figure 6 is a topology
example of 4 core nodes and 5 edge nodes. In this CCDR simulation, example of 4 core nodes and 5 edge nodes. In this CCDR simulation,
100 core nodes and 400 edge nodes are generated. 100 core nodes and 400 edge nodes are generated.
+----+ +----+
/|Edge|\ /|Edge|\
| +----+ | | +----+ |
| | | |
| | | |
+----+ +----+ +----+ +----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+ |Edge|----|Core|-----|Core|---------+
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+----+ | / \ | | +----+ | / \ | |
\ | / \ | | \ | / \ | |
+----+ +----+ +----+ | +----+ +----+ +----+ |
|Edge|----|Core|-----|Core| | |Edge|----|Core|-----|Core| |
+----+ +----+ +----+ | +----+ +----+ +----+ |
| | | | | |
| +------\ +----+ | +------\ +----+
| ---|Edge| | ---|Edge|
+-----------------/ +----+ +-----------------/ +----+
Figure 5: Topology of Simulation Figure 6: Topology of Simulation
The number of links connecting one edge node to the set of core nodes 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 is randomly between 2 to 30, and the total number of links is more
than 20000. Each link has a congestion threshold. than 20000. Each link has a congestion threshold.
4.2. Traffic Matrix Simulation. 4.3. Traffic Matrix Simulation
The traffic matrix is generated based on the link capacity of The traffic matrix is generated based on the link capacity of
topology. It can result in many kinds of situations, such as topology. It can result in many kinds of situations, such as
congestion, mild congestion and non-congestion. congestion, mild congestion and non-congestion.
In the CCDR simulation, the dimension of the traffic matrix is In the CCDR simulation, the dimension of the traffic matrix is
500*500. About 20% links are overloaded when the Open Shortest Path 500*500. About 20% links are overloaded when the Open Shortest Path
First (OSPF) protocol is used in the network. First (OSPF) protocol is used in the network.
4.3. CCDR End-to-End Path Optimization 4.4. CCDR End-to-End Path Optimization
The CCDR E2E path optimization is to find the best path which is the 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 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 threshold. Based on the current state of the network, the PCE within
CCDR framework combines the shortest path algorithm with a penalty CCDR framework combines the shortest path algorithm with a penalty
theory of classical optimization and graph theory. theory of classical optimization and graph theory.
Given a background traffic matrix, which is unscheduled, when a set Given a background traffic matrix, which is unscheduled, when a set
of new flows comes into the network, the E2E path optimization finds of new flows comes into the network, the E2E path optimization finds
the optimal paths for them. The selected paths bring the least the optimal paths for them. The selected paths bring the least
congestion degree to the network. congestion degree to the network.
The link Utilization Increment Degree(UID), when the new flows are The link Utilization Increment Degree(UID), when the new flows are
added into the network, is shown in Figure 6. The first graph in added into the network, is shown in Figure 7. The first graph in
Figure 6 is the UID with OSPF and the second graph is the UID with 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 CCDR E2E path optimization. The average UID of the first graph is
more than 30%. After path optimization, the average UID is less than 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- 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. catching decrease in UID relative to the path chosen based on OSPF.
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| * * * *| | * * * *|
60| * * * * * *| 60| * * * * * *|
|* * ** * * * * * ** * * * * **| |* * ** * * * * * ** * * * * **|
|* * ** * * ** *** ** * * ** * * * ** * * *** **| |* * ** * * ** *** ** * * ** * * * ** * * *** **|
skipping to change at page 9, line 48 skipping to change at page 12, line 38
UID(%)| | UID(%)| |
| | | |
| | | |
20| | 20| |
| *| | *|
| * *| | * *|
| * * * * * ** * *| | * * * * * ** * *|
0+-----------------------------------------------------------+ 0+-----------------------------------------------------------+
0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000
Flow Number Flow Number
Figure 6: Simulation Result with Congestion Elimination Figure 7: Simulation Result with Congestion Elimination
4.4. Network Temporal Congestion Elimination 4.5. Network Temporal Congestion Elimination
Different degrees of network congestions were simulated. The Different degrees of network congestions were simulated. The
Congestion Degree (CD) is defined as the link utilization beyond its Congestion Degree (CD) is defined as the link utilization beyond its
threshold. threshold.
The CCDR congestion elimination performance is shown in Figure 7. The CCDR congestion elimination performance is shown in Figure 8.
The first graph is the CD distribution before the process of The first graph is the CD distribution before the process of
congestion elimination. The average CD of all congested links is congestion elimination. The average CD of all congested links is
more than 10%. The second graph shown in Figure 7 is the CD more than 10%. The second graph shown in Figure 8 is the CD
distribution after using the congestion elimination process. It distribution after using the congestion elimination process. It
shows only 12 links among totally 20000 links exceed the threshold, 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 and all the CD values are less than 3%. Thus, after scheduling of the
traffic away from the congested paths, the degree of network traffic away from the congested paths, the degree of network
congestion is greatly eliminated and the network utilization is in congestion is greatly eliminated and the network utilization is in
balance. balance.
Before congestion elimination Before congestion elimination
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| * ** * ** ** *| | * ** * ** ** *|
skipping to change at page 11, line 41 skipping to change at page 13, line 44
CD(%) | | CD(%) | |
10| | 10| |
| | | |
| | | |
5 | | 5 | |
| | | |
| * ** * * * ** * ** * | | * ** * * * ** * ** * |
0 +-----------------------------------------------------------+ 0 +-----------------------------------------------------------+
0 0.5 1 1.5 2 0 0.5 1 1.5 2
Link Number(*10000) Link Number(*10000)
Figure 7: Simulation Result with Congestion Elimination Figure 8: Simulation Result with Congestion Elimination
5. CCDR Deployment Consideration. 5. CCDR Deployment Consideration
With the above CCDR scenarios and simulation results, we demonstrate With the above CCDR scenarios and simulation results, we demonstrate
it is feasible to find one general solution to cope with various it is feasible to find one general solution to cope with various
complex situations. Integrated use of a centralized controller for complex situations. Integrated use of a centralized controller for
the more complex optimal path computations in a native IP network the more complex optimal path computations in a native IP network
results in significant improvements without impacting the underlay results in significant improvements without impacting the underlay
network infrastructure. A proposed solution is described in network infrastructure. A proposed solution is described in
draft[I-D.ietf-teas-pce-native-ip] . draft[I-D.ietf-teas-pce-native-ip] .
More detailed information about the algorithm can refer to the IEEE
document " A Practical Traffic Control Scheme With Load Balancing
Based on PCE Architecture"
6. Security Considerations 6. Security Considerations
This document considers mainly the integration of distributed This document considers mainly the integration of distributed
protocols and the central control capability of a PCE. While It protocols and the central control capability of a PCE. While It
certainly can ease the management of network in various traffic certainly can ease the management of network in various traffic
engineering scenarios as described in this document, the centralized engineering scenarios as described in this document, the centralized
control also bring a new point that may be easily attacked. control also bring a new point that may be easily attacked.
Solutions for CCDR scenarios need to consider protection of the Solutions for CCDR scenarios need to consider protection of the PCE
PCEand communication with the underlay devices. [RFC5440] and and communication with the underlay devices. [RFC5440] and [RFC8253]
[RFC8253] provide additional information. provide additional information.
7. IANA Considerations 7. IANA Considerations
This document does not require any IANA actions. This document does not require any IANA actions.
8. Contributors 8. Contributors
Lu Huang contributed to the content of this draft. Lu Huang contributed to the content of this draft.
9. Acknowledgement 9. Acknowledgement
skipping to change at page 12, line 50 skipping to change at page 15, line 10
"PCEPS: Usage of TLS to Provide a Secure Transport for the "PCEPS: Usage of TLS to Provide a Secure Transport for the
Path Computation Element Communication Protocol (PCEP)", Path Computation Element Communication Protocol (PCEP)",
RFC 8253, DOI 10.17487/RFC8253, October 2017, RFC 8253, DOI 10.17487/RFC8253, October 2017,
<https://www.rfc-editor.org/info/rfc8253>. <https://www.rfc-editor.org/info/rfc8253>.
10.2. Informative References 10.2. Informative References
[I-D.ietf-teas-pce-native-ip] [I-D.ietf-teas-pce-native-ip]
Wang, A., Zhao, Q., Khasanov, B., Chen, H., and R. Mallya, Wang, A., Zhao, Q., Khasanov, B., Chen, H., and R. Mallya,
"PCE in Native IP Network", draft-ietf-teas-pce-native- "PCE in Native IP Network", draft-ietf-teas-pce-native-
ip-03 (work in progress), April 2019. ip-04 (work in progress), August 2019.
[RFC3209] Awduche, D., Berger, L., Gan, D., Li, T., Srinivasan, V., [RFC3209] Awduche, D., Berger, L., Gan, D., Li, T., Srinivasan, V.,
and G. Swallow, "RSVP-TE: Extensions to RSVP for LSP and G. Swallow, "RSVP-TE: Extensions to RSVP for LSP
Tunnels", RFC 3209, DOI 10.17487/RFC3209, December 2001, Tunnels", RFC 3209, DOI 10.17487/RFC3209, December 2001,
<https://www.rfc-editor.org/info/rfc3209>. <https://www.rfc-editor.org/info/rfc3209>.
[RFC8402] Filsfils, C., Ed., Previdi, S., Ed., Ginsberg, L., [RFC8402] Filsfils, C., Ed., Previdi, S., Ed., Ginsberg, L.,
Decraene, B., Litkowski, S., and R. Shakir, "Segment Decraene, B., Litkowski, S., and R. Shakir, "Segment
Routing Architecture", RFC 8402, DOI 10.17487/RFC8402, Routing Architecture", RFC 8402, DOI 10.17487/RFC8402,
July 2018, <https://www.rfc-editor.org/info/rfc8402>. July 2018, <https://www.rfc-editor.org/info/rfc8402>.
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