draft-ietf-teas-native-ip-scenarios-10.txt   draft-ietf-teas-native-ip-scenarios-11.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: April 11, 2020 C. Kou Expires: April 27, 2020 C. Kou
BUPT BUPT
Z. Li Z. Li
China Mobile China Mobile
P. Mi P. Mi
Huawei Technologies Huawei Technologies
October 9, 2019 October 25, 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-10 draft-ietf-teas-native-ip-scenarios-11
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 networks. While there are
various technology solutions, there is no one solution which can various technology solutions, there is no single solution that can
fulfill these requirements for a native IP network. One universal fulfill these requirements for a native IP network. In particular,
(E2E) solution which can cover both intra-domain and inter-domain there is a need for a universal (E2E) solution that is simultaneously
scenarios is needed. applicable for both intra- and inter-domain scenarios.
One feasible E2E traffic engineering solution is the addition of One feasible E2E traffic engineering solution is the addition of
central control in a native IP network. This document describes central control in a native IP network. This document describes
various complex scenarios and simulation results when applying the various complex scenarios and simulation results when applying the
Path Computation Element (PCE) in a native IP network. This Path Computation Element (PCE) in a native IP network. This
solution, referred to as Centralized Control Dynamic Routing (CCDR), solution, referred to as Centralized Control Dynamic Routing (CCDR),
integrates the advantage of using distributed protocols and the power integrates the advantage of using distributed protocols and the power
of a centralized control technology. 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 Status of This Memo
This Internet-Draft is submitted in full conformance with the This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79. provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on April 27, 2020.
This Internet-Draft will expire on April 11, 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 . . . . . . . . . . . . . . . . . . . . . . . . . 4
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. Case Study for CCDR algorithm . . . . . . . . . . . . . . 8 4.1. Case Study for CCDR algorithm . . . . . . . . . . . . . . 8
4.2. Topology Simulation . . . . . . . . . . . . . . . . . . . 10 4.2. Topology Simulation . . . . . . . . . . . . . . . . . . . 10
4.3. Traffic Matrix Simulation . . . . . . . . . . . . . . . . 10 4.3. Traffic Matrix Simulation . . . . . . . . . . . . . . . . 10
4.4. CCDR End-to-End Path Optimization . . . . . . . . . . . . 11 4.4. CCDR End-to-End Path Optimization . . . . . . . . . . . . 11
4.5. Network Temporal Congestion Elimination . . . . . . . . . 12 4.5. Network Temporal Congestion Elimination . . . . . . . . . 12
5. CCDR Deployment Consideration . . . . . . . . . . . . . . . . 13 5. CCDR Deployment Consideration . . . . . . . . . . . . . . . . 14
6. Security Considerations . . . . . . . . . . . . . . . . . . . 14 6. Security Considerations . . . . . . . . . . . . . . . . . . . 14
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 15 8. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 15
9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 15 9. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 15
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1. Normative References . . . . . . . . . . . . . . . . . . 15 10.1. Normative References . . . . . . . . . . . . . . . . . . 15
10.2. Informative References . . . . . . . . . . . . . . . . . 15 10.2. Informative References . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
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
maximizing the link utilization within an IP network. maximizing the link utilization within an IP network.
Multiprotocol Label Switching (MPLS) using Traffic Engineering (TE) Multiprotocol Label Switching (MPLS) using Traffic Engineering (TE)
technology (MPLS-TE)[RFC3209]is one solution for traffic engineering technology (MPLS-TE)[RFC3209]is one solution for traffic engineering
network but it introduces an MPLS network and related technology networks but it introduces an MPLS network and related technology
which would be an overlay of the IP network. MPLS-TE technology is which would be an overlay of the IP network. MPLS-TE technology is
often used for Label Switched Path (LSP) protection and complex path often used for Label Switched Path (LSP) protection and complex path
set-up within a domain. set-up within a domain.
It has not been widely deployed for meeting E2E (especially in inter- It has not been widely deployed for meeting E2E (especially in inter-
domain) dynamic performance assurance requirements for an IP network. domain) dynamic performance assurance requirements for an IP network.
Segment Routing [RFC8402] is another solution that integrates some Segment Routing [RFC8402] is another solution that integrates some
advantages of using a distributed protocol and a centrally control advantages of using a distributed protocol and a centrally control
technology, but it requires the underlying network, especially the technology, but it requires the underlying network, especially the
provider edge router, to do a label push and pop action in-depth, and provider edge router, to do a label push and pop action in-depth, and
adds complexity, when coexisting with the Non-Segment Routing adds complexity when coexisting with the Non-Segment Routing network.
network. Additionally, it can only maneuver the E2E paths for MPLS Additionally, it can only maneuver the E2E paths for MPLS and IPv6
and IPv6 traffic via different mechanisms. traffic via different mechanisms.
Deterministic Networking (DetNet)[RFC8578] is another possible Deterministic Networking (DetNet)[RFC8578] is another possible
solution. It is primarily focused on providing bounded latency for a solution. It is primarily focused on providing bounded latency for a
flow and introduces additional requirements on the domain edge flow and introduces additional requirements on the domain edge
router. The current DetNet scope is within one domain. The use router. The current DetNet scope is within one domain. The use
cases defined in this document do not require the additional cases defined in this document do not require the additional
complexity of deterministic properties and so differ from the DetNet complexity of deterministic properties and so differ from the DetNet
use cases. use cases.
This draft describes scenarios for a native IP network that a This draft describes several scenarios for a native IP network where
Centralized Control Dynamic Routing (CCDR) framework can easily a Centralized Control Dynamic Routing (CCDR) framework can produce
solve, without requiring a change of the data plane behavior on the qualitative improvement in efficiency without requiring a change of
router. It also provides path optimization simulation results to the data-plane behavior on the router. Using knowledge of BGP(Border
illustrate the applicability of the CCDR framework. Gateway Protocol) session-specific prefixes advertised by a router,
the network topology and the near real time link utilization
information from network management systems, a central PCE is able to
compute an optimal path and give the underly routers the destination
address to use to reach the BGP nexthop, such that the distributed
routing protocol will use the computed path via traditional recursive
lookup procedure. Some results from simulations of path optimization
are also presented, to concretely illustrate a variety of scenarios
where CCDR shows significant improvement over traditional distributed
routing protocols.
This draft is the base document of the following two drafts: the This draft is the base document of the following two drafts: the
universal solution draft, which is suitable for intra-domain and universal solution draft, which is suitable for intra-domain and
inter-domain TE scenario, is described in inter-domain TE scenario, is described in
[I-D.ietf-teas-pce-native-ip]; the related protocol extension [I-D.ietf-teas-pce-native-ip]; the related protocol extension
contents is described in [I-D.ietf-pce-pcep-extension-native-ip] contents is described in [I-D.ietf-pce-pcep-extension-native-ip]
2. Terminology 2. Terminology
This document uses the following terms defined in [RFC5440]: PCE. This document uses the following terms defined in [RFC5440]: PCE.
skipping to change at page 4, line 28 skipping to change at page 4, line 40
o SR: Service Router o SR: Service Router
o TE: Traffic Engineering o TE: Traffic Engineering
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 where
applying the CCDR framework. 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 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
skipping to change at page 5, line 43 skipping to change at page 5, line 51
Section 4.4 of this document describes the simulation results for Section 4.4 of this document describes the simulation results for
this use case. this use case.
3.2. Link Utilization Maximization 3.2. Link Utilization Maximization
Network topology within a Metro Area Network (MAN) is generally in a Network topology within a Metro Area Network (MAN) is generally in a
star mode as illustrated in Figure 2, with different devices star mode as illustrated in Figure 2, with different devices
connected to different customer types. The traffic from these connected to different customer types. The traffic from these
customers is often in a tidal pattern, with the links between the customers is often in a tidal pattern, with the links between the
Core Router(CR)/Broadband Remote Access Server(BRAS) and CR/Service Core Router(CR)/Broadband Remote Access Server(BRAS) and CR/Service
Router(SR), experiencing congestion in different periods, because the Router(SR) experiencing congestion in different periods, because the
subscribers under BRAS, often use the network at night, and the subscribers under BRAS often use the network at night, and the leased
dedicated line users under SR, often use the network during the line users under SR often use the network during the daytime. The
daytime. The link between BRAS/SR and CR must satisfy the maximum link between BRAS/SR and CR must satisfy the maximum traffic volume
traffic volume between them respectively and this causes these links between them, respectively, and this causes these links often to be
often to be under-utilized. under-utilized.
+--------+ +--------+
| CR | | CR |
+----|---+ +----|---+
| |
--------|--------|-------| --------|--------|-------|
| | | | | | | |
+--|-+ +-|- +--|-+ +-|+ +--|-+ +-|- +--|-+ +-|+
|BRAS| |SR| |BRAS| |SR| |BRAS| |SR| |BRAS| |SR|
+----+ +--+ +----+ +--+ +----+ +--+ +----+ +--+
Figure 2: Star-mode Network Topology within MAN Figure 2: Star-mode Network Topology within MAN
If we consider connecting the BRAS/SR with a local link loop (which 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 is usually lower cost), and control the overall MAN topology with the
CCDR framework, we can exploit the tidal phenomena between the BRAS/ CCDR framework, we can exploit the tidal phenomena between the BRAS/
CR and SR/CR links, maximizing the utilization of these links (which CR and SR/CR links, maximizing the utilization of these central trunk
are usually higher cost). links (which are usually higher cost than the local loops).
+-------+ +-------+
----- PCE | ----- PCE |
| +-------+ | +-------+
+----|---+ +----|---+
| CR | | CR |
+----|---+ +----|---+
| |
--------|--------|-------| --------|--------|-------|
| | | | | | | |
skipping to change at page 7, line 20 skipping to change at page 7, line 20
| ----|----| | | ----|----| |
| | | | | |
+-|-- | ----+ +-|-- | ----+
|IDC|----------------|MAN| |IDC|----------------|MAN|
+---| |---+ +---| |---+
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(s). 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 according to the to schedule the traffic among the different domains according to the
solution described in CCDR framework. solution described in CCDR framework.
3.4. Network Temporal Congestion Elimination 3.4. Network Temporal Congestion Elimination
In more general situations, there are often temporal congestion In more general situations, there are often temporal congestion
within the service provider's network. Such congestion phenomena within the service provider's network, for example due to daily or
often appear repeatedly, and if the service provider has methods to weekly periodic bursts, or large events that are scheduled well in
mitigate it, it will certainly improve their network operations advance. Such congestion phenomena often appear regularly, and if
capabilities and increase satisfaction for their customers. CCDR is the service provider has methods to mitigate it, it will certainly
also suitable for such scenarios, as the controller can schedule improve their network operations capabilities and increase
traffic out of the congested links, lowering the utilization of them satisfaction for their customers. CCDR is also suitable for such
during these times. Section 4.5 describes the simulation results of scenarios, as the controller can schedule traffic out of the
this scenario. congested links, lowering the utilization of them during these times.
Section 4.5 describes the simulation results of this scenario.
4. CCDR Simulation 4. CCDR Simulation
The following sections describe one case study to illustrate CCDR The following sections describe a specific case study to illustrate
algorithm, the topology and traffic matrix generation process and the the workings of the CCDR algorithm with concrete paths/metrics, as
optimization results for E2E QoS assured path and congestion well as a procedure for generating topology and traffic matrices and
elimination in applied scenarios. 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 with the The structure and scale of the simulated topology is similar to that
real network. Several amounts of traffic matrix are generated to of the real networks. Multiple different traffic matrices were
simulate the different congestion condition in network, only one of generated to simulate different congestion conditions in the network,
them is illustrated. but only one of them is illustrated since the others produce similar
results.
4.1. Case Study for CCDR algorithm 4.1. Case Study for CCDR algorithm
Figure 5 depicts the topology of the network for the case study of In this section we consider a specific network topology for a worked
CCDR algorithm. There are 8 forwarding devices in the network. The case study, examining the path selected by OSPF and CCDR and
original cost and utilization are marked on it, as shown in the evaluating how and why the paths differ. Figure 5 depicts the
figure. For example, the original cost and utilization for the link topology of the network in question. There are 8 forwarding devices
(1,2) are 3 and 50% respectively. There are two flows: f1 and f2. in the network. The original cost and utilization are marked on it,
Both of these two flows are from node 1 to node 8. For simplicity, as shown in the figure. For example, the original cost and
it is assumed that the bandwidth of the link in the network is 10Mb/ utilization for the link (1,2) are 3 and 50% respectively. There are
s. The flow rate of f1 is 1Mb/s, and the flow rate of f2 is 2Mb/s. two flows: f1 and f2. Both of these two flows are from node 1 to
The threshold of the link in congestion is 90%. node 8. For simplicity, it is assumed that the bandwidth of the link
in the network is 10Mb/s. The flow rate of f1 is 1Mb/s, and the flow
rate of f2 is 2Mb/s. The threshold of the link in congestion is 90%.
If OSPF protocol (ISIS is similar, because it also use the Dijstra's If OSPF protocol (ISIS is similar, because it also use the Dijstra's
algorithm) is applied in the network, which adopts Dijkstra's algorithm) is applied in the network, which adopts Dijkstra's
algorithm, the two flows from node 1 to node 8 can only use the OSPF 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 path (p1: 1->2->3->8). It is because Dijkstra's algorithm mainly
considers original cost of the link. Since CCDR considers cost and considers original cost of the link. Since CCDR considers cost and
utilization simultaneously, the same path with OSPF will not be utilization simultaneously, the same path as OSPF will not be
selected due to the severe congestion of the link (2,3). In this 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 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 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. 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 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 congestion in the link (6,7). As a result, f2 will select the path
(p3: 1->2->4->7->8). (p3: 1->2->4->7->8).
+-------+ +-------+ +-------+ +-------+
+---------+ f1 +--------->| | ----------> | | +---------+ f1 +--------->| | ----------> | |
skipping to change at page 10, line 7 skipping to change at page 10, line 7
| | 3/60% | | 5/55% | | 3/75%| | | | 3/60% | | 5/55% | | 3/75%| |
| +---------------| 5 |-----------| 6 |----------+ | | +---------------| 5 |-----------| 6 |----------+ |
+--------------> | |---------> | |------------+ +--------------> | |---------> | |------------+
+-------+ +-------+ +-------+ +-------+
(b) CCDR Algorithm (b) CCDR Algorithm
Figure 5: Case Study for CCDR's Algorithm Figure 5: Case Study for CCDR's Algorithm
4.2. Topology Simulation 4.2. Topology Simulation
The network topology mainly contains nodes and links information. Moving on from the specific case study, we now consider a class of
Nodes used in the simulation have two types: core node and edge node. networks more representative of real deployments, with a fully-linked
The core nodes are fully linked to each other. The edge nodes are core network that serves to connect edge nodes, which themselves
connected only with some of the core nodes. Figure 6 is a topology connect to only a subset of the core. An example of such a topology
example of 4 core nodes and 5 edge nodes. In this CCDR simulation, is shown in Figure 6, for the case of 4 core nodes and 5 edge nodes.
100 core nodes and 400 edge nodes are generated. 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|\
| +----+ | | +----+ |
| | | |
| | | |
+----+ +----+ +----+ +----+ +----+ +----+
|Edge|----|Core|-----|Core|---------+ |Edge|----|Core|-----|Core|---------+
+----+ +----+ +----+ | +----+ +----+ +----+ |
/ | \ / | | / | \ / | |
skipping to change at page 10, line 37 skipping to change at page 10, line 40
+----+ +----+ +----+ | +----+ +----+ +----+ |
|Edge|----|Core|-----|Core| | |Edge|----|Core|-----|Core| |
+----+ +----+ +----+ | +----+ +----+ +----+ |
| | | | | |
| +------\ +----+ | +------\ +----+
| ---|Edge| | ---|Edge|
+-----------------/ +----+ +-----------------/ +----+
Figure 6: Topology of Simulation Figure 6: Topology of Simulation
The number of links connecting one edge node to the set of core nodes For the simulations, the number of links connecting one edge node to
is randomly between 2 to 30, and the total number of links is more the set of core nodes is randomly chosen between 2 to 30, and the
than 20000. Each link has a congestion threshold. total number of links is more than 20000. Each link has a congestion
threshold, which can be arbitrarily set to (e.g.) 90% of the nominal
link capacity without affecting the simulation results.
4.3. Traffic Matrix Simulation 4.3. Traffic Matrix Simulation
The traffic matrix is generated based on the link capacity of For each topology, a traffic matrix is generated based on the link
topology. It can result in many kinds of situations, such as capacity of topology. It can result in many kinds of situations,
congestion, mild congestion and non-congestion. such as 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 (100 core nodes plus 400 edge nodes). About 20% of links are
First (OSPF) protocol is used in the network. overloaded when the Open Shortest Path First (OSPF) protocol is used
in the network.
4.4. 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 for each link of the path is far below
threshold. Based on the current state of the network, the PCE within link's congestion threshold. Based on the current state of the
CCDR framework combines the shortest path algorithm with a penalty network, the PCE within CCDR framework combines the shortest path
theory of classical optimization and graph theory. algorithm with a penalty 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 7. The first graph in 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 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.
While real-world results invariably differ from simulations (for
example, real-world topologies are likely to exhibit correlation in
the attachment patterns for edge nodes to the core, which are not
reflected in these results), the dramatic nature of the improvement
in UID and the choice of simulated topology to resemble real-world
conditions suggests that real-world deployments will also experience
significant improvement in UID results.
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| * * * *| | * * * *|
60| * * * * * *| 60| * * * * * *|
|* * ** * * * * * ** * * * * **| |* * ** * * * * * ** * * * * **|
|* * ** * * ** *** ** * * ** * * * ** * * *** **| |* * ** * * ** *** ** * * ** * * * ** * * *** **|
|* * * ** * ** ** *** *** ** **** ** *** **** ** *** **| |* * * ** * ** ** *** *** ** **** ** *** **** ** *** **|
40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **| 40|* * * ***** ** *** *** *** ** **** ** *** ***** ****** **|
UID(%)|* * ******* ** *** *** ******* **** ** *** ***** *********| UID(%)|* * ******* ** *** *** ******* **** ** *** ***** *********|
|*** ******* ** **** *********** *********** ***************| |*** ******* ** **** *********** *********** ***************|
|******************* *********** *********** ***************| |******************* *********** *********** ***************|
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| *| | *|
| * *| | * *|
| * * * * * ** * *| | * * * * * ** * *|
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 7: Simulation Result with Congestion Elimination Figure 7: Simulation Result with Congestion Elimination
4.5. Network Temporal Congestion Elimination 4.5. Network Temporal Congestion Elimination
Different degrees of network congestion were simulated. The During the simulations, different degrees of network congestion were
Congestion Degree (CD) is defined as the link utilization beyond its considered. To examine the effect of CCDR on link congestion, we
threshold. 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 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
about 20%. The second graph shown in Figure 8 is the CD distribution about 20%. The second graph shown in Figure 8 is the CD distribution
after using the congestion elimination process. It shows only 12 after using the congestion elimination process. It shows that only
links among totally 20000 links exceed the threshold, and all the CD 12 links among the total of 20000 links exceed the threshold, and all
values are less than 3%. Thus, after scheduling of the traffic away the CD values are less than 3%. Thus, after scheduling of the traffic
from the congested paths, the degree of network congestion is greatly away from the congested paths, the degree of network congestion is
eliminated and the network utilization is in balance. greatly eliminated and the network utilization is in balance.
Before congestion elimination Before congestion elimination
+-----------------------------------------------------------+ +-----------------------------------------------------------+
| * ** * ** ** *| | * ** * ** ** *|
20| * * **** * ** ** *| 20| * * **** * ** ** *|
|* * ** * ** ** **** * ***** *********| |* * ** * ** ** **** * ***** *********|
|* * * * * **** ****** * ** *** **********************| |* * * * * **** ****** * ** *** **********************|
15|* * * ** * ** **** ********* *****************************| 15|* * * ** * ** **** ********* *****************************|
|* * ****** ******* ********* *****************************| |* * ****** ******* ********* *****************************|
CD(%) |* ********* ******* ***************************************| CD(%) |* ********* ******* ***************************************|
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| | | |
| | | |
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 8: Simulation Result with Congestion Elimination Figure 8: Simulation Result with Congestion Elimination
More detailed information about the algorithm can refer to [PTCS] . It is clear that using an active path-computation mechanism that is
able to take into account observed link traffic/congestion, the
occurrence of congestion events can be greatly reduced. Only when a
preponderance of links in the network are near their congestion
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 5. CCDR Deployment Consideration
Above CCDR scenarios and simulation results demonstrate that it is The above CCDR scenarios and simulation results demonstrate that a
feasible to find one general solution to cope with various complex single general solution can be found that copes with multiple complex
situations. Integrated use of a centralized controller for the more situations. The specific situations considered are not known to have
complex optimal path computations in a native IP network results in any special properties, so it is expected that the benefits
demonstrated will have general applicability. Accordingly, the
integrated use of a centralized controller for the more complex
optimal path computations in a native IP network should result in
significant improvements without impacting the underlay network significant improvements without impacting the underlay network
infrastructure. infrastructure.
For intra-domain or inter-domain native IP TE scenario, the For intra-domain or inter-domain native IP TE scenarios, the
deployment of CCDR solution is similar. This universal deployment deployment of a CCDR solution is similar, with the centralized
characteristic can facilitate the operator to tackle their traffic controller being able to compute paths and no changes required to the
engineering issues in one general manner. To deploy the CCDR underlying network infrastructure. This universal deployment
solution, the PCE should collect the underlay network topology characteristic can facilitate a generic traffic engineering solution,
dynamically, for example via BGP-LS[RFC7752]. It also needs to where operators do not need to differentiate between intra-domain and
gather the network traffic information periodically from the network inter-domain TE cases.
management platform. The simulation results show PCE can compute the
E2E optimal path within seconds thus it can cope with the change of To deploy the CCDR solution, the PCE should collect the underlay
underlay network in minute scale. More agile requirements needs network topology dynamically, for example via BGP-LS[RFC7752]. It
increase the sample rate of underlay network, also decrease the also needs to gather the network traffic information periodically
detection and notification interval of underlay network. The methods from the network management platform. The simulation results show
to gather and decrease the latency of these information are out of that the PCE can compute the E2E optimal path within seconds, thus it
the scope of this draft. can cope with the change of underlay network on the scale of minutes.
More agile requirements would need to increase the sample rate of
underlay network and decrease the detection and notification interval
of the underlay network. The methods to gather and decrease the
latency of these information are out of the scope of this draft..
6. Security Considerations 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 PCE Solutions for CCDR scenarios need to consider protection of the PCE
and communication with the underlay devices. and communication with the underlay devices.
skipping to change at page 15, line 15 skipping to change at page 15, line 29
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
The author would like to thank Deborah Brungard, Adrian Farrel, The author would like to thank Deborah Brungard, Adrian Farrel,
Huaimo Chen, Vishnu Beeram and Lou Berger for their support and Huaimo Chen, Vishnu Beeram and Lou Berger for their support and
comments on this draft. comments on this draft.
Thanks Benjamin Kaduk, Roman Danyliw, Alvaro Retana and Eric Vyncke Thanks Benjamin Kaduk for his careful review and valuable suggestions
for their views and comments. to this draft. Also thanks Roman Danyliw, Alvaro Retana and Eric
Vyncke for their views and comments.
10. References 10. References
10.1. Normative References 10.1. Normative References
[RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation [RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation
Element (PCE) Communication Protocol (PCEP)", RFC 5440, Element (PCE) Communication Protocol (PCEP)", RFC 5440,
DOI 10.17487/RFC5440, March 2009, DOI 10.17487/RFC5440, March 2009,
<https://www.rfc-editor.org/info/rfc5440>. <https://www.rfc-editor.org/info/rfc5440>.
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