PERFORMANCE AND COMPLEXITY EVALUATION OF

MULTI-PATH ROUTING ALGORITHMS FOR MPLS-TE

K. Abboud, A. Toguyeni and A. Rahmani

Ecole centrale de Lille, LAGIS CNRS UMR 8146, BP 48, 59651 Villeneuve d’ASCQ, France

Keywords:

MPLS Trafﬁc Engineering, Load balancing, Differentiated Services, Topology generation, Performance.

Abstract:

This paper discusses and evaluates the behaviour of a DS-TE algorithm (DiffSev aware MPLS trafﬁc Engi-

neering) called PEMS, and a dynamic multipath routing algorithm for load balancing (LBWDP), applied on a

huge topology that correspond to real network. To clarify network topologies and routing algorithms that are

suitable for MPLS Trafﬁc Engineering, we evaluate them from the viewpoint of network scalability and end-

to-end quality. We characterize typical network topologies and practical routing algorithms. Using a network

topology generated by BRITE, that has many alternative paths, can provide a real simulation of the internet

and a good evaluation for the end-to-end quality and the network use. In this paper, we ﬁrst review MPLS-TE,

DiffServ and load balancing. We then discuss the general issues of designing for a lot of DS-TE and load

balancing algorithms. Based on our works, a generic procedure for deploying and simulating these algorithms

is proposed. We also discuss the results and a comparison between the algorithms. Putting these together,

we present a practical issue of Trafﬁc Engineering, load balancing and a working solution for DS-TE in the

Internet.

1 INTRODUCTION

With increasing demands for broadband IP net-

works, for enterprise intranets, ISPs (Internet service

providers), datacenters and so on, MPLS (Multi

Protocol Label Switching) (MPLS, 2001) is looking

promising as a backbone technology for broadband

IP networks. With MPLS, routes are calculated at

source routers, called Ingress Routers, which take

into account not only the network topology but

also trafﬁc oriented constraint (such as bandwidth,

delay, hop count) and administrative constraints (i.e.

some links or nodes are preferred for certain trafﬁc

demands). The network operator therefore has a

greater control over how trafﬁc is routed and trafﬁc

engineering can be more effective. This IP network

control technology is called Trafﬁc Engineering, and

it is standardized in the IETF TE Working Group

(IETF, ). Trafﬁc Engineering improves and controls

the total network use efﬁciency and end-to-end

quality of service. Speciﬁcally, it prevents congestion

being caused by trafﬁc deviation even when there are

sufﬁcient physical network resources.

The purpose of Load balancing is to reduce the

load of each link and to increase service availability.

Multipath routing is one of load balancing mecha-

nisms in TE. With multipath routing algorithm, an

ingress router distributes the demand on multiple

paths in the network with dynamic rates instead of

routing all the trafﬁc on only one path. It concerns

how to select paths and distribute trafﬁc among

those paths such that given quality of service (QoS

hereafter) constraints are met or close to the target.

Following this assessment, a new combined load

balancing algorithm for multipath QoS based on

MPLS called LBWDP (LBWDP, 2005) will be

presented (combination from WDP [Widest Disjoint

Paths] (WDP, 2002) for candidate path selection

and PER [Prediction of Effective Repartition] (PER,

2006) for trafﬁc splitting).

MPLS has an ability to support the QoS models de-

veloped for IP by IETF to address QoS requirements

in Internet Service providers (ISPs) networks. The

two models used in IP networks for QoS provision

are: Integrated Services, which is based on Reser-

vation Protocol (RSVP) and DiffServ model. In

case of DiffServ model, trafﬁc ﬂows are aggregated

into a limited number of classes, each served at

routers according to a given Per-Hop-Behavior

(PHB), e.g. determining the service priority or

119

Abboud K., Toguyeni A. and Rahmani A. (2008).

PERFORMANCE AND COMPLEXITY EVALUATION OF MULTI-PATH ROUTING ALGORITHMS FOR MPLS-TE.

In Proceedings of the Third International Conference on Software and Data Technologies - PL/DPS/KE, pages 119-126

DOI: 10.5220/0001889101190126

 SciTePress

the discarding probability in case of congestion.

Routers only need to be able to support the different

PHBs. Furthermore, MPLS trafﬁc engineering (TE)

capability augmented with constraint-based routing,

has the ability to compute routes with constraints on

bandwidth and delay requirements. If the two tech-

nologies are combined, then standardized DiffServ

service offerings can be made. The combination of

MPLS and DiffServ is called DS-TE (DiffServ aware

MPLS Trafﬁc Engineering) (DS-TE, ).

This paper is organized as follows: Section 2 de-

scribes different algorithms of Trafﬁc Engineering us-

ing MPLS. Section 3 lists and compares a set of

topology generators. Section 4 deﬁnes our simula-

tion model. Section 5 describes experiments and the

analyses of results. Finally, the conclusion gives some

perspectives of this study.

2 ALGORITHMS

2.1 Introduction

Multipath routing algorithm consists in two main

steps as depicted by Figure 1: computation of mul-

tiple paths and trafﬁc splitting among these multiple

paths. In the ﬁrst step, it computes the set of candidate

paths which is a subset of all the paths between a pair

of considered routers according to various static cri-

teria such as bandwidth, hop count, delay, error ratio

and so on ...

The second step is to split trafﬁc among multiple

candidate paths. The repartition rate of a demand on

candidate paths depends on the evaluation of dynamic

criteria such as the blockages, the packet loss ratio,

the measured delay, the jitter, and so on ...

2.2 LBWDP (Load Balancing over

Widest Disjoint Paths)

LBWDP (LBWDP, 2005) is a hybrid algorithm that

takes advantages of the path selection of WDP (WDP,

2002) and the splitting mechanism of PER (PER,

2006). For ﬁnding the candidate path set, LBWDP

uses the existing WDP (Widest Disjoint Paths) (WDP,

2002) algorithm which focuses on the selection of

good paths. This approach is mainly based on two

concepts: path width and path distance. Path width

is a way to detect bottlenecks in the network and to

avoid them if possible. Path distance is original be-

cause contrary to most approaches, it is not a hop-

count measure but it is indirectly dependent on the

Figure 1: General schema of Multipath routing algorithm.

utilization ratio of each link deﬁning the path. WDP

algorithm performs candidate paths selection based

on the computation of the width of the good disjoint

paths with regard to bottleneck links. The width of a

path is deﬁned as the residual bandwidth of its bot-

tleneck link. The principle of WDP is to select a re-

stricted number of paths. A path is added to the sub-

set of good paths if its inclusion increases the width

of this subset. At the opposite, a path is deleted if this

does not reduce the width of the subset of good paths.

For the trafﬁc splitting stage, LBWDP uses the PER

algoritm (PER, 2006). It consists of two steps: cal-

culating a distribution probability and selecting one

path using Gradient Method with Constraint band-

width trafﬁc. For more details, please refer to (PER,

2006).

This trafﬁc engineering scheme will be useful for re-

ducing the probability of congestion by minimizing

the utilization of the most heavily used link in the net-

work.

2.3 PEMS (PEriodic Multi-Step

Routing Algorithm for DS-TE)

Since MPLS TE (Trafﬁc Engineering based on

MPLS) and DiffServ (Differentiated Services) can be

deployed concurrently in an IP backbone, a proposed

algorithm called PEMS (PEMS, 2006) is useful to

meet the customers’ various requirements and to give

the differentiated services for three classes in the DS-

TE network. Its goal is to develop a routing method

that optimizes differentiation of experienced service

of trafﬁc classes in terms of two metrics, delay and

available bandwidth.

The classes managed by PEMS are: Expedited

Forwarding (EF) for delay sensitive trafﬁc like VoIP

trafﬁc, Assured Forwarding (AF) for soft bandwidth

and loss guarantee trafﬁc like video and Best-Effort

forwarding (BE) for assuring the minimum quality

to best effort service like ftp or e-mail. PEMS aims

basically to minimize the maximum link utilization

over the network, and additionally to give different

service quality to each class, especially to ensure the

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120

low delay to EF class.

PEMS, has three stages (Figure 2): preprocess-

ing stage, candidate paths computing stage and de-

mand splitting stage for LSP (Label Switched Path)

requests. In the preprocessing stage, it extracts good

paths in order to avoid online searching overhead.

This stage uses only topology information. In the

online mode, when link state information are up-

dated, new candidate paths for each class are calcu-

lated based on updated information such as measured

delay and residual bandwidth. It calculates the split-

ting probability with different weights of delay and

bandwidth depending on the class of requested traf-

ﬁc. When a trafﬁc demand arrives, it performs PER

algorithm (PER, 2006) to select one LSP between the

set of candidate paths to carry current ﬂows. PEMS

aims to minimize the maximum link utilization like

LBWDP algorithm basically, at the same time to give

different service quality to each class, especially to

guarantee the low delay to EF class. For more details

of PEMS, refer to (PEMS, 2006).

Figure 2: Three stages of PEMS.

Figure 3 shows some characteristics for LBWDP

and PEMS from the view point of structure and ob-

jective.

Figure 3: LBWDP and PEMS features.

2.4 Models Scalability and Algorithms

Complexity

The proposed models must have a complexity al-

lowing them to be deployed on complex network

such like internet: it is the scalability problem. The

scalability depends on two factors: the extent of the

deployment and the complexity of the algorithms

that implement the model. In term of deployment,

LBWDP and PEMS reduce the size phenomenon

because they require a full implementation as ingress

routers only. Indeed, in the case of LBWDP, once

the network selects the ingress routers, the role of

the core routers is limited to the implementation of

mechanisms for MPLS. In the case of PEMS, the

core routers are in addition to those mechanisms to

implement the differentiation packages for Diffserv.

So, the scalability of our models is depending

on the algorithms complexity of paths selection and

load balancing algorithms implemented on the ingress

routers. This complexity can be expressed with differ-

ent criteria: the computational time and the memory

space used. In this study, we focus on the computa-

tional time to estimate routing plans after each Link

State Update. This time calculation can be theoreti-

cally approximated by the number of iterations in al-

gorithm. Figure 4 shows the theoretical complexity

of two algorithms depending on the steps that form

them. Thus, we see that the costly steps in terms of

time are those relating to the paths selection.

Figure 4: Algorithms complexity.

3 TOPOLOGY GENERATORS

3.1 Introduction

There are several synthetic topology generators avail-

able to the networking research community (Waxman

(Waxman, 1988), Inet (Inet, 2000), GT-ITM (GT-

ITM, 1997), Tiers (Tiers, 1996), BRITE (BRITE,

2001)..). Many of them differ signiﬁcantly with re-

spect to the characteristics of the topologies they gen-

erate. An ideal topology generator should enable the

use and development of generation models that pro-

duce accurate representations of Internet topologies.

In this section, we focus on the BRITE generation tool

that we choosed for the simulation after a study and

a comparison done on the set of generators listed be-

fore.

3.2 BRITE

BRITE (BRITE, 2001) is designed to be a ﬂexible

topology generator. As show in Figure 5, it sup-

ports multiple generation models (AS level, Router

PERFORMANCE AND COMPLEXITY EVALUATION OF MULTI-PATH ROUTING ALGORITHMS FOR MPLS-TE

121

Figure 5: BRITE Structure.

Figure 6: BRITE Architecture.

level, Hierarchical ..) that has several degrees of free-

dom with respect to how the nodes are placed in the

plane (Random or Heavly tailed) and the properties

of the interconnection method to be used (Waxman or

Barabasi-Albert).

Figure 6 shows the main architecture of BRITE.

BRITE reads the generation parameters from a

conﬁguration ﬁle (1) that can be either hand written

by the user or automatically generated by BRITE´ s

GUI. It provides the capability of importing topolo-

gies (2) generated by other topology generators

(GT-ITM , Inet , Tiers ) or topological data gathered

directly from the Internet (NLANR (NLANR, 2001),

Skitter (Skitter and McRobb, 2001)). In the current

distribution BRITE produces a topology in its own

ﬁle format (3), and it has an output capabilities for

producing topologies that can be used by the Net-

work Simulator (NS2) and the Scalable Simulation

Framework simulators.

Brite has BRITE Analysis Engine or BRIANA (4).

The idea of BRIANA is to provide a set of analysis

routines that may be applied to any topology that can

be imported into BRITE.

The speciﬁc details regarding how a topology is

generated depend on the speciﬁc generation model

being used. We can think of the generation process

as divided into a four-step process:

1. Placing the nodes in the plane

2. Interconnecting the nodes

3. Assigning attributes to topological

components (delay and bandwidth for

links...)

4. Giving the topology in a speciﬁc format

Figure 7: Level comparison.

Figure 8: Model comparison.

BRITE has two very similar implementation

methods (Java and C++) and can be extended by in-

corporating new generation models to its framework.

Since our objective caters to choice the best gen-

erator that represents efﬁciency and generally the in-

ternet, we present two comparison tables between

BRITE and the other generators based on two criteria:

topology nature and topology level. Figure 7 shows

a comparison in term of generation level. We can

see that BRITE provides a model in two levels: AS

and Router levels, that is not the case for the others.

Similarly, Figure 8 shows that from the view point of

topology nature (Hierarchical or Degree based), only

BRITE implements the 2 models. This two compar-

isons ensure that BRITE correlate closely with real

netwok topologies in term of topology nature and

topology level.

4 SIMULATION MODEL

4.1 Simulation Tools

We used an event-driven network simulator NS2

(NS2, ) to simulate the dynamic nature of a network.

NS2 is developed at the Lawrence Berkeley National

Laboratory (LBNL) of the University of California,

Berkeley (UCB). NS2 has been integrated with the

MNS patch. This patch gives to the simulator a good

support to the establishment of CR-LSP (Constraint

based Routing-Label Switching Path) for QoS trafﬁc

as well as basic MPLS functions as LDP (Label

Distribution Protocol) and label switching.

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122

As we descriped below, we used BRITE to gener-

ate the simulation topology that must be compatible

with NS2. For this end, we have developped a script

that we called brite2ns that it is capable to generate

a tcl ﬁle for each topology used to simulate the algo-

rithms.

4.2 Model

A simulation is deﬁned by an OTcl script. Running a

simulation involves creating and executing ﬁles with

a ’.tcl’ extension. For each algorithm, we deﬁne three

tcl ﬁles.

First we deﬁne a Tcl script containing the network

topology generated by BRITE and transformed in

NS2 format by the brite2ns script. This topology in-

cludes the nodes (nodes and MPLS nodes) and links

between nodes. Then we add the routing algorithm

that it is capable to search the all path between each

source/destination pair wich we determine the num-

ber of hopcounts between it. Note that the hop count

determination has little effect in the simulation. We

insert a list of commands to conﬁgure the LDP Pro-

tocol, CR-LDP and the MPLS QoS trafﬁc. Multiple

sources of trafﬁc and multiple destinations are con-

nected to some of routers. The link bandwith, the de-

lay and the queue type between two routers are spec-

iﬁed on the link between them. Moreover, the total

parametres are assumed to be the same in all cases to

equalize the topology costs. Parameters used for the

simulation topology are listed in Figure 9.

Figure 9: Topology parameters.

Secondly, we deﬁne the trafﬁc class between I/O

LSR. In this tcl ﬁle, we determine the time of sim-

ulation, the volume of each individual demand, the

trafﬁc distribution, the start and stop time of a trafﬁc

session and other parametres that speciﬁes each algo-

rithms (Trafﬁc proﬁle in Annex). We simulate all the

algorithms with the same trafﬁc scenario. Figure 10

shows the trafﬁc parametres used for the simulations.

Finally, a tcl script containing the algorithm to

simulate is made, and employed the two tcl scripts

Figure 10: Trafﬁc parameters.

listed below (Topology and Trafﬁc). In this script, we

collects statistics and outputs the results of the simu-

lation. Results are usually written to ﬁles, including

ﬁles for Nam.

5 EVALUATION AND ANALYSIS

5.1 Evaluation Criteria

The main purpose of Trafﬁc Engineering and Diff-

Serv is to control the end-to-end quality by improving

an efﬁciency use of network resources. An evaluation

that considers both viewpoints is necessary. It is also

necessary to evaluate the scalability of our algorithms.

For this simulation experiments, end-to-end delay and

link utilization are chosen to be the performance met-

rics of interest. The link utilization which is good to

be a performance metric is selected to show whether

the network load is well balanced. End-to-end delay

is added as a new performance metric in this simu-

lation to estimate whether delay-sensitive trafﬁc, EF

trafﬁc, can be guaranteed its appropriate service. To

estimate delay, queue monitor is used in NS simulator.

5.2 Evaluation Graphs and Results

The simulation has been made with the purpose of a

differents numbers of highly redundant nodes MPLS

network core, basically to studying the response of

the algorithms on the topology increase, speciﬁcally

in term of network use and end-to-end quality.

We simulate topologies with a growing number

of nodes (100, 200 and 300). The graphs were

made from data obtained by the simulations of these

different topologies, and they are placed in scales in

3D form to better analyze and compare the results.

We can analyze the load-balancing capacity of two

models using the ﬁgures 11, 12 and 13. These ﬁgures

give us respectively as function of time, the maximum

rate utilization of different topologies links for each

model. They show that LDWDP gets more efﬁcient

result for load balancing than PEMS and it seems

PERFORMANCE AND COMPLEXITY EVALUATION OF MULTI-PATH ROUTING ALGORITHMS FOR MPLS-TE

123

Figure 11: Maximum link utilization for 100 nodes.

Figure 12: Maximum link utilization for 200 nodes.

Figure 13: Maximum link utilization for 300 nodes.

more scalable, we can see easily that the links are less

saturated with LBWDP than PEMS.

As regards the maximum utilization (Figure 14),

LBWDP takes the minimum ”maximum utilization”

throughout all topologies simulated, and it is clear

from the difference between the scales of two mod-

els, which is also constant throughout all topologies

from 100 to 300 nodes, that the load through the net-

work still well balanced with LBWDP than PEMS.

This result can be explained by the fact that PEMS

is more complex than LBWDP especially in paths se-

lection part or in promosing certain paths to speciﬁc

trafﬁc classes like EF that needs a low delay.

Elsewhere, this conclusion is conﬁrmed by the

analysis of the average link utilization for the two

models (Figure 15). In this ﬁgure, we can observe that

the gap between the scales of two models is smaller

than that in the case of the maximum link utiliza-

Figure 14: Maximum link utilization.

Figure 15: Average link utilization.

Figure 16: LBWDP average delay.

tion, which expresses the proper load balancing of

LBWDP that distributes better the load on the set of

links. We remark also that the scales degrade less

and less from 100 nodes to 300 nodes, that may be

referring to the fact that when the number of nodes

increases, the possibility to get better paths increases

also.

By contrast, as regards the delay, if we look at the

graphs in ﬁgures 16 and 17, we remark that PEMS

(Figure 17) can differentiate the measured delay ac-

cording to each class of trafﬁc and it gives the best

delay for the EF trafﬁc in contrast with LBWDP (Fig-

ure 16) that gets results for all classes indifferently

and it didn’t privilege the EF trafﬁc. This tendency is

also conﬁrmed by the perturbation of LBWDP scales

in ﬁgure 16 from 100 to 300 nodes, against a remark-

able stability of PEMS scales in Figure 17.

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124

Figure 17: PEMS average delay.

6 CONCLUSIONS

In this paper, we have described and evaluated a

framework consisted of two algorithms (LBWDP and

PEMS), and concentrated on load balancing for pro-

viding QoS and TE objective through the multipath

routing in the IP-based MPLS and DS-TE network.

We simulate them using a single topology each time

for 3 cases (100, 200 and 300 nodes), a single sig-

nalling protocol and a single type of trafﬁc. By sim-

ulation, LBWDP gets more efﬁcient result for load

balancing than PEMS. We have showen that LBWDP

can balance the load and minimize the maximum link

utilization better than PEMS because it does not con-

cern a speciﬁc class, while PEMS can differentiate the

measured delay according to the class, that LBWDP

gets the results for all classes indifferently. Other sim-

ulations represent subject to an actual work will be

presented in future papers.

In the future, we will integrate other load balancing

algorithms for MPLS TE and DS-TE and we will

compare them for the view point of scalability and

end-to-end quality with increasing the topology level

to prove and determine which algorithm is the best.

Also, PEMS can be improved by adapting dynami-

cally the parametres of the trafﬁc splitting stage de-

pending on the network state, or extended for more

trafﬁc classes. Other combinations could be made

in the future, per example the combination of WDP,

ﬁrst step of LBWDP, with the trafﬁc splitting part of

PEMS. A research is in way to implemente this work

in the ad hoc wireless technology.

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APPENDIX

Figure 18: EF Trafﬁc in Time/Trafﬁc source.

PERFORMANCE AND COMPLEXITY EVALUATION OF MULTI-PATH ROUTING ALGORITHMS FOR MPLS-TE

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Figure 19: AF Trafﬁc in Time/Trafﬁc source.

Figure 20: BE Trafﬁc in Time/Trafﬁc source.

Figure 21: Loss packets Rate.

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