Network traffic engineering

Network traffic engineering

Toolbox, hybrid IP/MPLS optimisation method and fairness

Fabian Skiv ´ee

Research Unit in Networking EECS Department University of Li `ege

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Outline

1 Introduction

2 _{A toolbox of TE methods} 3 _{A new hybrid IP/MPLS method} 4 _{Fairness in MPLS networks} 5 _Conclusion

Outline

1 _Introduction

Introduction MPLS principles

2 A toolbox of TE methods

3 _{A new hybrid IP/MPLS method}

4 _{Fairness in MPLS networks}

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Introduction MPLS principles

Introduction

Today, most operators overprovision their network.

But increasing demand and quality of service requirements means this approach is less and less tenable economically. Traffic Engineering (TE) involves adapting the routing of traffic to the network conditions, with the joint goals of good user performance and efficient use of network resources.

MPLS principles

Multi Protocol Label Switching (MPLS) allows to establish tunnels (LSPs, label switched paths) in the topology, along explicit routes (e.g. using RSVP-TE).

Routing is not only destination-based any more LSPs can be bandwidth guaranteed

Advantages

We can freely choose the path and the granularity of these LSPs

These are independent from each other

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion Objectives Architecture Applications

Outline

3 A new hybrid IP/MPLS method

4 Fairness in MPLS networks

Toolbox objectives

Lots of research in TE domain but not used by operators because of their integration and usage complexity. Our TOTEM toolbox has two objectives:

Allow researchers to promote their new TE methods and compare them with other existing ones.

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion Objectives Architecture Applications

Architecture requirements

The architecture must meet several requirements: Minimise new algorithm integration effort Be interoperable with existing tools

Allow the integration of algorithms written in multiple languages

Allow different execution modes

on-line in a real network off-line for simulation purposes

Toolbox integration

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion Objectives Architecture Applications

Toolbox architecture

IGP metric optimiser

LSP path computation Backup LSP path computation BGP decision process simulation Simulation Scenario CONTROL

Optimise and simulate

Link load analysis Path delay analysis Inter domain traffic analysis Analyse report BGP routing table

Toolbox applications

Analyse link load associated with a traffic matrix Simulate link failure

Compare link load based on several traffic matrices Optimise the IP metric

Compute near-optimal MPLS full mesh using DAMOTE Compute hybrid IP/MPLS solution using SAMTE Simulate inter-domain routing using C-BGP simulator Compute traffic matrix from netflow monitoring data Generate topology and traffic matrix using Topgen generator

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

Outline

1 Introduction

2 A toolbox of TE methods

3 A new hybrid IP/MPLS method

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

4 _{Fairness in MPLS networks}

Objective

Objective: find a small number of LSPs to optimize a given operational need like minimize max load, load balancing,... Advantages

Scalable: just a few LSPs, i.e. not a full mesh

Incremental: you know exactly which traffic will be routed on the LSPs

Generic: you can combine different LSPs for different objectives such as delay, max load,...

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

Hybrid IP/MPLS routing models

1 2 3 4 5 6 F2,5 F2,6 F1,6 LSP 2 2 2 2 1 2 2 2 1 2 3 4 5 6 F2,5 F2,6 F1,6 LSP 2 2 2 2 1 2 2 2 1 2 3 4 5 6 F2,5 F2,6 F1,6 LSP 2 2 2 2 1 2 2 2

Overlay Basic IGP Shortcut IGP shortcut

All flows entering

the network at

node 2 and exiting it at node 6 will be forwarded on the LSP

All flows exiting the network at node 6 will be forwarded on the LSP (if they cross node 2 before)

All flows crossing node 2 and then node 6 will be for-warded on the LSP

SAMTE : hybrid IP/MPLS method

Compute a solution of K LSPs to optimise your objective Generate a ”candidate path list” with P shortest path for each pair origin/destination.

Based on simulated annealing meta heuristic

Initial solution : Generate a set of K LSPs at random Neigbourhood : replace a LSP of the solution by a LSP of the candidate list

Objective functions (bottleneck) : minimize maximum link utilisation load balancing (combined with shortest path)

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

Simulated annealing execution

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0 2000 4000 6000 8000 10000

Maximum link load

Simulated annealing execution

Current solution Best solution Temperature

G ´

EANT network

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

Minimize maximum link utilisation on GEANT

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 5 10 15 20 25 Link load

Link load with differents number of LSPs

Maximum link load Mean link load Std of the link load Percentile 10 of the link load

Comparaison with other TE methods

Method #LSP Max Per10 Mean Std CPU time

in % in % in % in % MCNF 506 41.9 - - - ¿ 2 days SPF-G ´EANT 0 70.7 23.0 7.1 11.5 0 SPF-InvCap 0 46.3 22.3 6.9 9.6 0 IGP-WO 0 45.1 22.2 7.2 9.6 315 s DAMOTEα=2 506 41.9 16.1 8.5 7.5 2.5 s G ´EANT + SAMTEf_ML 4 42.1 24.5 7.5 10.7 1.0 s G ´EANT + SAMTEf_LB 12 42.5 18.9 7.8 8.4 3.1 s G ´EANT + SAMTEf_LB 23 42.5 16.5 7.8 8.0 13.8 s

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

Description

Objective : When and how to re-engineer (update the set of LSPs) the network according to traffic evolution ? Maximize the operational objective

Minimize the number of re-optimisations

Simulations done on one month of traffic matrices of Geant (computed with Netflow)

Two strategies evaluated

The utopic strategy: At the beginning of each period of 15 minutes, we consider that the next traffic matrix is known exactly (which is obviously impossible) and SAMTE is used to set up 5 new LSPs to replace the former 5. [Reference] The TM-max strategy: we build TM-max which is a traffic matrix composed of the maximal traffic over one month for each pair. Then TM-max will be used as input to SAMTE to compute 5 LSPs for the next month. [Proposition]

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Objective

Hybrid IP/MPLS routing models SAMTE

Simulations

Re-optimisation strategy for dynamic TE

Comparison of the two strategies

-10 0 10 20 30 40 50 30/04 07/05 14/05 21/05 28/05 04/06 % Time Summary Worst max load:

66.9 % - TM-max 45.2 % - each TM ⇒48 % of increasing Average increase : 3.8 % Each TM rerouting In average 3.5 % of the pairs 1.6 % of the volume Maximum 11.46% of the pairs 25.3% of the volume

Outline

1 Introduction

2 A toolbox of TE methods

3 A new hybrid IP/MPLS method

4 _{Fairness in MPLS networks}

Objectives Max-min fairness Distributed algorithm Simulation

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion Objectives Max-min fairness Distributed algorithm Simulation

Objectives

Our goal : sharing the available bandwidth among all the LSPs according to their weights

The classical max-min rate allocation policy has been accepted as an optimal network bandwidth sharing criterion among user flows.

Extension with a weight : WPMM (Weighted Proportional Max-Min)

Weighted Proportional Max-min fairness

A fair share allocates a LSP with a ”small” demand what it wants, and distributes unused resources evenly to the ”big” LSPs according to their weights

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion Objectives Max-min fairness Distributed algorithm Simulation

Proposed distributed WPMM algorithm

Periodically, the ingress sends a PATH packet

Each router computes a local fair share for the LSP and updates a new RSVP field (called ER, explicit rate) if its local fair rate is less than the actual fair rate

Upon receiving a PATH packet, the egress router sends a RESV packet.

Each router updates its information with the RESV parameters.

Simulation

For stabilizing 90% of the LSPs, our solution takes 4 iterations (16 with Hou’s one). In the worst topology (among 63

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Outline

1 Introduction

2 _{A toolbox of TE methods}

3 _{A new hybrid IP/MPLS method}

4 _{Fairness in MPLS networks}

Conclusion

The toolbox is now really pertinent for researchers and operators. It could become a tool to accelerate the developement of recent traffic engineering methods The proposed hybrid IP/MPLS method is a scalable and very flexible method to traffic engineer a network

Other contraints could be added, like resilience, combination with IP-FRR, re-optimisation strategy This work, characterised by its pragmatic approach, focuses on real needs in current high speed networks

Introduction A toolbox of TE methods A new hybrid IP/MPLS method Fairness in MPLS networks Conclusion

Publications

G. Leduc, H. Abrahamsson, S. Balon, S. Bessler, M. D’Arienzo, O. Delcourt, J. Domingo-Pascual, S. Cerav-Erbas, I. Gojmerac, X. Masip, A. Pescap `e, B. Quoitin, S. P. Romano, E. Salvadori, F. Skiv ´ee, H. T. Tran, S. Uhlig, and H. ¨Umit. An open source traffic engineering toolbox. To appear in Computer

Communications, 2005. (18 pages)

F. Skiv ´ee, S. Balon, O. Delcourt, J. Lepropre, and G. Leduc. Architecture d’une boˆıte `a outils d’algorithmes d’ing ´enierie de trafic et application au r ´eseau G ´EANT. Actes de Colloque Francophone sur l’Ing ´enierie des Protocoles (CFIP), pages 317-332, Bordeaux, France, 29 Mar.-1 Avr. 2005. Herm `es Lavoisier. F. Skiv ´ee and G. Leduc. A Distributed Algorithm for Weighted Max-Min Fairness in MPLS Networks. Proc. of 11th IEEE International Conference on

Telecommunications (ICT’2004), 1-6 Aug. 2004, Fortaleza, Brazil, J. Neuman de

Souza, P. Dini, P. Lorenz (eds.), Telecommunications and Networking, LNCS, 3124, pp. 644-653, Springer Verlag.

S. Balon, F. Skiv ´ee, G. Leduc. Comparing traffic engineering objective functions.

Proc. of the 1sh ACM CoNEXT student workshop, October 24-27, 2005,

Submitted papers

S. Balon, O. Delcourt, J. Lepropre, F. Skiv ´ee and G. Leduc. A traffic engineering toolbox and its application to the GEANT network. IEEE eTransactions on

Network and Service Management. (11 pages)

F. Skiv ´ee, S. Balon and G. Leduc. A scalable heuristic for hybrid IGP/MPLS traffic engineering - Case study on the GEANT network. IEEE International