Load shared sequential routing in MPLS networks: system and user optimal solutions

Authors:
Gilles Brunet;Fariba Heidari;Lorne G. Mason
Affiliations:
Videotron Ltd., Montreal, QC, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
Venue:
NET-COOP'07 Proceedings of the 1st EuroFGI international conference on Network control and optimization
Year:
2007

Citing 7
Cited 1

Effective bandwidths at multi-class queues

Queueing Systems: Theory and Applications
Computational techniques for accurate performance evaluation of multirate, multihop communication networks

IEEE/ACM Transactions on Networking (TON)
Decentralized Adaptive Flow Control of High-Speed Connectionless Data Networks

Operations Research
Traffic Engineering and QoS Optimization of Integrated Voice & Data Networks

Traffic Engineering and QoS Optimization of Integrated Voice & Data Networks
Reinforcement learning-based load shared sequential routing

NETWORKING'07 Proceedings of the 6th international IFIP-TC6 conference on Ad Hoc and sensor networks, wireless networks, next generation internet
Performance evaluation of QoS-routing methods for IP-based multiservice networks

Computer Communications
QoS online routing and MPLS multilevel protection: a survey

IEEE Communications Magazine

Reinforcement learning-based load shared sequential routing

NETWORKING'07 Proceedings of the 6th international IFIP-TC6 conference on Ad Hoc and sensor networks, wireless networks, next generation internet

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently Gerald Ash has shown through case studies that event dependent routing is attractive in large scale multi-service MPLS networks. In this paper, we consider the application of Load Shared Sequential Routing (LSSR) in MPLS networks where the load sharing factors are updated using reinforcement learning techniques. We present algorithms based on learning automata techniques for optimizing the load sharing factors both from the user equilibrium and system optimum perspectives. To overcome the computationally expensive gradient evaluation associated with the Kuhn-Tucker conditions of the system optimum problem, we derive a computationally efficient method employing shadow prices. The proposed method for calculating the user equilibrium solution represents a computationally efficient alternative to discrete event simulation. Numerical results are presented for the performance comparison of the LSSR model with the user equilibrium and the system optimum load sharing factors in some example network topologies and traffic demands.