Learning automata: an introduction
Learning automata: an introduction
Introduction to algorithms
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
DORA: Efficient Routing for MPLS Traffic Engineering
Journal of Network and Systems Management
Traffic Engineering with MPLS
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
A New Class of Online Minimum-Interference Routing Algorithms
NETWORKING '02 Proceedings of the Second International IFIP-TC6 Networking Conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; and Mobile and Wireless Communications
Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Guest editorial learning automata: theory, paradigms, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Profile-based routing and traffic engineering
Computer Communications
Quality-of-service routing for supporting multimedia applications
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
LACAS: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers & Mathematics with Applications
A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks
The Journal of Supercomputing
Stochastic Learning for SAT-Encoded Graph Coloring Problems
International Journal of Applied Metaheuristic Computing
| Hi-index | 14.98 |
This paper presents an efficient adaptive online routing algorithm for the computation of bandwidth-guaranteed paths in Multiprotocol Label Switching (MPLS)-based networks by using a learning scheme that computes an optimal ordering of routes. The contribution of this work is twofold. The first is that we propose a new class of solutions other than those available in the literature, incorporating the family of stochastic Random Races (RR) algorithms. The most popular previously proposed MPLS-based Traffic Engineering (TE) solutions attempt to find a superior path to route an incoming setup request. Our algorithm, on the other hand, tries to learn an optimal ordering of the paths through which requests can be routed according to the rank of the paths in the order learned by the algorithm. The second contribution of our work is that we have proposed a routing algorithm that has a performance superior to the important algorithms in the literature. Our conclusions are based on three important performance criteria: 1) the rejection ratio, 2) the percentage of accepted bandwidth, and 3) the average route computation time per request. Although some of the previously proposed algorithms were designed to achieve low rejection and high throughput of route requests, they are unreasonably slow. Our algorithm, on the other hand, in general attempts to reject the least number of requests, achieves the highest throughput, and computes routes in the fastest possible time when compared to the algorithms that we used as benchmarks for comparison.