Publication:
A robust GA-based QoS routing algorithm for solving multi-constrained path problem

Date
2010
Authors
Yussof S.
See O.H.
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Research Projects
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Abstract
To support networked multimedia applications, it is important for a network to provide guaranteed qualityof- service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill certain constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is NP-complete and finding an exact solution can be difficult. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. However, the actual link metrics in a QoS-aware network is dynamic and may continuously change over time and since the path given by the routing algorithm is computed using the state information available to the router, which may or may not be up-to-date, it is possible that a feasible path returned by the algorithm may turn out to be no longer valid. This paper presents a GAbased QoS routing algorithm for solving the general kconstrained problem which has the capability to return multiple feasible paths in a single run. This makes the algorithm more robust in the case that the rate of change of state information in the network is higher than the rate of state information received by the router. Simulation results show that this algorithm consistently achieve higher feasibility ratio relative to existing well-known MCP routing algorithms when state information in the router lags behind the network. � 2010 Academy Publisher.
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Keywords
Genetic algorithm , Multi-constrained path , QoS routing , Robust routing , Computer simulation , Genetic algorithms , Routing algorithms , Exact solution , Heuristics and approximations , Multi constrained path , Multi-constrained path problems , Networked multimedia , NP Complete , QoS routing , QoS routing algorithms , Rate of change , Robust routing , Simulation result , State information , Approximation algorithms
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