Grid resource allocation based on particle swarm optimization

Resource allocation in grid environment is a complex undertaking due to the heterogeneity and dynamic nature aroused by wide area sharing. Most of the existing studies for this problem have been found to be performance deficiency or slow convergence. To address the heterogeneous and computationally intractable problem of resource allocation optimization in grid, this paper presents an allocation algorithm for parallel tasks based on particle swarm optimization. The heterogeneity of grid user is tackled by introducing a universal utility function. And that computational intractability is solved using iterative searching of particle swarm. Experimental results show that the proposed algorithm is convergent and performs better than genetic algorithm.