A Hybrid Linear Programming and Evolutionary Algorithm based Approach for On-line Resource Matching in Grid Environments

Abstract

We describe a hybrid linear programming (LP) and evolutionary algorithm (EA) based resource matcher suitable for heterogeneous grid environments. The hybrid matcher adopts the iterative approach of the EA methods to perform a goal oriented search over the solution space and, within each iteration, uses the LP method to solve a partial resource matching problem. By judiciously controlling the partial problem size and its complexity, the hybrid matcher balances the accuracy of the solution and the execution time. We describe a grid management architecture that incorporates the hybrid resource matcher. Performance results indicate that the execution time of the hybrid matcher, under a variety of conditions, is at least as good and often significantly better than the execution time of LP and EA based matchers. The hybrid matcher is found to scale well with the complexity of the problem and to maintain sensitivity to the response time constraints of on-line environments.