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

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.

[1]  Oscar H. Ibarra,et al.  Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors , 1977, JACM.

[2]  Rajesh Raman,et al.  Policy driven heterogeneous resource co-allocation with Gangmatching , 2003, High Performance Distributed Computing, 2003. Proceedings. 12th IEEE International Symposium on.

[3]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[4]  Chuang Liu,et al.  Online resource matching for heterogeneous grid environments , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[5]  Rajesh Raman,et al.  Matchmaking: distributed resource management for high throughput computing , 1998, Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244).

[6]  Chuang Liu,et al.  A constraint language approach to matchmaking , 2004, 14th International Workshop Research Issues on Data Engineering: Web Services for e-Commerce and e-Government Applications, 2004. Proceedings..

[7]  Yong Zhao,et al.  On-line evolutionary resource matching for job scheduling in heterogeneous grid environments , 2006, 12th International Conference on Parallel and Distributed Systems - (ICPADS'06).

[8]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[9]  Brian W. Kernighan,et al.  AMPL: A Modeling Language for Mathematical Programming , 1993 .

[10]  Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007), 14-17 May 2007, Rio de Janeiro, Brazil , 2007, CCGRID.

[11]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..