Security-Driven Heuristics and A Fast Genetic Algorithm for Trusted Grid Job Scheduling
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
On-line Evolutionary Resource Matching for Job Scheduling in Heterogeneous Grid Environments
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 2
Cooperative grid jobs scheduling with multi-objective genetic algorithm
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
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The computing GRID infrastructure could benefit of techniques that can improve the overall throughput of the system. It is possible that job submission will include different ontology in resource requests due to the generality of the GRID infrastructure. Such flexible resource request could offer the opportunity to optimize several parameter, from network load to job costs in relation to due time, more generally the quality of services. We present the result of the simulation of GRID jobs allocation. The search strategy for this input case do not converge to the optimal case inside the limited number of trial performed, in contrast with previous work on up to 24 jobs. The benefits of the usage of the Genetic Algorithms to improve the quality of the scheduling is discussed. The simulation has been obtained using a sw environment GGAS suitable to study the scheduling of jobs in a distributed group of parallel machines. The result of this paper suggest the usage of local search strategy to improve the convergence.