Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
QoS guided min-min heuristic for grid task scheduling
Journal of Computer Science and Technology - Grid computing
Scheduling of scientific workflows in the ASKALON grid environment
ACM SIGMOD Record
Scientific Programming - Scientific Workflows
Multi-objective planning for workflow execution on Grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Grid computing infrastructure emerged as a next generation of high performance computing by providing availability of vast heterogenous resources. In the dynamic envirnment of grid, a schedling decision is still challenging area and it should consider reliability of reources while generating schedule in addition to other objectives. In this paper, we used evolutionary approach to obtain multiple trade-off soltions which minimizes makespan and cost along with the maximization of reliability under the deadline and budget constraints. We apply NSGA-II and ε - MOEA algorithms in order to explore solutions in the Pareto optimal front. Simulation analysis shows that multiple solutions obtained with ε -MOEA approach gives better convergence, uniform diversity in small computation time.