ε -Pareto Dominance Based Multi-objective Optimization to Workflow Grid Scheduling

Grid facilitates global computing infrastructure for user to consume the services over the network. To optimize the workflow grid execution, a robust multi-objective scheduling algorithm is needed. In this paper, we considered two conflicting objectives like execution time (makespan) and total cost. We propose a multi-objective scheduling algorithm, using epsilon -MOEA approach based on evolutionary computing paradigm. Simulation results show that the proposed algorithm generates multiple scheduling solutions near the Pareto optimal front with uniform spacing and better convergence in small computation time.