Grid provides global computing infrastructure for users to avail the services supported by the network. The task scheduling decision is a major concern in heterogeneous grid computing environment. The scheduling being an NP-hard problem, meta-heuristic approaches are preferred option. In order to optimize the performance of workflow execution two conflicting objectives, namely makespan (execution time) and total cost, have been considered here. In this paper, reference point based multi-objective evolutionary algorithms, R-NSGA-II and R-e-MOEA, are used to solve the workflow grid scheduling problem. The algorithms provide the preferred set of solutions simultaneously, near the multiple regions of interest that are specified by the user. To improve the diversity of solutions we used the modified form of R-NSGA-II (represented as M-R-NSGA-II). From the simulation analysis it is observed that, compared to other algorithms, R-e-MOEA delivers better convergence, uniform spacing among solutions keeping the computation time limited.
Nature Inspired Meta-heuristics for Grid Scheduling: Single and Multi-objective Optimization Approaches
