Task Scheduling Using Two-Phase Variable Neighborhood Search Algorithm on Heterogeneous Computing and Grid Environments

Grid computing solves high-performance and high-throughput computing problems through sharing nodes ranging from personal computers to supercomputers distributed around the world. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. In this paper, an investigation on implementing Two-Phase Variable Neighborhood Search (TPVNS) algorithm for scheduling independent jobs on computational grid is carried out. The proposed algorithm consists of two modules with General Variable Neighborhood Search and Basic Variable Neighborhood Search algorithms in order to find a good mapping of grid jobs with grid nodes. The performance of the proposed algorithm has been evaluated with deterministic heuristic and evolutionary algorithms. Simulation results show that TPVNS algorithm generally performs better than the existing methods.