Optimal task scheduling model and its algorithm in grid computing

Task Scheduling is a very important and complex problem in grid computing. Grid computing differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as back ground workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of resource load balancing. In this paper, a novel optimal scheduling model and its algorithm are developed for task scheduling problems. A novel annealing-inspired genetic algorithm is applied to solve the scheduling model. It first generates a new group of individuals through genetic operation such as selection, crossover, mutation etc, and than simulated annealing independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. The effectiveness of algorithm is shown for a number of test problems and performance comparisons with genetic algorithm, simulated annealing are also discussed.