Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on Heterogeneous Master-Slave Environments
Problem statement: To examine the strategies for scheduling of independent file-sharing tasks in a heterogeneous environment and the concept of load balancing. Approach: We propose hypergraph partitioning based strategy for the scheduling of non-critical jobs. This is done by scheduling the tasks that share tasks among them to the same processor. The tasks thus scheduled are employed to a load balancing scheme for balancing the load on the processors by considering the average load on all processors. Results: This strategy reduces the input output overheads among the tasks thus reducing the end-point contention. Conclusion: Thus the batch execution time on the processors is reduced.