Scheduling of Dependent Tasks Application using Random Search Technique

Since beginning of Grid computing, scheduling of dependent tasks application has attracted attention of researchers due to NP-Complete nature of the problem. In Grid environment, scheduling is deciding about assignment of tasks to available resources. Scheduling in Grid is challenging when the tasks have dependencies and resources are heterogeneous. The main objective in scheduling of dependent tasks is minimizing make-span. Due to NP-complete nature of scheduling problem, exact solutions cannot generate schedule efficiently. Therefore, researchers apply heuristic or random search techniques to get optimal or near to optimal solution of such problems. In this paper, we show how Genetic Algorithm can be used to solve dependent task scheduling problem. We describe how initial population can be generated using random assignment and height based approaches. We also present design of crossover and mutation operators to enable scheduling of dependent tasks application without violating dependency constraints. For implementation of GA based scheduling, we explore and analyze SimGrid and GridSim simulation toolkits. From results, we found that SimGrid is suitable, as it has support of SimDag API for DAG applications. We found that GA based approach can generate schedule for dependent tasks application in reasonable time while trying to minimize make-span

Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids

To use services transparently in a distributed environment, the Utility Grids develop a cyber-infrastructure. The parameters of the Quality of Service such as the allocation-cost and makespan have to be dealt with in order to schedule workflow application tasks in the Utility Grids. Optimization of both target parameters above is a challenge in a distributed environment and may conflict one another. We, therefore, present a novel heuristic algorithm for scheduling a workflow application on Utility Grids. Our proposed algorithm optimizes the allocation-cost and makespan in a scalable and very low runtime. The results of the wide-spread simulation indicate that the proposed algorithm is scalable against an increase in the application size and task parallelism of the application. The proposed algorithm effectively outperforms the current algorithms in terms of the allocation-cost, makespan and runtime scalability