Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments
نویسندگان
چکیده
Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed dataintensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulate efficient schedules. The proposed variable neighborhood particle particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experiment results illustrate the algorithm performance and its feasibility and effectiveness for scheduling work-flow applications.
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