Autonomic fault tolerant scheduling approach for scientific workflows in Cloud computing

Abstract

Autonomic fault tolerant scheduling is now a mandatory approach for the execution of performance-motivated Cloud applications such as scientific workflows. Since concurrent engineering is strongly associated with scientific workflows, an efficient scheduling for scientific workflows can have major impact on the performance of concurrent systems and engineering applications in Cloud computing. To facilitate the execution of concurrent tasks in scientific workflows, Cloud providers entail efficient scheduling heuristics and fault tolerant approaches. The work presented in this article formulates an effort focusing on this research problem to design an autonomic fault tolerant scheduling approach for scientific workflow applications. First, hybrid heuristic has been proposed to schedule scientific workflows effectively. Second, fault tolerant technique has been implemented using virtual machine migration approach that migrates the virtual machine automatically in case of task failure occurrences due to the overutilization of resources. Furthermore, the proposed approach has been validated through performance evaluation parameters using CloudSim and WorkflowSim toolkits. The simulation results demonstrate the effectiveness of the proposed approach to improve the performance of scientific workflows by appreciably reducing total mean execution time, standard deviation time, and makespan.