Standard Article Concurrent Engineering: Research and Applications 2015, Vol. 23(1) 27–39 Ó The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1063293X14567783 cer.sagepub.com Autonomic fault tolerant scheduling approach for scientific workflows in Cloud computing Anju Bala and Inderveer Chana 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 sci- entific 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 vir- tual machineautomatically 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. Keywords Fault tolerance, virtual machine migration, Cloud computing, workflow scheduling, scientific workflows Introduction Cloud computing has transformed the Information and Communication Technology industry by enabling on- demand services and provisioning of computing resources based on utility. Having large amount of resources such as RAM, CPU, Bw, and disk storage, Clouds prospect for start-up companies to host their big data applications in the Cloud by reducing the overhead of procuring infrastructure resources. Cloud providers offer various services to the users such as Software as a Service (SaaS), Hardware as a Service (HaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Therefore, Cloud requires reliability, availability, robustness, and high- performance services. Additionally, Cloud computing has also emerged as a computational standard that enables scientists for constructing more intricate scien- tific workflow applications to control large data sets or high-performance applications based on Cloud resources (Armbrust et al., 2009). Concurrent engineering is a workflow (Addo- Tenkorang, 2011) being used for parallel execution of independent tasks in scientific workflows which can comprise few tasks to millions of tasks (Bouikni et al., 2008). For large workflows, various tasks need to be distributed and parallelized among multiple resources in Cloud to complete the task in a reasonable time (Kara et al., 2001; Ramakrishnan et al., 2013). Cloud infrastructures have also been evaluated as an execu- tion platform for scientific workflows and support all the techniques which had already been implemented in Grids and Clusters. In Cloud computing, workflows can be designed by Directed Acyclic Graph (DAG), Department of Computer Science & Engineering, Thapar University, Patiala, India Corresponding author: Anju Bala, Department of Computer Science & Engineering, Thapar University, Patiala 147004, India. Email: anjubala@thapar.edu by guest on December 5, 2016 cer.sagepub.com Downloaded from