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Scientific workflows have been predominantly used for complex and large scaledata analysis and scientific computation/automation and the need for robustworkflow scheduling techniques has grown considerably. But, most of theexisting workflow scheduling algorithms do not provide the required reliabilityand robustness. In this paper, a new fault tolerant workflow schedulingalgorithm that learns replication heuristics in an unsupervised manner has beenproposed. Furthermore, the use of light weight synchronized checkpointingenables efficient resubmission of failed tasks and ensures workflow completioneven in precarious environments. The proposed technique improves upon metricslike Resource Wastage and Resource Usage in comparison to the Replicate-Allalgorithm, while maintaining an acceptable increase in Makespan as compared tothe vanilla Heterogeneous Earliest Finish Time (HEFT).

An Efficient Fault Tolerant Workflow Scheduling Approach using Replication Heuristics and Checkpointing in the Cloud