Development of cognitive fault tolerant model for scientific workflows by integrating overlapped migration and check-pointing approach

Abstract

High computation power and storage space are needed to execute the complex scientific workflows. Cloud computing resources are used effectively to execute such complex workflows. Tasks executing in the workflow is dependent in nature and hence failure of a task affects the overall performance of the execution. In order to execute the workflow without any interrupt, a proactive intelligent fault tolerant model is necessary. This paper proposes a model called cognitive fault tolerant (CFT) with three important phases for tolerating the task and VM failure proactively. In prediction phase, combined ensemble prediction method is used to predict the task failures and label tuning algorithms are used to generate the intermediate labels and to strengthen the prediction. The segregation phase isolates the task based on priority assignment. Last phase of CFT model is recovery. Fitness checking is used to find whether the predicted failure is due to task or VM. Post prediction checkpointing (PPC) method is used as recovery process for task failure. VM failure can be recovered using post or pre replication overlapped migration method. The validation of proposed CFT model can be done by comparing with other existing algorithms. Experimental analysis shows that proposed CFT model improvise the reliability of workflow execution in cloud environment.