Fault Tolerant and Optimal Task Clustering for Scientific Workflow in Cloud

Abstract

Scientific workflows are very complex, large-scale applications and require more computational power for data transmission and execution. In this article, the authors address the problem of scheduling scientific workflow on a number of virtual machines (VM) with the objective of reducing the total makespan of workflow and failure. This article implements checkpoints and replication strategies with the parallel task execution (PTE) algorithm to schedule scientific workflow for minimum time and cost. In order to reduce execution overhead and improve performance of the scientific application, the task uses clustering methods. Specifically, Horizontal Reclustering (HR) method were implemented to reduce failure and scheduling overhead. The authors have combined checkpoint, replication and PTE algorithms together and applied it to the HR method. Results show that the proposed strategies and method works efficiently in terms of reducing failure, makespan and execution cost compared to existing methods.