Abstract
High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed resources. However, earlier clustering methods ignore the effect of failures on the system, despite their significant impact on large-scale distributed resources, such as Clouds. In this paper, we present a new fault-tolerant task clustering method called FT-HCC that is designed by including the workflow execution time (makespan) and execution cost constraints, which are used to increase workflow performance. The proposed method is implemented and evaluated in a simulation-based approach, using a real-time workflow execution to analyze performance improvement. The results consolidate that the proposed strategies and techniques work efficiently in terms of fault tolerance and improve both workflow makespan and execution cost when compared to existing approaches.
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