Dynamic and Fault-Tolerant Clustering for Scientific Workflows

 Dynamic and Fault-Tolerant Clustering for Scientific Workflows

 Task clustering has proven to be an effective method to reduce execution overhead and to improve the computational granularity of scientific workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job. In this paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions.