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. Dynamic and Fault-Tolerant Clustering for Scientific Workflows However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job.
In this Dynamic and Fault-Tolerant Clustering for Scientific Workflows
paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions. We also propose three fault-tolerant clustering strategies to improve runtime performance of workflow executions in faulty execution environments.
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In this work, we model transient failures in a distributed environment and evaluate their influence on task clustering. We proposed three dynamic clustering methods to improve the fault tolerance of task clustering and applied them to ﬁve widely used scientiﬁc workﬂows. Experimental results showed that the proposed methods significantly improve the makepan of the workflow compared to an existing task clustering method used in workflow management systems. The Dynamic Reclustering method, in particular, performed best among all methods since it could adjust the clustering size based on the Maximum Likelihood Estimation of task runtime, system overheads, and inter-arrival failure time.