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Big-data Transportation in Orchestrated Bioinformatics Workflows: An Analysis and Hypothesis

Rapid growth of biological-data size brings the field of bioinformatics into the era of big-data and challenges the process of coordinating biological data sources and tool services. One of the challenges is how to transfer big-data among the components of a workflow in a faster and efficient manner. In this paper, we therefore (i) show the state-of-the-art approaches and analyze big-data handling in orchestrated bioinformatics workflows, (ii) provide a hypothesis based on the analysis and show that the hypothesis performs better than the state-of-the-art approaches. Our analysis shows that none of the existing approaches can retain the execution time of a workflow stable with respect to the size of the data involved. The main reason is that all the approaches attempt to move data which is varying in size, but not the tool (or the computation associated with it) which is fixed in size. Considering this as a hypothesis, we have moved computation instead of data in a workflow and shown that the workflow performs much better than other approaches in terms of execution time.