Computation migration: a new approach to execute big-data bioinformatics workflows

Abstract

Bioinformatics workflows frequently access various distributed biological data sources and computational analysis tools for data analysis and knowledge discovery. They move large volumes of data from biological data sources to computational analysis tools and follow the traditional data migration approach for workflow execution. However, in the advent of big-data in bioinformatics, moving large volumes of data to computation during workflow execution is no longer feasible. Considering the fact that the size of biological data is continuously growing and is much larger than the computational analysis tool size, moving computation to data in a workflow is a better solution to handle the growing data. In this paper, we therefore propose a computation migration approach to execute bioinformatics workflows. We move computational analysis tools to data sources during workflow execution and demonstrate with workflow patterns that moving computation instead of data yields high performance gains in terms of data-flow and execution time.