Optimizing bioinformatics workflows for data analysis using cloud management techniques

With the rapid development in recent years of high-throughput technologies in the life sciences, huge amounts of data are being generated and stored in databases. Despite signifi-cant advances in computing capacity and performance, an analysis of these large-scale data in a search for biomed-ically relevant patterns remains a challenging task. Sci-entific workflow applications support data-mining in more complex scenarios that include many data sources and com-putational tools, as commonly found in bioinformatics. A scientific workflow application is a holistic unit that defines, executes, and manages scientific applications using different software tools. Existing workflow applications are process-or data- rather than resource-oriented. Thus, they lack effi-cient computational resource management capabilities, such as those provided by Cloud computing environments. In-sufficient computational resources disrupt the execution of workflow applications, wasting time and money. To address this issue, advanced resource monitoring and management strategies are required to determine the resource consump-tion behaviours of workflow applications for a dynamical allocation and deallocation of resources. In this paper, we present a novel Cloud resource monitoring technique and a knowledge management strategy to manage computational resources for workflow applications in order to guarantee their performance goals and their successful completion. We present the design description of these techniques, demon-strate how they can be applied to scientific workflow ap-plications, and present first evaluation results as a proof of concept