A dynamic energy-aware model for scheduling computationally intensive bioinformatics applications

High Performance Computing (HPC) resources are housed in large datacenters, which consume huge amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In this paper, we provide a dynamic model for energy aware scheduling (EAS) in a HPC environment; we use a widely used bioinformatics tool named BLAT (BLAST-like alignment tool) running in a HPC environment as our case study. Our proposed EAS model incorporates 2-Phases: an Offline phase and an Online one. In the Offline Phase, we use sequences gathered from researchers and parallelize the runs to understand the run (speedup) profile of the program. The EAS Engine then utilizes such information to generate the initial schedule. In the Online Phase a feedback mechanism is incorporated between the EAS Engine and the master scheduling process. As scheduled tasks are completed, their actual execution time (AET) is used to adjust the resources required for scheduling remaining tasks using the least number of nodes while meeting a given deadline. The conducted experiments show that the proposed approach succeeded in meeting preset deadlines while minimizing the number of nodes; thus reducing overall energy utilized.