Job Scheduling of Parametric Computational Mechanics Studies on Cloud Computing Infrastructures

CARLOS GARCÍA GARINO; CRISTIAN MATEOS; ELINA PACINI

Cetraro

Workshop; International Advanced Workshop in High Performance Computing, Grids and Clouds (HPC 2012); 2012

Dipartimento Elettronica, Informatica, Sistemistica - Università della Calabria

Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. In particular non linear Computational Mechanical Problems are addressed in this case [1] This usually results in many jobs with high computational requirements. Thus, distributed environments, particularly Clouds, can be employed to fulfill these demands. However, job scheduling is challenging as it is an NP-complete problem. Recently, Cloud schedulers based on bio-inspired techniques -which work well in approximating problems- have been reviewed [2]. Sadly, existing proposals ignore job priority, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. In this work a new Cloud scheduler based on Ant Colony Optimization, the most popular bio-inspired technique, which also exploits well known notions from operating systems theory is proposed. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that this new proposal allows for a more agile job handling while reducing PSE completion time.