ACO-based dynamic job scheduling of parametric computational mechanics studies on Cloud Computing infrastructures

Abstract

Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with different input data, which typically results in many CPU-intensive jobs and thus computing environments such as Clouds must be used. Job scheduling is however challenging due to its inherent NP-completeness. Therefore, some Cloud schedulers based on Swarm Intelligence (SI) techniques, which are good at approximating combinatorial problems, have arisen. We describe a Cloud scheduler based on Ant Colony Optimization (ACO), a popular SI technique, to allocate Virtual Machines to physical resources belonging to a Cloud. Simulated experiments performed with real PSE job data and alternative classical Cloud schedulers show that our scheduler allows a fair assignment of VMs, which are requested by different users, while maximizing the number of jobs executed every time a new user connects to the Cloud. Unlike previous experiments with our algorithm, in which batch execution scenarios for jobs were used, the contribution of this paper is to experiment with our proposal in dynamic scheduling scenarios. Results suggest that our scheduler provides a better balance to the number of executed jobs per unit time versus serviced users, i.e., the number of Cloud users that the scheduler is able to successfully serve.