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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org