SI-based scheduling of scientific experiments on Clouds

Scientists and engineers usually require huge amounts of computing power for performing their experiments. Precisely, Parameter Sweep Experiments (PSE) allow these kind of users to perform simulations by running the same scientific code with different input data, which results in many CPU-intensive jobs and thus computing environments such as Clouds must be used. We describe two Cloud schedulers based on two popular swarm intelligence (SI) techniques, namely ant colony optimization (ACO) and particle swarm optimization (PSO), to allocate virtual machines (VM) to physical Cloud resources. The main performance metrics to study are the number of serviced users by the Cloud -i.e., the number of Cloud users that the scheduler is able to successfully serve- and the total number of created VMs, in dynamic (non-batch) scheduling scenarios. Simulated experiments performed by using CloudSim and real PSE job data suggest that our schedulers, through a weighted metric, perform competitively with respect to the number of serviced users and achieve an effective assignment of VMs compared to a scheduler based on Genetic Algorithms.