An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments

Abstract

Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. 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 with little input information-have been proposed. Unfortunately, existing proposals ignore job priorities, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. We present 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. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that our proposal allows for a more agile job handling while reducing PSE completion time.