Towards a Multi-Agent approach for Workflow Scheduling on Hybrid Clouds

Abstract Since scientific research and industrial applications are becoming ever-increasingly demanding in computing resources, Cloud Computing infrastructures are attracting a widespread interest thanks to their promising hallmarks such as scalability, high-performance, reliability and the pay-per-use strategy. The execution of these high-performant applications on such kind of computing environments in respect of optimizing many conflicting objectives brings us to a challenging issue commonly known as the Multi-objective workflows scheduling on large scale distributed systems. Bearing this in mind, we outline in the present paper our proposed approach called Genetically-modified Multi-objective Particle Swarm Optimization (GMPSO) for scheduling application workflows on hybrid Clouds in the context of high-performance computing in attempt to optimize Makespan and Cost. To achieve this, we have designed our proper encoding as well as a set of particular operators. Conducted simulations lead to significant results compared with a set of well-performed algorithms such NSGA-II, OMOPSO and SMPSO, especially, for the most-demanding workload of workflows.