Genetically-modified Multi-objective Particle Swarm Optimization approach for high-performance computing workflow scheduling

Nowadays, scientific research, industry, and many other fields are greedy regarding computing resources. Therefore, Cloud Computing infrastructures are now attracting pervasive interest thanks to their excellent 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. Having 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 an attempt to optimize Makespan and Cost. The GMPSO consists of incorporating genetic operations into the Multi-objective Particle Swarm Optimization to enhance the resulting solutions. To achieve this, we have designed a novel solution encoding that represents the task ordering, the task mapping and the resource provisioning processes of the workflow scheduling problem in hybrid Clouds. In addition, a set of particular adaptive evolutionary operators have been designed. 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.