LAUSR

Scientific workflow scheduling involves the allocation of workflow tasks to particular computational resources. The generation of optimal solutions to reduce run-time, cost, and energy consumption, as well as ensuring proper load balancing, remains a major challenge. Therefore, this work presents a Multi-Swarm Particle Swarm Optimization (MS-PSO) algorithm to improve the scheduling of scientific workflows in cloud-fog environments. MS-PSO seeks to address the canonical PSO’s problem of premature convergence, which leads it to suboptimal solutions. In MS-PSO, particles are divided into several swarms, with each swarm having its own cognitive and social learning coefficients. This work also develops a weighted sum objective function for the workflow scheduling problem, based on four objectives: makespan, cost, energy and load balancing for cloud and fog tiers. The FogWorkflowSim Toolkit is used in the evaluation process, with the objectives serving as performance metrics. The MS-PSO approach is compared with the canonical PSO, Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. The following scientific workflows are used in the simulations: Montage, Cybershake, Epigenomics, LIGO and SIPHT. MS-PSO outperforms the canonical PSO on all scientific workflows and under all performance metrics. It competes fairly well against the other approaches and it is more stable and reliable. It only ranks second to PSO, in terms of execution time. In future, multiple species, incorporating population update mechanisms from several algorithmic frameworks (MS-PSO, DE, GA), will be used for scientific workflow scheduling. Hybdridization of the realized algorithm with dynamic approaches will also be investigated.