Hybrid Meta-heuristic Approach for Workflow Scheduling in IaaS Cloud

Scheduling workflows in IaaS cloud infrastructure has become a research issue. Considering multiple objectives for optimizing in scheduling workflows makes it difficult to adapt the cloud computing features like resource heterogeneity and elasticity on pay-per-use business model. This paper highlights such problems and suggests a workflow scheduling model to minimize the makespan and execution cost as a multi-objective optimization problem in cloud computing. We presented non-dominated sort-based hybrid wind-driven optimization (WDO) algorithm to address the desired goal on infrastructure as a service platform. It is combination of WDO and genetic algorithm that produces a Pareto optimal solution set with optimized makespan, cost and reliability. The user is offered to select the best solution out of produced optimal set. The evaluation of the proposed method is ascertained using three metrics: inverted generational distance, hypervolume and spacing. The results obtained by conducting experiments using four different scientific workflows validate that the solutions generated with the proposed hybrid algorithm are improved in terms of diversity and accuracy over different multi-objective solutions presented in the literature.

Automated summary

Workflow scheduling in IaaS cloud infrastructure is a research issue, and this paper proposes a multi-objective optimization model to minimize makespan and execution cost. By using a non-dominated sort-based hybrid wind-driven optimization algorithm, a Pareto optimal solution set is generated, improving the diversity and accuracy of solutions.