Workflow Scheduling in Cloud Computing Environment by Combining Particle Swarm Optimization and Grey Wolf Optimization

Abstract

Scheduling workflows is a vital challenge in cloud computing due to its NP-complete nature and if an efficient workflow task scheduling algorithm is not used then it affects the system’s overall performance. Therefore, there is a need for an efficient workflow task scheduling algorithm that can distribute dependent tasks to virtual machines efficiently. In this paper, a hybrid workflow task scheduling algorithm based on a combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO GWO) algorithms, is proposed. PSO GWO overcomes the disadvantages of both PSO and GWO algorithms by improving the exploitation (local search) of PSO algorithm and exploration (global search) of GWO algorithm. This leads to better balance between exploration and exploitation, consequently it minimizes the makespan with 5.52% compared to GWO and 3.68% compared to PSO. The degree of imbalance reduced upto 33.22% compared to GWO and 17.61% compared to PSO, improves the convergence rate as well depending on number tasks and iterations. CloudSim tool is used to evaluate the proposed algorithm. The simulation results confirmed that the proposed method performs better than both of the standard PSO and GWO in terms of makespan, degree of imbalance and convergence rate

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

“…Shaikh Muhammad Suhail [37] and others combined the PSO algorithm with the moth-flame optimization algorithm for application in power transmission systems. Makhija Divya and colleagues [38] overcame the drawbacks of both the local search of the PSO algorithm and the global search of the grey wolf optimization algorithm, applying this method to the workflow task scheduling problem. Tijani Muhammed Adekilekun and team [39] combined the PSO algorithm with the bat algorithm to effectively avoid falling into local optima, applying this method to the joint economic dispatch scheduling problem in power systems.…”