Particle Swarm Optimization-Based approaches for Cloud-Based Task and Workflow scheduling: A Systematic Literature Review

Cloud computing is a novel perspective that provides effective methods to distribute independent and dependent tasks to virtual resources. Task and workflow scheduling play a significant role in optimizing makespan, energy consumption, load balancing, and cost in the cloud environment. Various metaheuristics solutions are proposed to solve scheduling problems. Optimizing the scheduling problem is NP-hard. In this paper, we are presenting an in-depth review of the current landscape of Particle Swarm Optimization(PSO)-based methods to schedule tasks and workflows. We adopt a systematic literature review(SLR) procedure to select studies based on framed Research Questions and predefined inclusion/exclusion criteria from online electronic databases. 39 articles were selected out of 364 articles. Those articles that answer our framed research questions are selected for analysis. The study considers various advantages, drawbacks, properties, simulation and evaluation tools, function objectives of PSO and prepares classification based on PSO modified and PSO hybrid. Finally, a future research direction is presented.