Smart PSO-based secured scheduling approaches for scientific workflows in cloud computing

Owing to its manifold advantages in adapting cloud computing for real-world scientific workflow applications, we intend to use cloud computing for executing the scientific workflows. In the present work, we aim for scheduling the workflow in the scalable resources in the cloud. In general, security is a vital challenge in cloud and so we include security constraints into our optimization model. The main objective of our work is to find an optimized schedule having minimum makespan and cost and by satisfying security demand constraint. The users can submit their security demand to the cloud provider during negotiation. The workflow is initially scheduled with list-based heuristics, which is then optimized by Particle Swarm Optimization (PSO). Thus we device a Smart Particle Swarm Optimization (SPSO)-based secured scheduling to find the optimized schedule with minimum makespan and cost. The proposed method is capable of assigning the task in the scientific workflows to the best suitable virtual machine in the cloud. Hence, the resource allocation is addressed as well by our method. Besides, a variant of PSO algorithm called Variable Neighbourhood PSO is also experimented to overcome the local optima problem. Our experimental results show that the scheduled workflows with assured security are yielding better makespan than existing methods with minimum iterations, which is well suited for cloud environment.