PPTS-PSO: a new hybrid scheduling algorithm for scientific workflow in cloud environment

The use of complex scientific workflows in cloud computing environments, taking into account different interdependency criteria, is becoming a key objective for cloud service providers and for customers. This gives the task scheduling operation a higher priority in order to improve the quality of services. In this work, we introduce a novel hybrid PPTS-PSO algorithm based on two efficient algorithms with the goal of improving the scheduling phase of a set of interdependent tasks that make up scientific workflows in the cloud-computing platform with the best execution time and cost while staying within the deadline and budget constraints. An intelligent variant of the PSO algorithm named neighborhood PSO and the heuristic PPTS algorithm are used. The suggested method can assign tasks in scientific workflows to the most appropriate cloud virtual machine. Therefore, our strategy takes into account resource allocation too. The experimental results show that our solution overcomes different algorithms in the literature with minimum iterations.