Scheduling Budget Constrained Cloud Workflows with Particle Swarm Optimization

Nowadays, many scientific workflows are deployed in the cloud, and how to schedule the tasks according to the users´ QoS (Quality of Service) requirements, such as the make span and the monetary cost, has been proposed as the main challenge. In this paper, we aim to solve the problem of finding the scheduling solutions to minimize the workflow make span under the constraint of the user´s budget. Considering it is very time consuming to find the optimal solution, instead, we adopt an evolutionary computation technique called Particle Swarm Optimization (PSO) to derive the approximate answers. The proposed method is evaluated with real scientific workflows of different structures and sizes. Comparing with the latest method, the experiment results show that our proposed approach can achieve better performance by increasing the number of particles and iterations.