Performability Evaluation and Optimization of Workflow Applications in Cloud Environments

Given the characteristics of dynamic provisioning and illusion of unlimited resources, clouds are becoming a popular alternative for running scientific workflows. In a cloud system for processing workflow applications, the system’s performance is heavily influenced by two factors: the scheduling strategy and failure of components. Failures in a cloud system can simultaneously affect several users and depreciate the number of available computing resources. A bad scheduling strategy can increase the expected makespan and the idle time of physical machines. In this paper, we propose an optimization method for the scheduling of scientific workflows on cloud systems. The method comprises the use of a meta-heuristic algorithm coupled to a performability model that provides the fitnesses of explored solutions. For being able to represent the combined effect of scheduling and component failures, we adopted discrete event simulation for the performability model. Experimental results show the effectiveness of the hybrid simulation-optimization approach for optimizing the number of allocated virtual machines and the scheduling of tasks regarding performability.