Abstract
Cloud Computing has emerged as a service model that enables on-demand network access to a large number of available virtualized resources and applications with a minimal management effort and a minor price. The spread of Cloud Computing technologies allowed dealing with complex applications such as Scientific Workflows, which consists of a set of intensive computational and data manipulation operations. Cloud Computing helps such Workflows to dynamically provision compute and storage resources necessary for the execution of its tasks thanks to the elasticity asset of these resources. However, the dynamic nature of the Cloud incurs new challenges, as some allocated resources may be overloaded or out of access during the execution of the Workflow. Moreover, for data intensive tasks, the allocation strategy should consider the data placement constraints since data transmission time can increase notably in this case which implicates the increase of the overall completion time and cost of the Workflow. Likewise, for intensive computational tasks, the allocation strategy should consider the type of the allocated virtual machines, more specifically its CPU, memory and network capacities. Yet, a critical challenge is how to efficiently schedule the Workflow tasks on Cloud resources to optimize its overall quality of service. In this paper, we propose a QoS-aware algorithm for Scientific Workflows scheduling that aims to improve the overall quality of service (QoS) by considering the metrics of execution time, data transmission time, cost, resources availability and data placement constraints. We extended the Parallel Cat Swarm Optimization (PCSO) algorithm to implement our proposed approach. We tested our algorithm within two sample Workflows of different scales and we compared the results to those given by the standard PSO, the CSO and the PCSO algorithms. The results show that our proposed algorithm improves the overall quality of service of the tested Workflows.