Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications' QoS
As companies shift from desktop applications to cloud based software as a service (SaaS) applications deployed on public clouds, the competition for end users by cloud providers offering similar services grows. In order to survive in such a competitive market, cloud based companies must achieve good quality of service (QoS) for their users, or risk losing their customers to competitors. However, meeting the QoS with a cost effective amount of resources is challenging because workloads experience variation overtime. This problem can be solved with proactive dynamic provisioning of resources, which can estimate the future need of applications in terms of resources and allocate them in advance, releasing them once they are not required. In this paper, we present the realization of a cloud workload prediction module for SaaS providers based on the autoregressive integrated moving average (ARIMA) model. We introduce the prediction based on the ARIMA model and evaluate its accuracy of future workload prediction using real traces of requests to Web servers. We also evaluate the impact of the achieved accuracy in terms of efficiency in resource utilization and QoS. Simulation results show that our model is able to achieve an average accuracy of up to 91 percent, which leads to efficiency in resource utilization with minimal impact on the QoS.
IEEE Transactions on Cloud Computing