KSwSVR: A New Load Forecasting Method for Efficient Resources Provisioning in Cloud

Abstract

Cloud provider should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to the actual resources demand of applications. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multi-types of resources were used to verify its prediction accuracy, stability and adaptability, comparing with AR, BPNN and standard SVR. CPU allocation experiment indicated that KSwSVR can effectively reduce resources consumption while meeting Service Level Agreements requirement.