Long-Term QoS-Aware Cloud Service Composition Using Multivariate Time Series Analysis

Abstract

We propose a cloud service composition framework that selects the optimal composition based on an end user's long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers’ historical QoS data and short-term advertisements represented using Time Series . The quality of the QoS prediction is improved by incorporating QoS attributes’ intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series’ inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.