An ensemble multiscale wavelet-GARCH hybrid SVR algorithm for mobile cloud computing workload prediction

Dynamic resource allocation and auto scalability are important aspects in mobile cloud computing environment. Predicting the cloud workload is a crucial task for dynamic resource allocation and auto scaling. Accuracy of workload prediction algorithm has significant impact on cloud quality of service and total cost of provided service. Since, existing prediction algorithms have competition for better accuracy and faster run time, in this paper we proposed a hybrid prediction algorithm to address both of these concerns. First we apply three level wavelet transform to decompose the workload time series into different resolution of time–frequency scales. An approximate and three details components. Second, we use support vector regression (SVR) for prediction of approximate and two low frequency detail components. The SVR parameters are tuned by a novel chaotic particle swarm optimization algorithm. Since the last detail component of time series has high frequency and is more likely to noise, we used generalized autoregressive conditional heteroskedasticity (GARCH) model to predict it. Finally, an ensemble method is applied to recompose these predicted samples from four multi scale predictions to achieve workload prediction for the next time step. The proposed method named wavelet decomposed 3 PSO optimized SVR plus GARCH (W3PSG). We evaluate the proposed W3PSG method with three different real cloud workload traces. Based on the results, the proposed method has relatively better prediction accuracy in comparison with competitive methods. According to mean absolute percentage error metric, in best case W3PSG method achieves 29.93%, 29.91%, and 24.53% of improvement in accuracy over three rival methods: GARCH, artificial neural network, and SVR respectively.