Three-Way Ensemble Prediction for Workload in the Data Center

Abstract

Accurate prediction of the cloud data center workload used to improve resource utilization and reduce energy consumption, is a vital methodology and technology in cloud computing. However, the workload presents a quasi-volatile, is challenging to obtain accurate results in cloud resource management. In this paper, the three-way ensemble prediction for workload in the data center is first proposed to improve the accuracy of the prediction. Moreover, we first defined the workload as the stable period, the volatility period, and the jitter period and adopted a simulated annealing algorithm to learn the optimal threshold to divide the workload. Then, according to the basic idea of the three-way decision model (i.e., TAO model), we assigned various prediction models based on workload characteristics and a priori error prediction to improve the prediction accuracy further. Finally, all the experimental results carried on the CPU load monitoring logs from Google cluster trace and compared with ARIMA, NN, and DMASVR-3WD, TWD-RCPM improve the accuracy of workload prediction by 69.0%, 68.6% and 72.6%, respectively.INDEX TERMS three-way decision, three-way division, cost assessment, load prediction, cloud energy consumption I. INTRODUCTION

PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction

“…Various error measurements were utilized to evaluate the precision of predictions. We employed the three statistical metrics [54] mean absolute error (MAE), which is less biased for large mistakes and outliers but may not effectively capture huge errors, standard deviation (SD), and variance (V). In the trials, root mean square error (RMSE) and mean absolute percent error (MAPE) were also used to gauge the prediction efficacy of the predictive performance of our suggested ensemble model.…”