A workload prediction approach using models stacking based on recurrent neural network and autoencoder

Abstract

Workload prediction in computing systems like Cloud and Grid is an essential prerequisite for successful load balancing and achieving service-level agreements. However, since workloads in different systems and architectures have varied characteristics, providing an accurate single prediction model can be very challenging. Therefore, in this paper we have designed and implemented a model of stacking prediction algorithms to predict workload time series in Cloud and Grid systems using Recurrent Neural Network and Autoencoder. We have also performed experiments with several datasets containing different workload types and conducted comparisons with each component algorithm as well as the fixed weighted optimal combination value. Experimental results show that our model achieves lower average NRMSE in 3 datasets than the fixed weighted optimal combination value, and outperforms the component algorithms with improvements in NRMSE from 7.43% to 12.45%.