RPTCN: Resource Prediction for High-dynamic Workloads in Clouds based on Deep Learning

Abstract

Resource management is challenging in clouds due to the dynamics and sharing characteristics. The crucial problem is how to allocate resources accurately and satisfy demands of workloads timely. The traditional solution is to use historical data to predict future resource usage. Although these resource prediction methods can predict the periodicity, they can not accurately predict mutation points due to the high dynamics and uncertainty of resource usage. To tackle this issue, in this paper we propose a resource usage prediction method - RPTCN, which is based on a deep learning method - temporal convolutional networks (TCNs) in cloud systems. We add a fully connected layer and attention mechanism to TCNs to improve the prediction accuracy. In order to explore the relationship between the usage of different resources in the temporal dimension, we use correlation analysis to screen performance indicators as multidimensional feature input for prediction. Finally, we evaluate the performance of this method on Alibaba trace v2018. Evaluations show that RPTCN improves the overall MAE and MSE by 6.50%~89.03% and 0.41%~68.82% respectively compared to baselines in dynamic and long-term prediction of resource usage. Moreover, the convergence and generalization of RPTCN are also better than the baselines.