CSSAP: Software Aging Prediction for Cloud Services Based on ARIMA-LSTM Hybrid Model

Abstract

Cloud services typically compose of multiple distributed software components that communicate with each other through web service interfaces in the cloud environments. During their long time running, the accumulation of cloud software internal errors or large consumption of computing resources will very likely lead to software aging problems. In order to solve this problem, software rejuvenation technology is proposed to prevent them from causing more serious failures by restarting the services running. In the research field of software aging and rejuvenation for cloud services, how to accurately predict the cloud resource consumption in the aging software system for determining suitable time to perform rejuvenation is a significant and indispensable issue. In this paper, a novel hybrid aging prediction model named CSSAP is proposed, which well integrates the Autoregressive Integrated Moving Average (ARIMA) model and Long Short Term Memory (LSTM) model for better fitting the linear pattern and mining the nonlinear relationship in the time series of computing resource usage data for cloud services. The experiments results show that through such hybrid and unified time series analysis, our CASSP prediction method has 4% to 71% improvements in MAE evaluation criteria and 6% to 66% improvements in RMSE evaluation criteria under different time series scenarios compared with single model used, that is, the more accurate and more comprehensive aging prediction results achieved by CSSAP is definitely conducive to perform more effective and more efficient software aging and rejuvenation for cloud services.