Software Rejuvenation in Computer Systems: An Automatic Forecasting Approach Based on Time Series

Abstract

Distributed computing is bringing many advantages in cost, flexibility and availability. However, it increases the demand for performance and reliability. Resources such as CPU, memory, storage and network bandwidth, are very susceptible of presenting software aging issues. Therefore, proactive actions, also known as software rejuvenation must be performed to avoid these issues. The identification of the best moment to perform software rejuvenation is not a simple task, mostly because it may affect the system's availability and reliability. To overcome this problem, we propose an automatic forecasting strategy to support the system administrators to choose the best moment to perform software rejuvenation. Our strategy uses six time series techniques: Drift, Simple Exponential Smoothing, Holt, Holt-Winters, Linear Regression, and ARIMA. In our proposal, the most suitable one is chosen automatically as the best fit for a particular scenario. Three case studies were performed to evaluate the efficiency of our automatic strategy. Our proposal aims to increase the system's availability while decreasing the QoS violation probability. In one of our experiments, we can observe a reduction of 92.3% in the system's downtime. This research supports decision making activities and opens possibilities to foster the usage of forecasting strategies when dealing with software aging phenomenon.