Resource usage prediction based on ARIMA-ARCH model for virtualized server system

Abstract

Performance degradation is unavoidable in server systems and this is because of factors such as shrinkage of system resources, data corruption, and numerical error accumulation. The resource shrinkage leads to the system failure due to the error propagation. Thus the resource prediction is useful to the administrator of the system so that an accidental outage can be avoided. It has been observed in past that most of the failures occur due to the exhaustion of free physical memory, so here free physical memory of a server consolidation setup is observed. It is also found that most of the studies in this direction were using the measurement-based approach with time series models for prediction. This paper reviews the effectiveness of such models and it examines whether volatility is present in the data or not. It checks whether Gauss-Markov assumptions about homoscedasticity holds good for the ordinary least square estimators of such models or not. This paper applies a combination of AutoRegressive Integrated Moving Average - AutoRegressive Conditional Heteroskedastic (ARIMA-ARCH) model to predict resource usage. Experimental results demonstrate that the goodness of fit of the ARIMA-ARCH Model has improved when compared to the linear ARIMA model. Int. J. of GEOMATE.