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 suchas shrinkage of system resources, data corruption, and numerical error accumulation. The resource shrinkageleads to the system failure due to the error propagation. Thus the resource prediction is useful to theadministrator of the system so that an accidental outage can be avoided. It has been observed in past thatmost of the failures occur due to the exhaustion of free physical memory, so here free physical memory of aserver consolidation setup is observed. It is also found that most of the studies in this direction were using themeasurement-based approach with time series models for prediction. This paper reviews the effectiveness ofsuch models and it examines whether volatility is present in the data or not. It checks whether Gauss-Markovassumptions about homoscedasticity holds good for the ordinary least square estimators of such models ornot. This paper applies a combination of AutoRegressive Integrated Moving Average - AutoRegressiveConditional Heteroskedastic (ARIMA-ARCH) model to predict resource usage. Experimental resultsdemonstrate that the goodness of fit of the ARIMA-ARCH Model has improved when compared to the linearARIMA model.