document: Short term performance forecasting in enterprise systems We use data mining and machine learning techniques to predict upcoming periods of high utilization or poor performance in enterprise systems . The abundant data available and complexity of these systems defies human characterization or static models and makes the task suitable for data mining techniques . We formulate the problem as one of classification : given current and past information about the system 's behavior , can we forecast whether the system will meet its performance targets over the next hour ? Using real data gathered from several enterprise systems in Hewlett-Packard , we compare several approaches ranging from time series to Bayesian networks . Besides establishing the predictive power of these approaches our study analyzes three dimensions that are important for their application as a stand alone tool . First , it quantifies the gain in accuracy of multivariate prediction methods over simple statistical univariate methods . Second , it quantifies the variations in accuracy when using different classes of system and workload features . Third , it establishes that models induced using combined data from various systems generalize well and are applicable to new systems , enabling accurate predictions on systems with insufficient historical data . Together this analysis offers a promising outlook on the development of tools to automate assignment of resources to stabilize performance , ( e.g. , adding servers to a cluster ) and allow opportunistic job scheduling ( e.g. , backups or virus scans ) .
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