document: Critical event prediction for proactive management in large-scale computer clusters As the complexity of distributed computing systems increases , systems management tasks require significantly higher levels of automation ; examples include diagnosis and prediction based on real-time streams of computer events , setting alarms , and performing continuous monitoring . The core of autonomic computing , a recently proposed initiative towards next-generation IT-systems capable of ` self-healing ' , is the ability to analyze data in real-time and to predict potential problems . The goal is to avoid catastrophic failures through prompt execution of remedial actions . This paper describes an attempt to build a proactive prediction and control system for large clusters . We collected event logs containing various system reliability , availability and serviceability ( RAS ) events , and system activity reports ( SARs ) from a 350-node cluster system for a period of one year . The ` raw ' system health measurements contain a great deal of redundant event data , which is either repetitive in nature or misaligned with respect to time . We applied a filtering technique and modeled the data into a set of primary and derived variables . These variables used probabilistic networks for establishing event correlations through prediction algorithms . We also evaluated the role of time-series methods , rule-based classification algorithms and Bayesian network models in event prediction . Based on historical data , our results suggest that it is feasible to predict system performance parameters ( SARs ) with a high degree of accuracy using time-series models . Rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70 % accuracy .
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