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As computer systems become increasingly complex, system anomalies have become major concerns in system management. In this paper, the authors present a comprehensive measurement study to quantify the predictability of different system anomalies. Online anomaly prediction allows the system to foresee impending anomalies so as to take proper actions to mitigate anomaly impact. The anomaly prediction approach combines feature value prediction with statistical classification methods. They conduct extensive measurement study to investigate anomalous behavior of three systems in the real world: PlanetLab, SMART hard drive data, and IBM System S. They observe that real world system anomalies do exhibit predictability, which can be predicted with high accuracy and significant lead time.
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