A Statistical Approach to Predictive Detection

Service Providers Typically define quality of service problems using threshold tests, such as "Ae HTTP operations greater than 12 per second on server XYZ?" This paper explores the feasibility of prdeicting violations of threshold tests. Such a capability allows providers to take corrective actions in advance of service disruptions. Our approach estimates the probability of threshold violations for specific times in the future. We model the threshold metric (e.g., HTTP operations per second) at two levels: (1) nonstationary behavior (as is done in workload forecasting for capacity planning) and (2) stationary, time-serial dependencies. Using these models, we compute the probability of threshold violations. Our approach is assessed using simulation experiments and measurements of a production web server. For both assessments, the probilities of threshold violations produced by our approach lie well within two standard deviations of the measured fraction of threshold violations