Neha Gandhi, Joseph L. Hellerstein, et al.
CMG 2001
Service providers typically define quality of service problems using threshold tests, such as `Are HTTP operations greater than 12 per second on server XYZ?' Herein, we estimate 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) non-stationary behavior (as is done in workload forecasting for capacity planning) and (2) stationary, time-serial dependencies. Our approach is assessed using simulation experiments and measurements of a production Web server. For both assessments, the probabilities of threshold violations produced by our approach lie well within two standard deviations of the measured fraction of threshold violations.
Neha Gandhi, Joseph L. Hellerstein, et al.
CMG 2001
Joseph L. Hellerstein
CMG 1992
Sam S. Lightstone, Maheswaran Surendra, et al.
ICDEW 2007
Marc Willebeek-LeMair, Perwez Shahabuddin
IEEE/ACM Transactions on Networking