Abstract: In one embodiment, a method includes identifying a plurality of period combinations for a metric of interest. Each period combination comprises one or more time periods and each of the one or more time periods comprises one or more segments. The method further includes, for each period combination of the plurality of period combinations and each historical value of a plurality of historical values of the metric of interest, incrementally inserting the historical value into corresponding segments of the one or more time periods. Moreover, the method includes ranking the plurality of period combinations based on comparisons of the plurality of historical values to sets of corresponding predicted values. The method also includes selecting a highest-ranked period combination as best characterizing the metric of interest.

Abstract: In one embodiment, a method includes executing at least one application. The method further includes collecting, at periodic intervals as the least one application executes, an application load of each of the at least one application. In addition, the method includes generating, based at least in part on the collecting, an application-behavior baseline for each of the at least one application. The application-behavior baseline includes a plurality of projected application loads over a future period of time. The application-behavior baseline has a configurable time resolution. The method also includes automatically determining, for a subperiod of the future period, a quantity of compute resources required to manage the at least one application in satisfaction of at least one performance criterion. Moreover, the method includes causing the determined quantity of compute resources to be proactively provisioned to the at least one application in advance of a start of the subperiod.

Abstract: In one embodiment, a method includes receiving an identity of a metric of interest and a future time point. The method further includes retrieving a prediction configuration previously associated with the metric of interest. The prediction configuration comprising a period combination. The period combination comprises a plurality of time periods, each time period comprises one or more segments, and each segment of the one or more segments comprises adapted historical values of the metric of interest incrementally inserted therein. The method also includes, for each time period of the plurality of time periods, identifying, for the future time point, a corresponding segment of the one or more segments, accessing a set of adapted historical values from the corresponding segment, and computing an intermediate predicted value from the set of adapted historical values. Moreover, the method includes calculating a predicted value for the metric of interest based on the computing.

Abstract: In one embodiment, a method includes receiving an identity of a metric of interest and a future time point. The method further includes retrieving a prediction configuration previously associated with the metric of interest. The prediction configuration comprising a period combination. The period combination comprises a plurality of time periods, each time period comprises one or more segments, and each segment of the one or more segments comprises adapted historical values of the metric of interest incrementally inserted therein. The method also includes, for each time period of the plurality of time periods, identifying, for the future time point, a corresponding segment of the one or more segments, accessing a set of adapted historical values from the corresponding segment, and computing an intermediate predicted value from the set of adapted historical values. Moreover, the method includes calculating a predicted value for the metric of interest based on the computing.

Abstract: In one embodiment, a method includes identifying a plurality of period combinations for a metric of interest. Each period combination comprises one or more time periods and each of the one or more time periods comprises one or more segments. The method further includes, for each period combination of the plurality of period combinations and each historical value of a plurality of historical values of the metric of interest, incrementally inserting the historical value into corresponding segments of the one or more time periods. Moreover, the method includes ranking the plurality of period combinations based on comparisons of the plurality of historical values to sets of corresponding predicted values. The method also includes selecting a highest-ranked period combination as best characterizing the metric of interest.