Predictive scaling for cloud applications
Abstract:
A compute cloud hosts a distributed application and is configured to add or remove instances of the application at datacenters at disperse geographic regions. Operations of the application are captured in a telemetry stream. Each record in the telemetry stream indicates a time, client location, and performance measure for a corresponding client request. After pre-processing such as rounding the times, the telemetry records are passed to a frequent itemset mining algorithm that identifies frequent time-location pairs in the telemetry stream. The frequent time-location pairs are consolidated into encompassing frequent region time-range pairs. An aggregate performance measure is computed from the performance measures of the telemetry records that match a frequent region time-range pair. A recommended region and time for adding or removing instances of the application is computed based on the aggregate performance measure and the region time-range pair.
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