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The information model of cloud app was done. It is a formal description of cloud app infrastructure and possible transitions between them, and cloud app current working state classification criterion. Cloud app current state classification criterion on the basis of Page-Hinckley method and calendar of events related to the cloud app working state considers the current state to one of three classes in order to improve the accuracy of prediction of cloud app workload. Proposed criterion was compared with standard offline criterion that analyzes information about the entire time series of cloud app through a considerable time after the events that lead to the load peak, and therefore can’t be used when grading in real time. It is shown that the classification of cloud app state is consistent in 92 % of cases. The resulting information model of cloud app scaling with variable load peaks can be used as a component of information technology for cloud app scaling with variable load peaks.

INFORMATION MODEL OF CLOUD APP SCALING WITH VARIABLE LOAD PEAKS