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Subject of Research. The existing approaches to the automatic scaling of non-stationary operating cloud systems are analyzed. The drawbacks of the existing approaches to workload prediction are revealed due to the insufficient performance of the algorithms being used. The analysis of the properties of periodic non-stationary processes and automatic length estimation of their period are performed on the basis of measured data. The accuracy of the developed analytical models was confirmed in the course of numerous simulation experiments in the AnyLogic Professional modeling environment. Method. The basis of the developed method of automatic length estimation of the non-stationary processes period is the consistent approximation of the intermediate result to the desired value. The proposed method ranks the expected results in accordance with the probability of their compliance with the determined period of the non-stationary process. Main Results. The possibility is provided to

Nonstationary processes period estimation in cloud systems