NONSTATIONARY PROCESSES PERIOD ESTIMATION IN CLOUD SYSTEMS

Abstract

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 toestimate the period length for an adequate time. The testing was carried out on a system with an AMD FX 8120 CPU with a clock frequency of 3.1 GHz in one thread. The original signal was generated with amplitude 1. The waveform, the period value, the amplitude multiplier and the magnitude of the superimposed random noise were varied. According to the data from the largest transport network hub of Russia, Joint-Stock Company Center for Interaction of Computer Networks MSK-IX, the period of total transit traffic has been successfully determined, and also the periods of non-stationary processes for the cloud system model have been successfully determined. Practical Relevance. The developed method can be used as part of the services of cloud systems automatic scaling and provides more efficient management of the infrastructure resources of cloud computing systems.