NONSTATIONARY PROCESSES PERIOD ESTIMATION IN CLOUD SYSTEMS

Zhmylev S.A. Nonstationary processes period estimation in cloud systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 3, pp. 475–481 (in Russian). doi: 10.17586/2226-1494-2019-19-3-475-481

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.

1. Bogatyrev V.A., Parshutina S.A., Poptcova N.A., Bogatyrev A.V. Efficiency of redundant service with destruction of expired and irrelevant request copies in real-time clusters. Communications in Computer and Information Science, 2016, vol. 678, pp. 337–348. doi: 10.1007/978-3-319-51917-3_30
2. Patel R.P. Cloud computing and virtualization technology in radiology. Clinical Radiology, 2012, vol. 67, no. 11, pp. 1095– 1100. doi: 10.1016/j.crad.2012.03.010
3. Moreno-Vozmediano R., Montero R.S, Llorente I.M. Iaas cloud architecture: From virtualized datacenters to federated cloud infrastructures. Computer, 2012, vol. 45, no. 12, pp. 65–72. doi: 10.1109/mc.2012.76
4. Chang B.R., Tsai H.F., Chen C.M. Empirical analysis of server consolidation and desktop virtualization in cloud computing. Mathematical Problems in Engineering, 2013, vol. 2013, 11 p. doi: 10.1155/2013/947234
5. Martynchuk I.G., Zhmylev S.A. Architecture and organization of autoscaling services in cloud systems. Al’manakh Nauchnykh Rabot Molodykh Uchenykh Universiteta ITMO. St. Petersburg, ITMO University Publ., 2017, vol. 5, pp. 200–203. (in Russian)
6. Zhmylev S.A., Martynchuk I.G., Kireev V.Yu., Aliev T.I. Estimation of periods of nonstationaty processes in cloud systems. Journal of Instrument Engineering, 2018, vol. 61, no. 8, pp. 645–651 (in Russian). doi: 10.17586/0021-3454-2018-61-8- 645-651
7. Cattani C., Kudreyko A. Application of periodized harmonic wavelets towards solution of eigenvalue problems for integral equations. Mathematical Problems in Engineering, 2010, 8 p. doi: 10.1155/2010/570136
8. Chen W.W., Liu S., Wang Q.J. Fast Fourier transform based numerical methods for elasto-plastic contacts of nominally flat surfaces. Journal of Applied Mechanics, 2008, vol. 75, no. 1. doi: 10.1115/1.2755158
9. Levina A.B., Taranov S.V. Investigation of influence of encoding function complexity on distribution of error masking probability. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 2, pp. 331–337 (in Russian). doi: 10.17586/2226-1494-2016-16-2-331-337
10. Bogatyrev V.A. Fault tolerance of clusters configurations with direct connection of storage devices. Automatic Control and Computer Sciences, 2011, vol. 45, no. 6, pp. 330–337. doi: 10.3103/S0146411611060046
11. Bogatyrev V.A., Bogatyrev S.V., Golubev I.Yu. Optimization and the process of task distribution between computer system clusters. Automatic Control and Computer Sciences, 2012, vol. 46, no. 3, pp. 103–111. doi: 10.3103/S0146411612030029
12. Pismak A.E., Kharitonova A.E., Tsopa E.A., Klimenkov S.V. Evaluation of semantic similarity for sentences in natural language by mathematical statistics methods. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 2, pp. 324–330. doi: 10.17586/2226- 1494-2016-16-2-324-330
13. Shahzad A., Lee Y.S., Lee M. et al. Real-time cloud-based health tracking and monitoring system in designed boundary for cardiology patients. Journal of Sensors, 2018, vol. 2018, 15 p. doi: 10.1155/2018/3202787
14. MSK-IX Traffic. Available at: https://www.msk-ix.ru/traffic/ (accessed: 09.04.2019).
15. Bezzateev S.V., Elina T.N., Myl’nikov V.A. Modeling of selection processes of cloud systems parameters providing their stability in accordance with reliability and safety. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 4, pp. 654–662 (in Russian). doi: 10.17586/2226-1494-2018-18-4-654-662
 

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License