Application of discrete wavelet transforms for forecasting the level of load on the web server of general purpose computer networks.
Keywords:
wavelet transform, web server of general purpose computer networks, load forecasting, Daubechies wavelets.
Abstract
This article focuses on the application of discrete wavelet transforms to develop an effective method of predicting the load, which is determined by the amount of web traffic, on the web server of general-purpose computer networks. The analysis of existing forecasting methods in computer networks is carried out and the criterion for a way of increase of accuracy and speed of forecasting of loading is offered. An experiment was performed and the most effective method of forecasting the load on the web server was determined.
References
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Hu Z., Tereykovskiy I., Tereykovska L., Tsiutsiura M., Radchenko K. Applying Wavelet Transforms for Web Server Load Forecasting // Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol. 938, pp. 13–22 (2019). Springer, Cham. ISSN 2194-5365 (Online). https://doi.org/10.1007/978-3-030-16621-2_2
Dychka, I., Tereikovskyi, I., Tereikovska, L., Pogorelov, V., Mussiraliyeva, S.(2018), “Deobfuscation of computer virus malware code with value state dependence graph”, Advances in Intelligent Systems and Computing, pp 370-379, 2018. DOI: 10.1007/978-3-319-91008-6.
Qi-Song Chen ; Xin Zhang ; Shi-Huan Xiong ; Xiao-Wei Chen Short-term power load forecasting with least squares support vector machines and wavelet transform 2008 International Conference on Machine Learning and Cybernetics Year: 2008 , Volume: 3. Pages: 1425 – 1429.
Sitnikov V.S., Bilenko A.A.. Classification of wavelet functions. Труды Одесского политехнического университета, 2008, вып . 1(29) с.168-171.
Starck, J.-L. Image and Data Analysis: the Multiscale Approach / J.-L. Starck, F. Murtagh, A. Bijaoui. — Cambridge University Press, 1998. — 307 с.
Steinbuch, M. Wavelet Theory and Applications A literature study / M. Steinbuch, M.J.G. van de Molengraft — Eindhoven University of Technology, 2005. - 39 с
Shuping Yao; Changzhen Hu; Mingqian Sun Prediction of Web Traffic Based on Wavelet and Neural Network 2006 6th World Congress on Intelligent Control and Automation Year: 2006 , Volume: 1, Page s: 4026 – 4028.
Shuping Yao; Changzhen Hu; Wu Peng Server Load Prediction Based on Wavelet Packet and Support Vector Regression 2006 International Conference on Computational Intelligence and Security Year: 2006, Volume: 2 Pages: 1016 – 1019.
Tereykovska, L., Tereykovskiy, I., Aytkhozhaeva, E., Tynymbayev, S., Imanbayev, A. (2017), “Encoding of neural network model exit signal, that is devoted for distinction of graphical images in biometric authenticate systems”, News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences, Volume 6, Number 426, pp. 217 – 224, 2017.
Zhengbing Hu, Igor A. Tereykovskiy, Lyudmila O. Tereykovska, Volodymyr V. Pogorelov, "Determination of Structural Parameters of Multilayer Perceptron Designed to Estimate Parameters of Technical Systems", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.10, pp.57-62, 2017. DOI: 10.5815/ijisa.2017.10.07.
Zijiang Yang Research on Server Load Prediction Based on Wavelet Packet Theory 2007 First IEEE International Symposium on Information Technologies and Applications in Education Year: 2007 Pages: 610 – 613.
Hu Z., Tereykovskiy I., Tereykovska L., Tsiutsiura M., Radchenko K. Applying Wavelet Transforms for Web Server Load Forecasting // Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol. 938, pp. 13–22 (2019). Springer, Cham. ISSN 2194-5365 (Online). https://doi.org/10.1007/978-3-030-16621-2_2
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Published
2021-12-24
How to Cite
Radchenko К. (2021). Application of discrete wavelet transforms for forecasting the level of load on the web server of general purpose computer networks . COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (45), 90-96. https://doi.org/10.36910/6775-2524-0560-2021-45-13
Section
Computer science and computer engineering
