Development of a method for selecting the approximatimg functions for the observable processes of cloud infrastructure

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.200372

Keywords:

cloud infrastructure monitoring, computer network, function approximation, computational burden

Abstract

This paper considers the techniques for improving the effectiveness of monitoring the cloud infrastructure processes implying the reduction of a computational burden while maintaining the required level of measurement accuracy. A technique for organizing the monitoring of cloud infrastructure processes, based on the approximation of accumulated measurements, has been further developed in this study. The necessary and sufficient set of approximating functions has been built, corresponding to the key properties of the observable processes. A method for selecting the approximating functions for the observable cloud infrastructure processes has been constructed. The method implies the assessment of properties of an observable process and the selection of its approximating function.

The practical value of this research relates to the ability to reduce the computational burden by reducing the number of planned measurements at an acceptable level of the decrease in their accuracy. The originality of the approach is the use of the a priori data about the observable processes aimed to obtain more accurate estimates of their properties. The practical implementation of the proposed method shows a 20–40 % decrease in the number of planned measurements at the level of monitoring accuracy not lower than 95 %. The proposed method makes it possible to reduce the load on cloud infrastructure components, to decrease the use of processor time, as well as the disk and random-access memories of physical and virtual nodes. The results of the study can be used for the software implementation of the system of cloud infrastructure monitoring

Author Biographies

Oleksii Grytsenko, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

Postgraduate Student

Department of Information Control System

Vladimir Sayenko, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

PhD

Department of Information Control System

References

  1. Network Monitoring Tools. Available at: http://www.slac.stanford.edu/xorg/nmtf/nmtf-tools.html
  2. Savic, M., Ljubojevic, M., Gajin, S. (2020). A Novel Approach to Client-Side Monitoring of Shared Infrastructures. IEEE Access, 8, 44175–44189. doi: https://doi.org/10.1109/access.2020.2978172
  3. Lavy, M., Meggitt, A. (2001). Windows Management Instrumentation (WMI). Indianapolis, Ind.: New Riders.
  4. Natu, M., Ghosh, R. K., Shyamsundar, R. K., Ranjan, R. (2016). Holistic Performance Monitoring of Hybrid Clouds: Complexities and Future Directions. IEEE Cloud Computing, 3 (1), 72–81. doi: https://doi.org/10.1109/mcc.2016.13
  5. Liaqat, M., Chang, V., Gani, A., Hamid, S. H. A., Toseef, M., Shoaib, U., Ali, R. L. (2017). Federated cloud resource management: Review and discussion. Journal of Network and Computer Applications, 77, 87–105. doi: https://doi.org/10.1016/j.jnca.2016.10.008
  6. Ward, J. S., Barker, A. (2015). Cloud cover: monitoring large-scale clouds with Varanus. Journal of Cloud Computing, 4 (1). doi: https://doi.org/10.1186/s13677-015-0041-9
  7. Alcaraz Calero, J. M., Aguado, J. G. (2015). MonPaaS: An Adaptive Monitoring Platformas a Service for Cloud Computing Infrastructures and Services. IEEE Transactions on Services Computing, 8 (1), 65–78. doi: https://doi.org/10.1109/tsc.2014.2302810
  8. Kozat, U. C., Liang, G., Kokten, K., Tapolcai, J. (2016). On Optimal Topology Verification and Failure Localization for Software Defined Networks. IEEE/ACM Transactions on Networking, 24 (5), 2899–2912. doi: https://doi.org/10.1109/tnet.2015.2494850
  9. Khalili, M., Zhang, M., Borbor, D., Wang, L., Scarabeo, N., Zamor, M.-A. (2019). Monitoring and Improving Managed Security Services inside a Security Operation Center. ICST Transactions on Security and Safety, 5 (18), 157413. doi: https://doi.org/10.4108/eai.8-4-2019.157413
  10. Tudoran, R., Costan, A., Antoniu, G. (2016). OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds. IEEE Transactions on Cloud Computing, 4 (1), 76–89. doi: https://doi.org/10.1109/tcc.2015.2440254
  11. Weingärtner, R., Bräscher, G. B., Westphall, C. B. (2015). Cloud resource management: A survey on forecasting and profiling models. Journal of Network and Computer Applications, 47, 99–106. doi: https://doi.org/10.1016/j.jnca.2014.09.018
  12. Surianarayanan, C., Chelliah, P. R. (2019). Cloud Monitoring. Essentials of Cloud Computing, 241–254. doi: https://doi.org/10.1007/978-3-030-13134-0_8
  13. Grytsenko, O. (2015). Evaluation method of information models of observed processes in computer network. Eastern-European Journal of Enterprise Technologies, 1 (2 (73)), 4–11. doi: https://doi.org/10.15587/1729-4061.2015.36277
  14. Inozemtsev, K. O., Sharapov, M. P. (2014). O vybore approksimiruyushchey funktsii dlya opisaniya zavisimosti otnositel'nogo energeticheskogo razresheniya gamma-spektrometrov ot energii gamma-kvantov. Vestnik magistratury, 1 (7 (34)), 4–13.
  15. Chatfield, C. (1995). Problem Solving: A statistician's guide. Chapman and Hall/CRC, 325. doi: https://doi.org/10.1201/b15238
  16. Saenko, V. I., Gritsenko, A. I. (2014). Informatsionnye modeli nablyudaemyh protsessov dlya monitoringa komp'yuternyh setey. Visnyk Natsionalnoho tekhnichnoho universytetu "KhPI". Seriya: Novi rishennia v suchasnykh tekhnolohiyakh, 48, 55–66.
  17. Lunt, M. (2013). Introduction to statistical modelling: linear regression. Rheumatology, 54 (7), 1137–1140. doi: https://doi.org/10.1093/rheumatology/ket146
  18. Lukashin, Yu. P. (2003). Adaptivnye metody kratkosrochnogo prognozirovaniya vremennyh ryadov. Moscow: Finansy i statistika, 416.
  19. Lemeshko, B. Yu. (1997). Robastnye metody otsenivaniya i otbrakovka anomal'nyh izmereniy. Zavodskaya laboratoriya, 63 (5), 43–49.
  20. Saenko, V. I., Gritsenko, A. I. (2007). A sampling method for the processes of contiguous monitoring. Radioelektronika i informatika, 4, 113–118.
  21. Hernandez, E. A., Chidester, M. C. George, A. D. (2001). Adaptive Sampling for Network Management. Journal of Network and Systems Management, 9, 409–434. doi: https://doi.org/10.1023/A:1012980307500

Downloads

Published

2020-04-30

How to Cite

Grytsenko, O., & Sayenko, V. (2020). Development of a method for selecting the approximatimg functions for the observable processes of cloud infrastructure. Eastern-European Journal of Enterprise Technologies, 2(2 (104), 17–24. https://doi.org/10.15587/1729-4061.2020.200372

Issue

Vol. 2 No. 2 (104) (2020): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2020 Oleksii Grytsenko, Vladimir Sayenko

Creative Commons License

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

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.