Informatics and Applications
2023, Volume 17, Issue 3, pp 76-87
TOWARD CLUSTERING OF NETWORK COMPUTING INFRASTRUCTURE OBJECTS BASED ON ANALYSIS OF STATISTICAL ANOMALIES IN NETWORK TRAFFIC
- A. K. Gorshenin
- S. A. Gorbunov
- D. Yu. Volkanov
Abstract
The problem of detecting statistical anomalies (that is, outliers in relation to the typical values of upload and download traffic) of the load on the nodes of the network computing infrastructure is considered. The regular scaling in computing resources and storage as well as redirection of data flows is needed due to the increase of load in real systems. The procedure for detecting statistical anomalies in network traffic is proposed using the approximation of observations by the generalized gamma distribution for further clustering of network computing infrastructure objects in order to evaluate resource need. All computational statistical procedures described in the paper are implemented using the R programming language and they are applied for network traffic, simulated using a specialized architectural and software stand. The proposed approaches can also be used for a wider class of telecommunication problems.
[+] References (25)
- Smelyansky, R. L. 2019. Ierarkhicheskie periferiynye vychisleniya [Hierarchical edge computing]. Modelirovanie i analiz informatsionnykh sistem [Modeling and Analysis of Information Systems] 26(1):146-169. doi: 10.18255/1818-1015-2019-1-146-169.
- Smeliansky, R. 2023. Network powered by computing. Edge computing - technology, management and integration. IntechOpen. 21 p. doi: 10.5772/intechopen.110178.
- Rossem, S. V., W. Tavernier, D. Colle, M. Pickavet, and P. Demeester. 2017. Automated monitoring and detection of resource-limited NFV-based services. Conference on Network Softwarization: Softwarization Sustaining a HyperConnected World: en Route to 5G. Piscataway, NJ: IEEE. 8004220. doi: 10.1109/NETSOFT.2017.8004220.
- Malak, D., M. Medard, and J. G. Andrews. 2021. Spatial concentration of caching in wireless heterogeneous networks. IEEE T. Wirel. Commun. 20(6):3397-3414. doi: 10.1109/TWC.2021.3049812.
- Zhang, Z., J. Lu, and H. Chen. 2022. Controller robust placement with dynamic traffic in software-defined networking. Comput. Commun. 194:458-467. doi: 10.1016/ j.comcom.2022.07.018.
- Mesodiakaki, A., E. Zola, and A. Kassler. 2022. Robust and energy-efficient user association and traffic routing in B5G HetNets. Comput. Netw. 217:109305. doi: 10.1016/j.comnet.2022.109305.
- Hosseinzadeh, S., M. Amirmazlaghani, and M. Shajari. 2020. An aggregated statistical approach for net-work flood detection using gamma-normal mixture modeling. Comput. Commun. 152:137-148. doi: 10.1016/ j.comcom.2020.01.028.
- Abood, M. S., A. S. Mustafa, H. F Mahdi, A.-F. A. Mohammed, M. M. Hamdi, and N. A. Hussein. 2021. The analysis of teletraffic and handover performance in cellular system. 3rd Congress (International) on Human-Computer Interaction, Optimization and Robotic Applications Proceedings. Piscataway, NJ: IEEE. 1-5. doi: 10.1109/ HORA52670.2021.9461300.
- Parulekar, M., and A. M. Makowski. 1997. M|G|to input processes: A versatile class of models for network traffic. IEEE INFOCOMProceedings. Piscataway, NJ: IEEE. 2:419-426. doi: 10.1109/INFCOM.1997.644490.
- Tabassum, H., Z. Dawy, E. Hossain, and M.-S. Alouini. 2014. Interference statistics and capacity analysis for uplink transmission in two-tier small cell networks: A geometric probability approach. IEEE T. Wirel. Commun. 13(7):3837-3852. doi: 10.1109/TWC.2014.2314101.
- Noor, K., H. Shahid, H. M. Obaid, A. Rauf, A. Yousaf, and
A. Shahid. 2022. Hybrid underwater intelligent communication system. Wireless Pers. Commun. 125(3):2219-2238. doi: 10.1007/s11277-022-09653-7.
- Padhan, A. K., S. H. Kumar, P. R. Sahu and S. R. Samantaray 2023. Performance analysis of smart grid wide area network with RIS assisted three hop system. IEEE Transactions Signal Information Processing Networks 9:48-59. doi: 10.1109/TSIPN.2023.3239652.
- Gorshenin, A., V. Korolev, V. Kuzmin, and A. Zeifman. 2013. Coordinate-wise versions of the grid method for the analysis of intensities of non-stationary information flows by moving separation of mixtures of gamma- distribution. 27th European Conference on Modelling and Simulation Proceedings. Eds. W Rekdalsbakken, R. T Bye, and H. Zhang. Dudweiler, Germany: Digitaldruck Pirrot GmbHP. 565-568. doi: 10.7148/2013-0565.
- Gorshenin, A., and V. Korolev. 2013. Modelling of statistical fluctuations of information flows by mixtures of gamma distributions. 27th European Conference on Modelling and Simulation Proceedings. Eds. W. Rekdalsbakken, R. T. Bye, and H. Zhang. Dudweiler, Germany: Digitaldruck Pirrot GmbHP. 569-572. doi: 10.7148/2013-0569.
- Gorshenin, A. K., and V. Kuzmin. 2015. Online system for the construction of structural models of information flows. 7th Congress (International) on Ultra Modern Telecommunications and Control Systems and Workshops Proceedings. Piscataway, NJ: IEEE. 216-219. doi: 10.1109/ICUMT2015.7382430.
- Gorshenin, A., and V. Kuzmin. 2016. On an interface of the online system for a stochastic analysis of the varied information flows. AIP Conference Proceedings 1738(1):220009. 4 p. doi: 10.1063/1.4952008.
- Gorshenin, A. K. 2017. O nekotorykh matematicheskikh i programmnykh metodakh postroeniya strukturnykh modeley informatsionnykh potokov [On some mathematical and programming methods for construction of structural
models of information flows]. Informatika i ee Primeneniya - Inform. Appl. 11(1):58-68. doi: 10.14357/ 19922264170105.
- Zonoozi, M. M., P. Dassanayake, and M. Faulkner. 1996. Teletraffic modelling of cellular mobile networks. IEEE VTCP. 2:1274-1277. doi: 10.1109/VETEC.1996.501517.
- Stacy, E. W. 1962. A generalization of the gamma distribution. Ann. Math. Stat. 33(3):1187-1192. doi: 10.1214/aoms/1177704481.
- Korolev, V. Yu., and A. K. Gorshenin. 2020. Probability models and statistical tests for extreme precipitation based on generalized negative binomial distributions. Mathematics 8(4):604. doi: 10.3390/math8040604.
- Huang, B., J. Chen, Q. He, B. Wang, Z. Liu, and Y. Cheng. 2016. HASO: A hot-page aware scheduling optimization method in virtualized NUMA systems. 7th Conference (International) on Information and Communication Systems Proceedings. Piscataway, NJ: IEEE. 68-73. doi: 10.1109/IACS.2016.7476088.
- Tian, H., S. Li, A. Wang, W. Wang, T. Wu, and
H. Yang. 2022. Owl: Performance-aware scheduling for resource-efficient function-as-a-service cloud. 13th Symposium on Cloud Computing Proceedings. New York, NY: Association for Computing Machinery. 78-93. doi: 10.1145/3542929.3563470.
- Gorshenin, A. K. 2016. Kontseptsiya onlayn-kompleksa dlya stokhasticheskogo modelirovaniya real'nykh protsessov [Concept of online service for stochastic modeling of real processes]. Informatika i ee Primeneniya - Inform. Appl. 10(1):72-81. doi: 10.14357/19922264160107.
- Gorshenin, A. K., and V. Yu. Kuzmin. 2017. Research support system for stochastic data processing. Pattern Recognition Image Analysis 27(3):518-524. doi: 10.1134/ S1054661817030117.
- Gorshenin, A. 2018. Toward modern educational IT- ecosystems: From learning management systems to digital platforms. 10th Congress (International) on Ultra Modern Telecommunications and Control Systems and Workshops Proceedings. Piscataway, NJ: IEEE. 329-333. doi: 10.1109/ICUMT.2018.8631229.
[+] About this article
Title
TOWARD CLUSTERING OF NETWORK COMPUTING INFRASTRUCTURE OBJECTS BASED ON ANALYSIS OF STATISTICAL ANOMALIES IN NETWORK TRAFFIC
Journal
Informatics and Applications
2023, Volume 17, Issue 3, pp 76-87
Cover Date
2023-10-10
DOI
10.14357/19922264230311
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
network infrastructure; network traffic; generalized gamma distribution; computational statistics; statistical hypothesis testing; anomaly detection; clustering
Authors
A. K. Gorshenin  ,  , S. A. Gorbunov ,  , and D. Yu. Volkanov
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
M. V. Lomonosov Moscow State University, 1 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation
Moscow Center for Fundamental and Applied Mathematics, M.V. Lomonosov Moscow State University, 1-52 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation
|