{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T13:30:59Z","timestamp":1758893459953,"version":"3.40.5"},"reference-count":65,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.<\/jats:p>","DOI":"10.2478\/acss-2022-0005","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T09:59:37Z","timestamp":1661335177000},"page":"43-54","source":"Crossref","is-referenced-by-count":3,"title":["Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision"],"prefix":"10.2478","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4840-7905","authenticated-orcid":false,"given":"Ahmed Yassine","family":"Chakor","sequence":"first","affiliation":[{"name":"Laboratory of Automation Technology, FST of Tangier , Abdelmalek Essaadi University , Tetouan , Morocco"}]},{"given":"Azmani","family":"Monir","sequence":"additional","affiliation":[{"name":"Laboratory of Automation Technology, FST of Tangier , Abdelmalek Essaadi University , Tetouan , Morocco"}]},{"given":"Azmani","family":"Abdellah","sequence":"additional","affiliation":[{"name":"Laboratory of Automation Technology, FST of Tangier , Abdelmalek Essaadi University , Tetouan , Morocco"}]}],"member":"374","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"2024042804303469349_j_acss-2022-0005_ref_001","doi-asserted-by":"crossref","unstructured":"[1] F. Salfner and M. Malek, \u201cArchitecting dependable systems with proactive fault management,\u201d in Architecting Dependable Systems VII. Lecture Notes in Computer Science, A. Casimiro, R. de Lemos, and C. Gacek, Eds., vol 6420. Springer, Berlin, Heidelberg, 2010, pp. 171\u2013200. https:\/\/doi.org\/10.1007\/978-3-642-17245-8_8","DOI":"10.1007\/978-3-642-17245-8_8"},{"key":"2024042804303469349_j_acss-2022-0005_ref_002","doi-asserted-by":"crossref","unstructured":"[2] F. Salfner and M. Malek, \u201cUsing hidden semi-Markov models for effective online failure prediction,\u201d in 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007), Beijing, China, Oct. 2007, pp. 161\u2013174. https:\/\/doi.org\/10.1109\/SRDS.2007.35","DOI":"10.1109\/SRDS.2007.35"},{"key":"2024042804303469349_j_acss-2022-0005_ref_003","doi-asserted-by":"crossref","unstructured":"[3] L. Eeckhout, R. Sundareswara, J. J. Yi, D. J. Lilja, and P. Schrater, \u201cAccurate statistical approaches for generating representative workload compositions,\u201d in Proceedings of the 2005 IEEE International Symposium on Workload Characterization, IISWC-2005, Austin, TX, USA, 2005, pp. 56\u201366. https:\/\/doi.org\/10.1109\/IISWC.2005.1526001","DOI":"10.1109\/IISWC.2005.1526001"},{"key":"2024042804303469349_j_acss-2022-0005_ref_004","doi-asserted-by":"crossref","unstructured":"[4] J. P. Magalhaes and L. M. Silva, \u201cAnomaly detection techniques for Web-based applications: An experimental study,\u201d in 2012 11th IEEE International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA, Aug. 2012, pp. 181\u201390. https:\/\/doi.org\/10.1109\/NCA.2012.27","DOI":"10.1109\/NCA.2012.27"},{"key":"2024042804303469349_j_acss-2022-0005_ref_005","doi-asserted-by":"crossref","unstructured":"[5] S. Fu, \u201cFailure-aware construction and reconfiguration of distributed virtual machines for high availability computing,\u201d in 2009 9th IEEE\/ACM International Symposium on Cluster Computing and the Grid, Shanghai, China, May 2009, pp. 372\u201379. https:\/\/doi.org\/10.1109\/CCGRID.2009.21","DOI":"10.1109\/CCGRID.2009.21"},{"key":"2024042804303469349_j_acss-2022-0005_ref_006","doi-asserted-by":"crossref","unstructured":"[6] G. Hoffmann and M. Malek, \u201cCall availability prediction in a telecommunication system: A data driven empirical approach,\u201d in Proceedings of the IEEE Symposium on Reliable Distributed Systems, Leeds, UK, Oct. 2006, pp. 83\u201395. https:\/\/doi.org\/10.1109\/SRDS.2006.12","DOI":"10.1109\/SRDS.2006.12"},{"key":"2024042804303469349_j_acss-2022-0005_ref_007","doi-asserted-by":"crossref","unstructured":"[7] L. Sihyung, K. Levanti, and H. S. Kim, \u201cNetwork monitoring: Present and future,\u201d Computer Networks, vol. 65, pp. 84\u201398, Jun. 2014. https:\/\/doi.org\/10.1016\/j.comnet.2014.03.007","DOI":"10.1016\/j.comnet.2014.03.007"},{"key":"2024042804303469349_j_acss-2022-0005_ref_008","unstructured":"[8] A. L. Porter, F. Rossini, T. W. Mason, J. Banks, and T. Roper, Forecasting and Management of Technology, 2nd edition. USA: Wiley, 2011."},{"key":"2024042804303469349_j_acss-2022-0005_ref_009","unstructured":"[9] AIRI, 2002. AIRI (Associazione Italiana per la Ricerca Industriale), 2002. Il monitoraggio tecnologico, Edizioni AIRI."},{"key":"2024042804303469349_j_acss-2022-0005_ref_010","unstructured":"[10] EIRMA, 1999. EIRMA (European Industrial Research Management Association), 1999. Working group 55. Technology monitoring for business success, Edizioni EIRMA, Brussels, Belgium."},{"key":"2024042804303469349_j_acss-2022-0005_ref_011","unstructured":"[11] W. B. Ashton et al., Keeping Abreast of Science and Technology: Technical Intelligence for Business. USA: Battelle Press, 1997."},{"key":"2024042804303469349_j_acss-2022-0005_ref_012","unstructured":"[12] C. M. Christensen, The Innovator\u2019s Dilemma: When New Technologies Cause Great Firms to Fail. Boston, MA: Harvard Business School Press, 1997."},{"key":"2024042804303469349_j_acss-2022-0005_ref_013","doi-asserted-by":"crossref","unstructured":"[13] M. Iansiti, \u201cHow the incumbent can win: Managing technological transitions in the semiconductor industry,\u201d Management Science, vol. 46, no. 2, pp. 169\u2013185, Feb. 2000. https:\/\/doi.org\/10.1287\/mnsc.46.2.169.11922","DOI":"10.1287\/mnsc.46.2.169.11922"},{"key":"2024042804303469349_j_acss-2022-0005_ref_014","doi-asserted-by":"crossref","unstructured":"[14] E. Lichtenthaler, \u201cTechnological change and the technology intelligence process: a case study,\u201d Journal of Engineering and Technology Management, vol. 21, no. 4, pp. 331\u2013348, Dec. 2004. https:\/\/doi.org\/10.1016\/j.jengtecman.2004.09.003","DOI":"10.1016\/j.jengtecman.2004.09.003"},{"key":"2024042804303469349_j_acss-2022-0005_ref_015","unstructured":"[15] B. Twiss, Managing Technological Innovation. London: Pitman Publishing, 1993."},{"key":"2024042804303469349_j_acss-2022-0005_ref_016","doi-asserted-by":"crossref","unstructured":"[16] A. Lemos and A. Porto, \u201cTechnology forecasting techniques and competitive technology intelligence: tools for improving the innovation process,\u201d Industrial Management & Data Systems, vol. 98, no. 7, pp. 330\u2013337, Nov. 1998. https:\/\/doi.org\/10.1108\/02635579810227698","DOI":"10.1108\/02635579810227698"},{"key":"2024042804303469349_j_acss-2022-0005_ref_017","doi-asserted-by":"crossref","unstructured":"[17] J. H. Vanston, \u201cBetter forecast, better plan, better results,\u201d Research Technology Management, vol. 46, no. 1, pp. 47\u201358, Jan. 2003. https:\/\/doi.org\/10.1080\/08956308.2003.11671544","DOI":"10.1080\/08956308.2003.11671544"},{"key":"2024042804303469349_j_acss-2022-0005_ref_018","doi-asserted-by":"crossref","unstructured":"[18] A. Papalambrou, A. G. Voyiatzis, D. N. Serpanos, and P. Soufrilas, \u201cMonitoring of a DTN2 network,\u201d 2011 Baltic Congress on Future Internet and Communications. Riga, Latvia, Mar. 2011, pp. 116\u2013119. https:\/\/doi.org\/10.1109\/bcfic-riga.2011.5733226","DOI":"10.1109\/BCFIC-RIGA.2011.5733226"},{"key":"2024042804303469349_j_acss-2022-0005_ref_019","unstructured":"[19] F. Bagaskara, \u201cNetwork monitoring system analysis using OpenNMS to analyze the irregularities of the Internet network,\u201d 1\u20133, 2019. https:\/\/www.researchgate.net\/publication\/334536508_network_monitoring_system_analysis_using_opennms_to_analyze_the_irregularities_of_the_internet_network"},{"key":"2024042804303469349_j_acss-2022-0005_ref_020","unstructured":"[20] W. Barth, Nagios: System and Network Monitoring. San Francisco, CA, USA: No Starch Press, 2006."},{"key":"2024042804303469349_j_acss-2022-0005_ref_021","unstructured":"[21] The Cacti Group, \u201cCacti Monitoring Tool,\u201d 2018. [Online]. Available: https:\/\/www.cacti.net\/. Accessed on: Sept. 24, 2021."},{"key":"2024042804303469349_j_acss-2022-0005_ref_022","unstructured":"[22] Zabbix LLC, \u201cZabbix monitoring system,\u201d 2018. [Online]. Available: https:\/\/www.zabbix.com\/. Accessed on: Sept. 24, 2021."},{"key":"2024042804303469349_j_acss-2022-0005_ref_023","doi-asserted-by":"crossref","unstructured":"[23] J. Case, M. Fedor, M. Schoffstall, and J. Davin, \u201cRFC 1157. Simple network management protocol (SNMP),\u201d MIT Laboratory for Computer Science, Cambridge, May 1990. https:\/\/doi.org\/10.17487\/rfc1157","DOI":"10.17487\/rfc1157"},{"key":"2024042804303469349_j_acss-2022-0005_ref_024","doi-asserted-by":"crossref","unstructured":"[24] S. Kumar et al., \u201cAlgorithms to accelerate multiple regular expressions matching for deep packet inspection,\u201d in Proc. SIGCOMM\u201806 Conf. Apps., Technologies, Architectures, and Protocols for Comp. Commun., vol. 36, no. 4, Pisa, Italy, Sept. 2006, pp. 339\u2013350. https:\/\/doi.org\/10.1145\/1151659.1159952","DOI":"10.1145\/1151659.1159952"},{"key":"2024042804303469349_j_acss-2022-0005_ref_025","doi-asserted-by":"crossref","unstructured":"[25] X. Dimitropoulos, P. Hurley, A. Kind, \u201cProbabilistic LossyCounting: An efficient algorithm for finding heavy hitters,\u201d SIGCOMM Comp. Commun. Rev., vol 38, no 1, Jan. 2008, pp. 7\u201316. https:\/\/doi.org\/10.1145\/1341431.1341433","DOI":"10.1145\/1341431.1341433"},{"key":"2024042804303469349_j_acss-2022-0005_ref_026","doi-asserted-by":"crossref","unstructured":"[26] R. Zemouri and Z. Noureddine, \u201cAutonomous and adaptive procedure for cumulative failure prediction,\u201d Neural Computing and Applications, vol. 21, no. 2, pp. 319\u201331, Apr. 2011. https:\/\/doi.org\/10.1007\/s00521-011-0585-7","DOI":"10.1007\/s00521-011-0585-7"},{"key":"2024042804303469349_j_acss-2022-0005_ref_027","unstructured":"[27] Y. Watanabe and Y. Matsumoto, \u201cOnline failure prediction in cloud datacenters,\u201d Fujitsu Scientific and Technical Journal, vol. 50, no. 1, pp. 66\u201371, Jan. 2014. https:\/\/docplayer.net\/3092614-Online-failure-prediction-in-cloud-datacenters.html"},{"key":"2024042804303469349_j_acss-2022-0005_ref_028","unstructured":"[28] J. Murray, G. Hughes, and K. Kreutz-Delgado, \u201cHard drive failure prediction using non-parametric statistical methods,\u201d in Proc. ICANN\/ICONIP, Istanbul, Turkey, 2003. https:\/\/www.researchgate.net\/publication\/228972414_Hard_drive_failure_prediction_using_non-parametric_statistical_methods"},{"key":"2024042804303469349_j_acss-2022-0005_ref_029","doi-asserted-by":"crossref","unstructured":"[29] Y. Liang, Y. Zhang, M. Jette, S. Anand, and R. Sahoo, \u201cBlueGene\/L failure analysis and prediction models,\u201d in International Conference on Dependable Systems and Networks (DSN\u201906), Philadelphia, PA, USA, Jul. 2006, pp. 425\u2013434. https:\/\/doi.org\/10.1109\/DSN.2006.18","DOI":"10.1109\/DSN.2006.18"},{"key":"2024042804303469349_j_acss-2022-0005_ref_030","doi-asserted-by":"crossref","unstructured":"[30] T. Pitakrat, A. Van Hoorn, and L. Grunske, \u201cIncreasing dependability of component-based software systems by online failure prediction (Short Paper),\u201d in 2014 Tenth European Dependable Computing Conference, Newcastle, UK, 2014, pp. 66\u201369. https:\/\/doi.org\/10.1109\/EDCC.2014.28","DOI":"10.1109\/EDCC.2014.28"},{"key":"2024042804303469349_j_acss-2022-0005_ref_031","doi-asserted-by":"crossref","unstructured":"[31] T. Zseby, T. Hirsch, and B. Claise, \u201cPacket sampling for flow accounting: Challenges and limitations,\u201d in Passive and Active Network Measurement. PAM 2008. Lecture Notes in Computer Science, M. Claypool and S. Uhlig, Eds., vol 4979. Springer, Berlin, Heidelberg, 2008. https:\/\/doi.org\/10.1007\/978-3-540-79232-1_7","DOI":"10.1007\/978-3-540-79232-1_7"},{"key":"2024042804303469349_j_acss-2022-0005_ref_032","doi-asserted-by":"crossref","unstructured":"[32] K. G. Anagnostakis, M. Greenwald, and R. S. Ryger, \u201ccing: measuring network-internal delays using only existing infrastructure,\u201d in INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), vol. 3, San Francisco, CA, Jul. 2003, pp. 2112\u20132121 https:\/\/doi.org\/10.1109\/INFCOM.2003.1209232","DOI":"10.1109\/INFCOM.2003.1209232"},{"key":"2024042804303469349_j_acss-2022-0005_ref_033","doi-asserted-by":"crossref","unstructured":"[33] N. Hu, E. Li, Z. Mao, P. Steenkiste, and J. Wang, \u201cLocating Internet bottlenecks: Algorithms, measurements, and implications,\u201d vol. 34, no. 4, pp. 41\u201354, Oct. 2004. https:\/\/doi.org\/10.1145\/1015467.1015474","DOI":"10.1145\/1030194.1015474"},{"key":"2024042804303469349_j_acss-2022-0005_ref_034","doi-asserted-by":"crossref","unstructured":"[34] B. Floering, B. Brothers, Z. Kalbarczyk, and R. Iyer, \u201cAn adaptive architecture for monitoring and failure analysis of high-speed networks,\u201d in Proceedings of the 2002 International Conference on Dependable Systems and Networks, Washington, DC, USA, Jun. 2002, pp. 69\u201378. https:\/\/doi.org\/10.1109\/DSN.2002.1028888","DOI":"10.1109\/DSN.2002.1028888"},{"key":"2024042804303469349_j_acss-2022-0005_ref_035","doi-asserted-by":"crossref","unstructured":"[35] G. L. dos Santos et al., \u201eUAMA: A unified architecture for active measurements in IP networks \u2013 End-to-end objective quality indicators,\u201d in 2007 10th IFIP\/IEEE International Symposium on Integrated Network Management, Munich, Germany, June 2007, pp. 246\u2013253. https:\/\/doi.org\/10.1109\/INM.2007.374789","DOI":"10.1109\/INM.2007.374789"},{"key":"2024042804303469349_j_acss-2022-0005_ref_036","doi-asserted-by":"crossref","unstructured":"[36] M. J. Luckie, A. J. McGregor, and H.-W. Braun, \u201cTowards improving packet probing techniques,\u201d in Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, San Francisco, California, USA, 2001, pp. 145\u2013150. https:\/\/doi.org\/10.1145\/505202.505221","DOI":"10.1145\/505217.505221"},{"key":"2024042804303469349_j_acss-2022-0005_ref_037","doi-asserted-by":"crossref","unstructured":"[37] R. R. Kompella, K. Levchenko, A. C. Snoeren, and G. Varghese, \u201cEvery microsecond counts: tracking fine-grain latencies with a lossy difference aggregator,\u201d SIGCOMM Comput. Commun. Rev., vol. 39, no. 4, pp. 255\u2013266, Oct. 2009. https:\/\/doi.org\/10.1145\/1594977.1592599","DOI":"10.1145\/1594977.1592599"},{"key":"2024042804303469349_j_acss-2022-0005_ref_038","doi-asserted-by":"crossref","unstructured":"[38] S. Machiraju and D. Veitch, \u201cA measurement-friendly network (MFN) architecture,\u201d in Proceedings of the 2006 SIGCOMM workshop on Internet network management (INM \u201806), New York, NY, USA, Sep. 2006, pp. 53\u201358. https:\/\/doi.org\/10.1145\/1162638.1162647","DOI":"10.1145\/1162638.1162647"},{"key":"2024042804303469349_j_acss-2022-0005_ref_039","doi-asserted-by":"crossref","unstructured":"[39] P. Papageorge, J. McCann, and M. Hicks, \u201cPassive aggressive measurement with MGRP,\u201d ACM SIGCOMM Computer Communication Review, vol. 39, no. 4, pp. 279\u2013290, Oct. 2009. https:\/\/doi.org\/10.1145\/1592568.1592601","DOI":"10.1145\/1594977.1592601"},{"key":"2024042804303469349_j_acss-2022-0005_ref_040","doi-asserted-by":"crossref","unstructured":"[40] C. Estan, K. Keys, D. Moore, and G. Varghese, \u201cBuilding a better NetFlow,\u201d Computer Communication Review, vol. 34, no. 4, pp. 245\u2013256, Oct. 2004. https:\/\/doi.org\/10.1145\/1015467.1015495","DOI":"10.1145\/1030194.1015495"},{"key":"2024042804303469349_j_acss-2022-0005_ref_041","doi-asserted-by":"crossref","unstructured":"[41] E. A. Hernandez, M. C. Chidester, and A.D. George, \u201cAdaptive sampling for network management,\u201d Journal of Network and Systems Management, vol. 9, pp. 409\u2013434, Dec. 2001. https:\/\/doi.org\/10.1023\/A:1012980307500","DOI":"10.1023\/A:1012980307500"},{"key":"2024042804303469349_j_acss-2022-0005_ref_042","doi-asserted-by":"crossref","unstructured":"[42] C. Fraleigh et al.,\u201cPacket-level traffic measurements from the Sprint IP backbone,\u201d IEEE Netw., vol. 17, no. 6, pp. 6\u201316, Nov.\u2013Dec. 2003. https:\/\/doi.org\/10.1109\/MNET.2003.1248656","DOI":"10.1109\/MNET.2003.1248656"},{"key":"2024042804303469349_j_acss-2022-0005_ref_043","doi-asserted-by":"crossref","unstructured":"[43] Y. J. Lin and M. C. Chan, \u201cA scalable monitoring approach based on aggregation and refinement,\u201d IEEE Journal on Selected Areas in Communications, vol. 20, no. 4, pp. 677\u2013690, May 2002. https:\/\/doi.org\/10.1109\/JSAC.2002.1003035","DOI":"10.1109\/JSAC.2002.1003035"},{"key":"2024042804303469349_j_acss-2022-0005_ref_044","doi-asserted-by":"crossref","unstructured":"[44] M. Cheikhrouhou and J. Labetoulle, \u201cAn efficient polling layer for SNMP,\u201d NOMS 2000. 2000 IEEE\/IFIP Network Operations and Management Symposium \u2018The Networked Planet: Management Beyond 2000\u2019 (Cat. No.00CB37074), Honolulu, HI, USA, Apr. 2000, pp. 477\u2013490. https:\/\/doi.org\/10.1109\/NOMS.2000.830404","DOI":"10.1109\/NOMS.2000.830404"},{"key":"2024042804303469349_j_acss-2022-0005_ref_045","doi-asserted-by":"crossref","unstructured":"[45] B. Trammell, E. Boschi, M. Lutz, T. Zseby, and A. Wagner, \u201cSpecification of the IP Flow Information Export (IPFIX) file format,\u201d RFC 5655, pp. 1\u201364, Oct. 2009. https:\/\/doi.org\/10.17487\/rfc5655","DOI":"10.17487\/rfc5655"},{"key":"2024042804303469349_j_acss-2022-0005_ref_046","unstructured":"[46] B. Trammell and C. Gates, \u201cNAF: The NetSA aggregated flow tool suite,\u201d in Proc. USENIX LISA, 20th Large Installation System Administration Conference, Washington, USA, Dec. 2006, pp. 221\u2013231. https:\/\/www.usenix.org\/legacy\/event\/lisa06\/tech\/full_papers\/trammell\/trammell_html\/index.html"},{"key":"2024042804303469349_j_acss-2022-0005_ref_047","unstructured":"[47] S. Romig, \u201cThe OSU flow-tools package and CISCO NetFlow logs,\u201d in Proc. USENIX LISA, New Orleans, Louisiana, USA, Dec. 2000, pp. 291\u2013303. chrome-extension:\/\/efaidnbmnnnibpcajpcglclefindmkaj\/https:\/\/www.usenix.org\/legacy\/event\/lisa2000\/full_papers\/fullmer\/fullmer.pdf"},{"key":"2024042804303469349_j_acss-2022-0005_ref_048","unstructured":"[48] D. Plonka, \u201cFlowScan: a network traffic flow reporting and visualization tool,\u201d in Proc. USENIX LISA, New Orleans, Louisiana, USA, Dec. 2000."},{"key":"2024042804303469349_j_acss-2022-0005_ref_049","unstructured":"[49] T. Oetiker, \u201cMRTG: The multi router traffic grapher,\u201d in Proc. USENIX LISA, 1998, pp. 141\u2013148."},{"key":"2024042804303469349_j_acss-2022-0005_ref_050","unstructured":"[50] \u201cSNMP Network Analysis and Presentation,\u201d SNAPP, 2013. [Online]. Available: http:\/\/sourceforge.net\/projects\/snapp\/"},{"key":"2024042804303469349_j_acss-2022-0005_ref_051","doi-asserted-by":"crossref","unstructured":"[51] A. Gonzalez et al., \u201cBig data and analysis of data transfers for international research networks using NetSage,\u201d in 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA, Jun. 2017, pp. 344\u2013351. https:\/\/doi.org\/10.1109\/BigDataCongress.2017.51","DOI":"10.1109\/BigDataCongress.2017.51"},{"key":"2024042804303469349_j_acss-2022-0005_ref_052","unstructured":"[52] H. Rashid, Power Electronics: Circuits, Devices, and Applications. Pearson\/Prentice Hall, 2004."},{"key":"2024042804303469349_j_acss-2022-0005_ref_053","doi-asserted-by":"crossref","unstructured":"[53] B. Nguyen, Z. Ge, J. van der Merwe, H. Yan, and J. Yates, \u201cABSENCE: Usagebased failure detection in mobile networks,\u201d in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, MobiCom\u201915, 2015, 464\u2013476. https:\/\/doi.org\/10.1145\/2789168.2790127","DOI":"10.1145\/2789168.2790127"},{"key":"2024042804303469349_j_acss-2022-0005_ref_054","doi-asserted-by":"crossref","unstructured":"[54] Z. Noshad et al., \u201cFault detection in wireless sensor networks through the Random Forest classifier,\u201d Sensors, vol. 19, no. 7, Apr. 2019. https:\/\/doi.org\/10.3390\/s19071568648019630939764","DOI":"10.3390\/s19071568"},{"key":"2024042804303469349_j_acss-2022-0005_ref_055","doi-asserted-by":"crossref","unstructured":"[55] V. C. Ferreira, R. C. Carrano, J. O. Silva, C. V. N. Albuquerque, D. C. Muchaluat-Saade, and D. Passos,\u201cFault detection and diagnosis for solar-powered wireless mesh networks using machine learning\u201d in IFIP\/IEEE symposium on integrated network and service management (IM), Lisbon, Portugal, May 2017, pp. 456\u2013462. https:\/\/doi.org\/10.23919\/INM.2017.7987312","DOI":"10.23919\/INM.2017.7987312"},{"key":"2024042804303469349_j_acss-2022-0005_ref_056","doi-asserted-by":"crossref","unstructured":"[56] J. C. Duenas, J. M. Navarro, H. A. Parada G., J. Andion and F. Cuadrado, \u201cApplying event stream processing to network online failure prediction,\u201d IEEE Communications Magazine, vol. 56, no. 1, pp. 166\u2013170, 2018. https:\/\/doi.org\/10.1109\/MCOM.2018.1601135","DOI":"10.1109\/MCOM.2018.1601135"},{"key":"2024042804303469349_j_acss-2022-0005_ref_057","doi-asserted-by":"crossref","unstructured":"[57] L. Breiman, \u201cRandom forests,\u201d Machine Learning, vol. 45, pp. 5\u201332, Oct. 2001. https:\/\/doi.org\/10.1023\/A:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"2024042804303469349_j_acss-2022-0005_ref_058","unstructured":"[58] R. Beverly, \u201cRTG: A scalable SNMP statistics architecture for service providers,\u201d in Proc. USENIX LISA, 2002."},{"key":"2024042804303469349_j_acss-2022-0005_ref_059","unstructured":"[59] P. A. Boccard, Nexworld, 2017. [Online]. Available: https:\/\/nexworld.fr\/machine-learning-retour-aux-sources\/[Accessed: 2022]."},{"key":"2024042804303469349_j_acss-2022-0005_ref_060","doi-asserted-by":"crossref","unstructured":"[60] B. Letham et al., \u201cInterpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model,\u201d The Annals of Applied Statistics, vol. 9, no. 3, pp. 1350\u20131371, Sep. 2015. https:\/\/doi.org\/10.1214\/15-AOAS848","DOI":"10.1214\/15-AOAS848"},{"key":"2024042804303469349_j_acss-2022-0005_ref_061","doi-asserted-by":"crossref","unstructured":"[61] J. Shotton, et al., \u201cReal-time human pose recognition in parts from single depth images,\u201d Communications of the ACM, vol. 56, no. 1, pp. 116\u2013124, 2013. https:\/\/doi.org\/10.1145\/2398356.2398381","DOI":"10.1145\/2398356.2398381"},{"key":"2024042804303469349_j_acss-2022-0005_ref_062","doi-asserted-by":"crossref","unstructured":"[62] B. Yu and K. Kumbier, \u201cThree principles of data science: predictability, computability, and stability (pcs),\u201d arXiv, preprint arXiv:1901.08152, 2019.","DOI":"10.1109\/BigData.2018.8622080"},{"key":"2024042804303469349_j_acss-2022-0005_ref_063","doi-asserted-by":"crossref","unstructured":"[63] Z. M. Fadlullah et al., \u201cState-of-the-art deep learning: Evolving machine intelligence toward tomorrow\u2019s intelligent network traffic control systems,\u201d IEEE Communications Surveys & Tutorials,\u201d vol. 19, no. 4, pp. 2432\u20132455, May 2017. https:\/\/doi.org\/10.1109\/COMST.2017.2707140","DOI":"10.1109\/COMST.2017.2707140"},{"key":"2024042804303469349_j_acss-2022-0005_ref_064","doi-asserted-by":"crossref","unstructured":"[64] D. Boyd and K. Crawford, \u201cCritical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon,\u201d Information, Communication & Society, vol. 15, no. 5, pp. 662\u2013679, May 2012. https:\/\/doi.org\/10.1080\/1369118X.2012.678878","DOI":"10.1080\/1369118X.2012.678878"},{"key":"2024042804303469349_j_acss-2022-0005_ref_065","unstructured":"[65] J. Lopes and P. Sim\u00f5es. Online failure prediction in containerized environments Diss. 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