References
- [1] F. Salfner and M. Malek, “Architecting dependable systems with proactive fault management,” 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. 171–200. https://doi.org/10.1007/978-3-642-17245-8_8
- [2] F. Salfner and M. Malek, “Using hidden semi-Markov models for effective online failure prediction,” in 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007), Beijing, China, Oct. 2007, pp. 161–174. https://doi.org/10.1109/SRDS.2007.35
- [3] L. Eeckhout, R. Sundareswara, J. J. Yi, D. J. Lilja, and P. Schrater, “Accurate statistical approaches for generating representative workload compositions,” in Proceedings of the 2005 IEEE International Symposium on Workload Characterization, IISWC-2005, Austin, TX, USA, 2005, pp. 56–66. https://doi.org/10.1109/IISWC.2005.1526001
- [4] J. P. Magalhaes and L. M. Silva, “Anomaly detection techniques for Web-based applications: An experimental study,” in 2012 11th IEEE International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA, Aug. 2012, pp. 181–90. https://doi.org/10.1109/NCA.2012.27
- [5] S. Fu, “Failure-aware construction and reconfiguration of distributed virtual machines for high availability computing,” in 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, Shanghai, China, May 2009, pp. 372–79. https://doi.org/10.1109/CCGRID.2009.21
- [6] G. Hoffmann and M. Malek, “Call availability prediction in a telecommunication system: A data driven empirical approach,” in Proceedings of the IEEE Symposium on Reliable Distributed Systems, Leeds, UK, Oct. 2006, pp. 83–95. https://doi.org/10.1109/SRDS.2006.12
- [7] L. Sihyung, K. Levanti, and H. S. Kim, “Network monitoring: Present and future,” Computer Networks, vol. 65, pp. 84–98, Jun. 2014. https://doi.org/10.1016/j.comnet.2014.03.007
- [8] A. L. Porter, F. Rossini, T. W. Mason, J. Banks, and T. Roper, Forecasting and Management of Technology, 2nd edition. USA: Wiley, 2011.
- [9] AIRI, 2002. AIRI (Associazione Italiana per la Ricerca Industriale), 2002. Il monitoraggio tecnologico, Edizioni AIRI.
- [10] EIRMA, 1999. EIRMA (European Industrial Research Management Association), 1999. Working group 55. Technology monitoring for business success, Edizioni EIRMA, Brussels, Belgium.
- [11] W. B. Ashton et al., Keeping Abreast of Science and Technology: Technical Intelligence for Business. USA: Battelle Press, 1997.
- [12] C. M. Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston, MA: Harvard Business School Press, 1997.
- [13] M. Iansiti, “How the incumbent can win: Managing technological transitions in the semiconductor industry,” Management Science, vol. 46, no. 2, pp. 169–185, Feb. 2000. https://doi.org/10.1287/mnsc.46.2.169.11922
- [14] E. Lichtenthaler, “Technological change and the technology intelligence process: a case study,” Journal of Engineering and Technology Management, vol. 21, no. 4, pp. 331–348, Dec. 2004. https://doi.org/10.1016/j.jengtecman.2004.09.003
- [15] B. Twiss, Managing Technological Innovation. London: Pitman Publishing, 1993.
- [16] A. Lemos and A. Porto, “Technology forecasting techniques and competitive technology intelligence: tools for improving the innovation process,” Industrial Management & Data Systems, vol. 98, no. 7, pp. 330–337, Nov. 1998. https://doi.org/10.1108/02635579810227698
- [17] J. H. Vanston, “Better forecast, better plan, better results,” Research Technology Management, vol. 46, no. 1, pp. 47–58, Jan. 2003. https://doi.org/10.1080/08956308.2003.11671544
- [18] A. Papalambrou, A. G. Voyiatzis, D. N. Serpanos, and P. Soufrilas, “Monitoring of a DTN2 network,” 2011 Baltic Congress on Future Internet and Communications. Riga, Latvia, Mar. 2011, pp. 116–119. https://doi.org/10.1109/bcfic-riga.2011.5733226
- [19] F. Bagaskara, “Network monitoring system analysis using OpenNMS to analyze the irregularities of the Internet network,” 1–3, 2019. https://www.researchgate.net/publication/334536508_network_monitoring_system_analysis_using_opennms_to_analyze_the_irregularities_of_the_internet_network
- [20] W. Barth, Nagios: System and Network Monitoring. San Francisco, CA, USA: No Starch Press, 2006.
- [21] The Cacti Group, “Cacti Monitoring Tool,” 2018. [Online]. Available: https://www.cacti.net/. Accessed on: Sept. 24, 2021.
- [22] Zabbix LLC, “Zabbix monitoring system,” 2018. [Online]. Available: https://www.zabbix.com/. Accessed on: Sept. 24, 2021.
- [23] J. Case, M. Fedor, M. Schoffstall, and J. Davin, “RFC 1157. Simple network management protocol (SNMP),” MIT Laboratory for Computer Science, Cambridge, May 1990. https://doi.org/10.17487/rfc1157
- [24] S. Kumar et al., “Algorithms to accelerate multiple regular expressions matching for deep packet inspection,” in Proc. SIGCOMM‘06 Conf. Apps., Technologies, Architectures, and Protocols for Comp. Commun., vol. 36, no. 4, Pisa, Italy, Sept. 2006, pp. 339–350. https://doi.org/10.1145/1151659.1159952
- [25] X. Dimitropoulos, P. Hurley, A. Kind, “Probabilistic LossyCounting: An efficient algorithm for finding heavy hitters,” SIGCOMM Comp. Commun. Rev., vol 38, no 1, Jan. 2008, pp. 7–16. https://doi.org/10.1145/1341431.1341433
- [26] R. Zemouri and Z. Noureddine, “Autonomous and adaptive procedure for cumulative failure prediction,” Neural Computing and Applications, vol. 21, no. 2, pp. 319–31, Apr. 2011. https://doi.org/10.1007/s00521-011-0585-7
- [27] Y. Watanabe and Y. Matsumoto, “Online failure prediction in cloud datacenters,” Fujitsu Scientific and Technical Journal, vol. 50, no. 1, pp. 66–71, Jan. 2014. https://docplayer.net/3092614-Online-failure-prediction-in-cloud-datacenters.html
- [28] J. Murray, G. Hughes, and K. Kreutz-Delgado, “Hard drive failure prediction using non-parametric statistical methods,” in Proc. ICANN/ICONIP, Istanbul, Turkey, 2003. https://www.researchgate.net/publication/228972414_Hard_drive_failure_prediction_using_non-parametric_statistical_methods
- [29] Y. Liang, Y. Zhang, M. Jette, S. Anand, and R. Sahoo, “BlueGene/L failure analysis and prediction models,” in International Conference on Dependable Systems and Networks (DSN’06), Philadelphia, PA, USA, Jul. 2006, pp. 425–434. https://doi.org/10.1109/DSN.2006.18
- [30] T. Pitakrat, A. Van Hoorn, and L. Grunske, “Increasing dependability of component-based software systems by online failure prediction (Short Paper),” in 2014 Tenth European Dependable Computing Conference, Newcastle, UK, 2014, pp. 66–69. https://doi.org/10.1109/EDCC.2014.28
- [31] T. Zseby, T. Hirsch, and B. Claise, “Packet sampling for flow accounting: Challenges and limitations,” 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
- [32] K. G. Anagnostakis, M. Greenwald, and R. S. Ryger, “cing: measuring network-internal delays using only existing infrastructure,” 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. 2112–2121 https://doi.org/10.1109/INFCOM.2003.1209232
- [33] N. Hu, E. Li, Z. Mao, P. Steenkiste, and J. Wang, “Locating Internet bottlenecks: Algorithms, measurements, and implications,” vol. 34, no. 4, pp. 41–54, Oct. 2004. https://doi.org/10.1145/1015467.1015474
- [34] B. Floering, B. Brothers, Z. Kalbarczyk, and R. Iyer, “An adaptive architecture for monitoring and failure analysis of high-speed networks,” in Proceedings of the 2002 International Conference on Dependable Systems and Networks, Washington, DC, USA, Jun. 2002, pp. 69–78. https://doi.org/10.1109/DSN.2002.1028888
- [35] G. L. dos Santos et al., „UAMA: A unified architecture for active measurements in IP networks – End-to-end objective quality indicators,” in 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, Munich, Germany, June 2007, pp. 246–253. https://doi.org/10.1109/INM.2007.374789
- [36] M. J. Luckie, A. J. McGregor, and H.-W. Braun, “Towards improving packet probing techniques,” in Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, San Francisco, California, USA, 2001, pp. 145–150. https://doi.org/10.1145/505202.505221
- [37] R. R. Kompella, K. Levchenko, A. C. Snoeren, and G. Varghese, “Every microsecond counts: tracking fine-grain latencies with a lossy difference aggregator,” SIGCOMM Comput. Commun. Rev., vol. 39, no. 4, pp. 255–266, Oct. 2009. https://doi.org/10.1145/1594977.1592599
- [38] S. Machiraju and D. Veitch, “A measurement-friendly network (MFN) architecture,” in Proceedings of the 2006 SIGCOMM workshop on Internet network management (INM ‘06), New York, NY, USA, Sep. 2006, pp. 53–58. https://doi.org/10.1145/1162638.1162647
- [39] P. Papageorge, J. McCann, and M. Hicks, “Passive aggressive measurement with MGRP,” ACM SIGCOMM Computer Communication Review, vol. 39, no. 4, pp. 279–290, Oct. 2009. https://doi.org/10.1145/1592568.1592601
- [40] C. Estan, K. Keys, D. Moore, and G. Varghese, “Building a better NetFlow,” Computer Communication Review, vol. 34, no. 4, pp. 245–256, Oct. 2004. https://doi.org/10.1145/1015467.1015495
- [41] E. A. Hernandez, M. C. Chidester, and A.D. George, “Adaptive sampling for network management,” Journal of Network and Systems Management, vol. 9, pp. 409–434, Dec. 2001. https://doi.org/10.1023/A:1012980307500
- [42] C. Fraleigh et al.,“Packet-level traffic measurements from the Sprint IP backbone,” IEEE Netw., vol. 17, no. 6, pp. 6–16, Nov.–Dec. 2003. https://doi.org/10.1109/MNET.2003.1248656
- [43] Y. J. Lin and M. C. Chan, “A scalable monitoring approach based on aggregation and refinement,” IEEE Journal on Selected Areas in Communications, vol. 20, no. 4, pp. 677–690, May 2002. https://doi.org/10.1109/JSAC.2002.1003035
- [44] M. Cheikhrouhou and J. Labetoulle, “An efficient polling layer for SNMP,” NOMS 2000. 2000 IEEE/IFIP Network Operations and Management Symposium ‘The Networked Planet: Management Beyond 2000’ (Cat. No.00CB37074), Honolulu, HI, USA, Apr. 2000, pp. 477–490. https://doi.org/10.1109/NOMS.2000.830404
- [45] B. Trammell, E. Boschi, M. Lutz, T. Zseby, and A. Wagner, “Specification of the IP Flow Information Export (IPFIX) file format,” RFC 5655, pp. 1–64, Oct. 2009. https://doi.org/10.17487/rfc5655
- [46] B. Trammell and C. Gates, “NAF: The NetSA aggregated flow tool suite,” in Proc. USENIX LISA, 20th Large Installation System Administration Conference, Washington, USA, Dec. 2006, pp. 221–231. https://www.usenix.org/legacy/event/lisa06/tech/full_papers/trammell/trammell_html/index.html
- [47] S. Romig, “The OSU flow-tools package and CISCO NetFlow logs,” in Proc. USENIX LISA, New Orleans, Louisiana, USA, Dec. 2000, pp. 291–303. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.usenix.org/legacy/event/lisa2000/full_papers/fullmer/fullmer.pdf
- [48] D. Plonka, “FlowScan: a network traffic flow reporting and visualization tool,” in Proc. USENIX LISA, New Orleans, Louisiana, USA, Dec. 2000.
- [49] T. Oetiker, “MRTG: The multi router traffic grapher,” in Proc. USENIX LISA, 1998, pp. 141–148.
- [50] “SNMP Network Analysis and Presentation,” SNAPP, 2013. [Online]. Available: http://sourceforge.net/projects/snapp/
- [51] A. Gonzalez et al., “Big data and analysis of data transfers for international research networks using NetSage,” in 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA, Jun. 2017, pp. 344–351. https://doi.org/10.1109/BigDataCongress.2017.51
- [52] H. Rashid, Power Electronics: Circuits, Devices, and Applications. Pearson/Prentice Hall, 2004.
- [53] B. Nguyen, Z. Ge, J. van der Merwe, H. Yan, and J. Yates, “ABSENCE: Usagebased failure detection in mobile networks,” in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, MobiCom’15, 2015, 464–476. https://doi.org/10.1145/2789168.2790127
- [54] Z. Noshad et al., “Fault detection in wireless sensor networks through the Random Forest classifier,” Sensors, vol. 19, no. 7, Apr. 2019. https://doi.org/10.3390/s19071568648019630939764
- [55] V. C. Ferreira, R. C. Carrano, J. O. Silva, C. V. N. Albuquerque, D. C. Muchaluat-Saade, and D. Passos,“Fault detection and diagnosis for solar-powered wireless mesh networks using machine learning” in IFIP/IEEE symposium on integrated network and service management (IM), Lisbon, Portugal, May 2017, pp. 456–462. https://doi.org/10.23919/INM.2017.7987312
- [56] J. C. Duenas, J. M. Navarro, H. A. Parada G., J. Andion and F. Cuadrado, “Applying event stream processing to network online failure prediction,” IEEE Communications Magazine, vol. 56, no. 1, pp. 166–170, 2018. https://doi.org/10.1109/MCOM.2018.1601135
- [57] L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5–32, Oct. 2001. https://doi.org/10.1023/A:1010933404324
- [58] R. Beverly, “RTG: A scalable SNMP statistics architecture for service providers,” in Proc. USENIX LISA, 2002.
- [59] P. A. Boccard, Nexworld, 2017. [Online]. Available: https://nexworld.fr/machine-learning-retour-aux-sources/[Accessed: 2022].
- [60] B. Letham et al., “Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model,” The Annals of Applied Statistics, vol. 9, no. 3, pp. 1350–1371, Sep. 2015. https://doi.org/10.1214/15-AOAS848
- [61] J. Shotton, et al., “Real-time human pose recognition in parts from single depth images,” Communications of the ACM, vol. 56, no. 1, pp. 116–124, 2013. https://doi.org/10.1145/2398356.2398381
- [62] B. Yu and K. Kumbier, “Three principles of data science: predictability, computability, and stability (pcs),” arXiv, preprint arXiv:1901.08152, 2019.
- [63] Z. M. Fadlullah et al., “State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems,” IEEE Communications Surveys & Tutorials,” vol. 19, no. 4, pp. 2432–2455, May 2017. https://doi.org/10.1109/COMST.2017.2707140
- [64] D. Boyd and K. Crawford, “Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon,” Information, Communication & Society, vol. 15, no. 5, pp. 662–679, May 2012. https://doi.org/10.1080/1369118X.2012.678878
- [65] J. Lopes and P. Simões. Online failure prediction in containerized environments Diss. Universidade de Coimbra, Sep. 2018. http://hdl.handle.net/10316/83550