Abstract
Improving computer network availability has been a focus of researchers for the past 30 years and considerable investigation into the use of AI and Machine Learning, primarily in the operate space has been conducted. Previous efforts have been primarily reactive in nature, monitoring networks, developing base models, and trying to predict future failures based on those models. This approach has shown limited success due to the dynamic nature of network equipment and function. Cisco has been developing capabilities over the last decade to proactively analysis network devices and identify issues that could impact a networks availability. In the current approach issues are identified to the customer and it is the customer’s responsibility to identify the issues that they determine need to be fixed. The capability has been trialed over the last 2 years and the research discussed in this paper is focused on the analysis of their actions. Machine Learning is applied to the issue consumption data set and observations made on the features that can be used to predict which issues will be fixed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hood, C., Ji, C.: Proactive network-fault detection. IEEE Trans. Reliabil. 46(3), 333–341 (1997)
Lazar, A., Wang, W., Dent, R.: Models and algorithms for network fault detection and identification: a review. In: Proceeding of the International Conference on Communication Systems, pp. 52–59, Singapore (1992)
Dawes, N., Altoft, J., Pagurek, B.: Network diagnosis by reasoning in uncertain nested evidence spaces. IEEE Trans. Commun. 43(24), 466–476 (1995)
Deng, R., Lazar, A., Wang, W.: A probabilistic approach to fault diagnosis in linear lightwave networks. IEEE Sel. Areas Commun. 11, 1438–1448 (1993)
Lakhina, A., Corvella, M., Diot, C.: Diagnosing network-wide traffic anomalies. ASC SIGCOMM Comput. Commun. Rev. 34(4), 219–230 (2004)
Bashar, A., Parr, G., McClean, S., Scotney, B., Nauck, D.: Application of Bayesian networks for autonomic network management. J. Netw. Syst. Manag. 22(2), 174–207 (2014)
Pitakrat, T., van Hoom, A., Grunske, L.: A Comparison of machine learning algorithms for proactive hard disk drive failure detection. In: Proceedings of the 4th International ACM Sigsoft Symposion on Architecting Critical Systems, pp. 1–10 (2013)
Castelli, R., Heidelberger, P., Hunter, S., Trivedi, K., Vaidyanathan, W., Zeggert, W.: Proactive management of software aging. IBM J. Res. Dev. 45(2), 311–332 (2001)
Eschelbeck, G.: A proactive approach for computer security systems. J. Netw. Comput. Appl. 23(2), 109–130 (2000)
Ramachandran, S., Ramachandran, A.: Rapid and proactive approach on exploration of vulnerabilities in cloud based operating systems. Int. J. Comput. Appl. 42(3), 37–44 (2012)
Kycyman, E.: Discovering correctness constraints for self-management of system configuration. In: Proceedings of the International Conference on Autonomic Computing, pp. 28–35 (2004)
Oppenheimer, D., Ganapathi, A., Patterson, D.: Why do internet services fail, and what can be done about it? In: Proceedings of the with USENIX Symposium on Internet Technologies and Systems, Seattle, WA, pp. 1–16 (2003)
Yuan, D., Xie, Y., Panigraphy, R., Yang, J., Verbowski, C., Kumar, A.: Context-based online configuration-error detections. In: Proceedings of the 2011 USENIX Annual Technical Conference, pp. 619–634 (2011)
Xu, T., et al.: Do not blame users for misconfigurations. In: Proceedings of the 24th ACM Symposium on Operating Systems Principles, pp. 224–259 (2013)
Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9, 16 (2018)
Connected TAC. http://www.cisco.com/c/en/us/support/services/connected-tac/index.html. Accessed 24 Jan 2020
H20 Driverless AI. https://www.h2o.ai/products/h2o-driverless-ai/. Accessed 29 Jan 2020
Gradient Boosting. https://en.wikipedia.org/wiki/Gradient_boosting. Accessed 29 Jan 2020
Area under the curve. https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve. Accessed 19 Jan 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Allen, D.M., Goloubew, D. (2020). Customer Self-remediation of Proactive Network Issue Detection and Notification. In: Degen, H., Reinerman-Jones, L. (eds) Artificial Intelligence in HCI. HCII 2020. Lecture Notes in Computer Science(), vol 12217. Springer, Cham. https://doi.org/10.1007/978-3-030-50334-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-50334-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50333-8
Online ISBN: 978-3-030-50334-5
eBook Packages: Computer ScienceComputer Science (R0)