Abstract
It is well recognized in this digital world that, businesses, government, and people depend on reliable network infrastructure for all aspects of daily operations such as for i.e. Banking, retail, transportation and even socializing. Moreover, today, with the growing trend for the internet of thing, demands for a safe network management system has tremendously increased. Network failures are expensive: network downtime or outages should be avoided as it might affect business operations and might generate a tremendous cost due to the Mean Time to Repair in Network Infrastructure (MTR). This paper presents an ongoing work in exploring the use of machine learning algorithms for better diagnosis of network failure by using PING. To this end, we have analyzed 3 methods such Machine Learning (ML), Feature Selection with ML and hyperparameter tuning of ML. Within each method we used 3 algorithms such as KNN, Logistic Regression and Decision Tree algorithms and benchmarked them with each other’s in order to define the best accuracy of ping failure identification.
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Helmy, M., Arntzen Bechina, A.A., Siqveland, A. (2019). Using Machine Learning for Identifying Ping Failure in Large Network Topology. In: Coppola, M., Carlini, E., D’Agostino, D., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2018. Lecture Notes in Computer Science(), vol 11113. Springer, Cham. https://doi.org/10.1007/978-3-030-13342-9_18
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DOI: https://doi.org/10.1007/978-3-030-13342-9_18
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