2022/03/09 by Georg Heiler, Thassilo Gadermaier, Heiler, Georg +7
Computer Science · Engineering · #Advanced Optical Network Technologies #Advanced Photonic Communication Systems #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Optical Network Technologies #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2203.06989
openalex publication_date 2022/03/09 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.