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NEVERMIND, the problem is already fixed: proactively detecting and troubleshooting customer DSL problems

Published: 30 November 2010 Publication History

Abstract

Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems and reducing customer tickets. Our system consists of: i) a ticket predictor which predicts future customer tickets; and ii) a trouble locator which helps technicians accelerate the troubleshooting process during field dispatches. Both components infer future tickets and trouble locations based on existing sparse line measurements, and the inference models are constructed automatically using supervised machine learning techniques. We propose several novel techniques to address the operational constraints in DSL networks and to enhance the accuracy of NEVERMIND. Extensive evaluations using an entire year worth of customer tickets and measurement data from a large network show that our method can predict thousands of future customer tickets per week with high accuracy and signifcantly reduce the time and effort for diagnosing these tickets. This is benefcial as it has the effect of both reducing the number of customer care calls and improving customer satisfaction.

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  1. NEVERMIND, the problem is already fixed: proactively detecting and troubleshooting customer DSL problems

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      cover image ACM Conferences
      Co-NEXT '10: Proceedings of the 6th International COnference
      November 2010
      349 pages
      ISBN:9781450304481
      DOI:10.1145/1921168
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      Published: 30 November 2010

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      Co-NEXT '10: Conference on emerging Networking EXperiments and Technologies
      November 30 - December 3, 2010
      Pennsylvania, Philadelphia

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      Overall Acceptance Rate 198 of 789 submissions, 25%

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      Cited By

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      • (2024)SEEN: ML Assisted Cellular Service DiagnosisProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690678(1060-1073)Online publication date: 4-Dec-2024
      • (2022)Automation of Detection and Fault Management Response of Common Last-Mile Loss-Of-Connectivity Outages Within the Access NetworkResearch Anthology on Cross-Disciplinary Designs and Applications of Automation10.4018/978-1-6684-3694-3.ch031(607-635)Online publication date: 2022
      • (2022)Towards automatic troubleshooting for user-level performance degradation in cellular servicesProceedings of the 28th Annual International Conference on Mobile Computing And Networking10.1145/3495243.3560535(716-728)Online publication date: 14-Oct-2022
      • (2021)The Influence of Background Sounds, Physical Sounds, and Managers’ Proactive Customer Service Regarding Situational Sounds on Customer Satisfaction in the Restaurant IndustryJournal of Small Business Strategy10.53703/001c.2983131:5Online publication date: 22-Dec-2021
      • (2021)Towards Internet-Scale Convolutional Root-Cause Analysis with DIAGNET2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS49936.2021.00084(746-755)Online publication date: May-2021
      • (2020)Automation of Detection and Fault Management Response of Common Last-Mile Loss-Of-Connectivity Outages Within the Access NetworkInternational Journal of Wireless Networks and Broadband Technologies10.4018/IJWNBT.20200101019:1(1-26)Online publication date: Jan-2020
      • (2020)Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2019.295434017:1(547-561)Online publication date: Mar-2020
      • (2019)Smart Prediction of the Complaint Hotspot Problem in Mobile NetworkProceedings of the 2019 Workshop on Network Meets AI & ML10.1145/3341216.3342209(22-28)Online publication date: 14-Aug-2019
      • (2016)A Multivariate Approach to Predicting Quantity of Failures in Broadband Networks Based on a Recurrent Neural NetworkJournal of Network and Systems Management10.1007/s10922-015-9348-624:1(189-221)Online publication date: 1-Jan-2016
      • (2015)Fault diagnosis in DSL networks using support vector machinesComputer Communications10.1016/j.comcom.2015.01.00662:C(72-84)Online publication date: 15-May-2015
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