Conference Paper

Scalable monitoring via threshold compression in a large operational 3G network

DOI: 10.1145/2007116.2007160 Conference: SIGMETRICS 2011, Proceedings of the 2011 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, San Jose, CA, USA, 07-11 June 2011 (Co-located with FCRC 2011)
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ABSTRACT

Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. There exists a fundamental tradeoff between the size of threshold settings and the alarm quality. In this paper, we propose a scalable monitoring solution, called threshold-compression that characterizes the tradeoff via intelligent threshold aggregation. The main insight behind our solution is to identify groups of network elements with similar threshold behaviors across location and time dimensions, thus forming spatial-temporal clusters and generating the associated compressed thresholds within the optimization framework. Our evaluations on a commercial 3G network have demonstrated the effectiveness of our threshold-compression solution, e.g., threshold setting reduction up to 90% within 10% false/miss alarms.

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Available from: Zihui Ge, Sep 28, 2015
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    • "The proof is given in the full version of the paper [1]. Now, all hours in each identified interval group C h of NE group γ can form a spatial-temporal cluster C δ . "
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    ABSTRACT: Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. There exists a fundamental tradeoff between the size of threshold settings and the alarm quality. In this paper, we propose a scalable monitoring solution, called threshold-compression that characterizes the tradeoff via intelligent threshold aggregation. The main insight behind our solution is to identify groups of network elements with similar threshold behaviors across location and time dimensions, thus forming spatial-temporal clusters and generating the associated compressed thresholds within the optimization framework. Our evaluations on a commercial 3G network have demonstrated the effectiveness of our threshold-compression solution, e.g., threshold setting reduction up to 90% within 10% false/miss alarms.
    Full-text · Conference Paper · Jun 2011
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    ABSTRACT: We study the problem of scalable monitoring of operational 3G wireless networks. Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. A fine-grained threshold setting (e.g., perlocation hourly) incurs prohibitively high management complexity, while a single static threshold fails to capture the network dynamics, thus resulting in unacceptably poor alarm quality (up to 70% false/miss alarm rates). In this paper, we propose a scalable monitoring solution, called threshold-compression that can characterize the location- and time-specific threshold trend of each individual network element (NE) with minimal threshold setting. The main insight is to identify groups of NEs with similar threshold behaviors across location and time dimensions, forming spatial-temporal clusters to reduce the number of thresholds while maintaining acceptable alarm accuracy in a large-scale 3G network. Our evaluations based on the operational experience on a commercial 3G network have demonstrated the effectiveness of the proposed solution. We are able to reduce the threshold setting up to 90% with less than 10% false/miss alarms.
    No preview · Article · Jan 2012 · Proceedings - IEEE INFOCOM