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Recommending Resolutions for Problems Identified by Monitoring
| Citations: | 2 - 2 self |
BibTeX
@MISC{Tang_recommendingresolutions,
author = {Liang Tang and Tao Li and Larisa Shwartz and Genady Grabarnik},
title = {Recommending Resolutions for Problems Identified by Monitoring},
year = {}
}
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Abstract
Service Providers are facing an increasingly intense competitive landscape and growing industry requirements. Modern service infrastructure management focuses on the development of methodologies and tools for improving the efficiency and quality of service. It is desirable to run a service in a fully automated operation environment. Automated problem resolution, however, is difficult. It is particularly difficult for the weakly-coupled service composition, since the coupling is not defined at design time. Monitoring software systems are designed to actively capture events and automatically generate incident tickets or event tickets. Repeating events generate similar event tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. We apply a recommendation systems approach to resolution of event tickets. In addition, we extend the recommendation methodology to take into account possible falsity of some of the tickets. The paper presents an analysis of the historical event tickets from a large service provider and proposes two resolution-recommendation algorithms for event tickets utilizing historical tickets. The recommendation algorithms take into account false positives often generated by monitoring systems. An additional penalty is incorporated in the algorithms to control the number of misleading resolutions in the recommendation results. An extensive empirical evaluation on three ticket data sets demonstrates that our proposed algorithms achieve a high accuracy with a small percentage of misleading results. I.
Keyphrases
problem identified event ticket problem resolution recommendation result monitoring software system design time account false positive extensive empirical evaluation ticket data set high accuracy weakly-coupled service composition capture event historical event ticket service provider incident ticket industry requirement intense competitive landscape recommendation system approach vast number recommendation methodology small percentage historical ticket resolution-recommendation algorithm similar event ticket large service provider additional penalty account possible falsity operation environment modern service infrastructure management
