Scalable monitoring via threshold compression in a large operational 3G network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Suk bok | - |
dc.contributor.author | Pei, Dan | - |
dc.contributor.author | Hajiaghayi, M. | - |
dc.contributor.author | Pefkianakis, I. | - |
dc.contributor.author | Lu, Songwu | - |
dc.contributor.author | Yan, He | - |
dc.contributor.author | Ge, Zihui | - |
dc.contributor.author | Yates, Jennifer | - |
dc.contributor.author | Kosseifi, Mario | - |
dc.date.accessioned | 2021-06-23T12:03:20Z | - |
dc.date.available | 2021-06-23T12:03:20Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2011-06 | - |
dc.identifier.issn | 0163-5999 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/39076 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Scalable monitoring via threshold compression in a large operational 3G network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Suk bok | - |
dc.identifier.doi | 10.1145/1993744.1993793 | - |
dc.identifier.scopusid | 2-s2.0-79960159060 | - |
dc.identifier.bibliographicCitation | Performance Evaluation Review, v.39, no.1 SPEC. ISSUE, pp.135 - 136 | - |
dc.relation.isPartOf | Performance Evaluation Review | - |
dc.citation.title | Performance Evaluation Review | - |
dc.citation.volume | 39 | - |
dc.citation.number | 1 SPEC. ISSUE | - |
dc.citation.startPage | 135 | - |
dc.citation.endPage | 136 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | 3G Networks | - |
dc.subject.keywordPlus | Large networks | - |
dc.subject.keywordPlus | Network element | - |
dc.subject.keywordPlus | Optimization framework | - |
dc.subject.keywordPlus | Performance monitoring | - |
dc.subject.keywordPlus | Spatial domains | - |
dc.subject.keywordPlus | Spatial temporals | - |
dc.subject.keywordPlus | Threshold behavior | - |
dc.subject.keywordPlus | Threshold setting | - |
dc.subject.keywordPlus | Time dimension | - |
dc.subject.keywordPlus | Computer systems | - |
dc.subject.keywordPlus | Monitoring | - |
dc.subject.keywordPlus | Wireless networks | - |
dc.subject.keywordAuthor | Large networks | - |
dc.subject.keywordAuthor | Network element | - |
dc.subject.keywordAuthor | Spatial domains | - |
dc.subject.keywordAuthor | 3G Networks | - |
dc.subject.keywordAuthor | Computer systems | - |
dc.subject.keywordAuthor | Wireless networks | - |
dc.subject.keywordAuthor | Threshold behavior | - |
dc.subject.keywordAuthor | Optimization framework | - |
dc.subject.keywordAuthor | Time dimension | - |
dc.subject.keywordAuthor | Threshold setting | - |
dc.subject.keywordAuthor | Spatial temporals | - |
dc.subject.keywordAuthor | Performance monitoring | - |
dc.subject.keywordAuthor | Monitoring | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/1993744.1993793 | - |
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