Label-Less: A Semi-Automatic Labelling Tool for KPI Anomalies

Nengwen Zhao, Jing Zhu, Rong Liu, Dapeng Liu, Ming Zhang, Dan Pei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Scopus citations

Abstract

KPI (Key Performance Indicator) anomaly detection is critical for Internet-based services to ensure the quality and reliability. However, existing algorithms' performance in reality is far from satisfying due to the lack of sufficient KPI anomaly data to help train and evaluate these algorithms. In this paper, we argue that labeling overhead is the main hurdle to obtain such datasets.Thus we novelly propose a semi-automatic labelling tool called Label-Less, which minimizes the labeling overhead in order to enable an ImageNet-like large-scale KPI anomaly dataset with high-quality ground truth. One novel technique in Label-Less is robust and rapid anomaly similarity search, which saves operators from scanning and checking the long KPIs back and forth for abnormal patterns or label consistency. In our evaluations using 30 real KPIs from a large Internet company, our anomaly similarity search achieves the best F-score of 0.95 on average, and a real-time per-KPI response time (less than 0.5 second). Overall, the feedback from deployment in practice shows that Label-Less can reduce operators' labeling overhead by more than 90%.

Original languageEnglish
Title of host publicationINFOCOM 2019 - IEEE Conference on Computer Communications
Pages1882-1890
Number of pages9
ISBN (Electronic)9781728105154
DOIs
StatePublished - Apr 2019
Event2019 IEEE Conference on Computer Communications, INFOCOM 2019 - Paris, France
Duration: 29 Apr 20192 May 2019

Publication series

NameProceedings - IEEE INFOCOM
Volume2019-April
ISSN (Print)0743-166X

Conference

Conference2019 IEEE Conference on Computer Communications, INFOCOM 2019
Country/TerritoryFrance
CityParis
Period29/04/192/05/19

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