research-article

CoFlux: robustly correlating KPIs by fluctuations for service troubleshooting

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Dan PeiAuthors Info & Claims

IWQoS '19: Proceedings of the International Symposium on Quality of Service

Article No.: 13, Pages 1 - 10

https://doi.org/10.1145/3326285.3329048

Published: 24 June 2019 Publication History

Abstract

Internet-based service companies monitor a large number of KPIs (Key Performance Indicators) to ensure their service quality and reliability. Correlating KPIs by fluctuations reveals interactions between KPIs under anomalous situations and can be extremely useful for service troubleshooting. However, such a KPI flux-correlation has been little studied so far in the domain of Internet service operations management. A major challenge is how to automatically and accurately separate fluctuations from normal variations in KPIs with different structural characteristics (such as seasonal, trend and stationary) for a large number of KPIs. In this paper, we propose CoFlux, an unsupervised approach, to automatically (without manual selection of algorithm fitting and parameter tuning) determine whether two KPIs are correlated by fluctuations, in what temporal order they fluctuate, and whether they fluctuate in the same direction. CoFlux's robust feature engineering and robust correlation score computation enable it to work well against the diverse KPI characteristics. Our extensive experiments have demonstrated that CoFlux achieves the best F1-Scores of 0.84 (0.90), 0.92 (0.95), 0.95 (0.99), in answering these three questions, in the two real datasets from a top global Internet company, respectively. Moreover, we showed that CoFlux is effective in assisting service troubleshooting through the applications of alert compression, recommending Top N causes, and constructing fluctuation propagation chains.

References

[1]

Mike P Papazoglou and Willem-Jan Van Den Heuvel. Service oriented architectures: approaches, technologies and research issues. The VLDB journal, 16(3):389--415, 2007.

Digital Library

[2]

Chen Luo, Jian-Guang Lou, Qingwei Lin, Qiang Fu, Rui Ding, Dongmei Zhang, and Zhe Wang. Correlating events with time series for incident diagnosis. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1583--1592. ACM, 2014.

Digital Library

[3]

Dapeng Liu, Youjian Zhao, Haowen Xu, Yongqian Sun, Dan Pei, Jiao Luo, Xiaowei Jing, and Mei Feng. Opprentice: Towards practical and automatic anomaly detection through machine learning. In Proceedings of the 2015 Internet Measurement Conference, pages 211--224. ACM, 2015.

Digital Library

[4]

Shenglin Zhang, Ying Liu, Dan Pei, Yu Chen, Xianping Qu, Shimin Tao, Zhi Zang, Xiaowei Jing, and Mei Feng. Funnel: Assessing software changes in web-based services. IEEE Transactions on Service Computing, 2016.

[5]

Yongqian Sun, Youjian Zhao, Ya Su, Dapeng Liu, Xiaohui Nie, Yuan Meng, Shiwen Cheng, Dan Pei, Shenglin Zhang, Xianping Qu, and Xuanyou Guo. Hotspot: Anomaly localization for additive kpis with multi-dimensional attributes. IEEE Access, 6:10909--10923, 2018.

[6]

Xiaohui Nie, Youjian Zhao, Kaixin Sui, Dan Pei, Yu Chen, and Xianping Qu. Mining causality graph for automatic web-based service diagnosis. In Performance Computing and Communications Conference (IPCCC), 2016 IEEE 35th International, pages 1--8. IEEE, 2016.

[7]

Jacob Cohen. Statistical power analysis for the behavioural sciences, 1988.

[8]

Jingmin Xu, Yuan Wang, Pengfei Chen, and Ping Wang. Lightweight and adaptive service api performance monitoring in highly dynamic cloud environment. In 2017 IEEE International Conference on Services Computing (SCC), pages 35--43. IEEE, 2017.

[9]

Adam J Oliner, Ashutosh V Kulkarni, and Alex Aiken. Using correlated surprise to infer shared influence. In Dependable Systems and Networks (DSN), 2010 IEEE/IFIP International Conference on, pages 191--200. IEEE, 2010.

[10]

Yasushi Hamao, Ronald W Masulis, and Victor Ng. Correlations in price changes and volatility across international stock markets. The review of financial studies, 3(2):281--307, 1990.

[11]

Maurizio Filippone and Guido Sanguinetti. Information theoretic novelty detection. Pattern Recognition, 43(3):805--814, 2010.

Digital Library

[12]

Helmut Lütkepohl. Forecasting with varma models. Handbook of economic forecasting, 1:287--325, 2006.

[13]

Richard ID Harris. Using cointegration analysis in econometric modelling. 1995.

[14]

Huida Qiu, Yan Liu, Niranjan A Subrahmanya, and Weichang Li. Granger causality for time-series anomaly detection. In Data Mining (ICDM), 2012 IEEE 12th International Conference on, pages 1074--1079. IEEE, 2012.

Digital Library

[15]

Jianqing Fan and Qiwei Yao. Nonlinear time series: nonparametric and parametric methods. Springer Science & Business Media, 2008.

[16]

Shashank Shanbhag and Tilman Wolf. Accurate anomaly detection through parallelism. IEEE network, 23(1):22--28, 2009.

Digital Library

[17]

Yasushi Sakurai, Spiros Papadimitriou, and Christos Faloutsos. Braid: Stream mining through group lag correlations. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data, pages 599--610. ACM, 2005.

Digital Library

[18]

Suk-Bok Lee, Dan Pei, MohammadTaghi Hajiaghayi, Ioannis Pefkianakis, Songwu Lu, He Yan, Zihui Ge, Jennifer Yates, and Mario Kosseifi. Threshold compression for 3g scalable monitoring. In INFOCOM, 2012 Proceedings IEEE, pages 1350--1358. IEEE, 2012.

[19]

Yingying Chen, Ratul Mahajan, Baskar Sridharan, and Zhi-Li Zhang. A provider-side view of web search response time. In ACM SIGCOMM Computer Communication Review, volume 43, pages 243--254. ACM, 2013.

Digital Library

[20]

David R Choffnes, Fabián E Bustamante, and Zihui Ge. Crowdsourcing service-level network event monitoring. ACM SIGCOMM Computer Communication Review, 41(4):387--398, 2011.

Digital Library

[21]

He Yan, Ashley Flavel, Zihui Ge, Alexandre Gerber, Dan Massey, Christos Papadopoulos, Hiren Shah, and Jennifer Yates. Argus: End-to-end service anomaly detection and localization from an isp's point of view. In INFOCOM, 2012 Proceedings IEEE, pages 2756--2760. IEEE, 2012.

[22]

Keyi Zhang, Ramazan Gençay, and M Ege Yazgan. Application of wavelet decomposition in time-series forecasting. Economics Letters, 158:41--46, 2017.

[23]

Katherine S Pollard and Mark J Van Der Laan. A method to identify significant clusters in gene expression data. 2002.

[24]

Xiwang Yang, Harald Steck, Yang Guo, and Yong Liu. On top-k recommendation using social networks. In Proceedings of the sixth ACM conference on Recommender systems, pages 67--74. ACM, 2012.

Digital Library

[25]

William A Gardner, Antonio Napolitano, and Luigi Paura. Cyclostationarity: Half a century of research. Signal processing, 86(4):639--697, 2006.

Digital Library

Cited By

Wang CZhou TChen LZhao SCai Z(2025)ISSD: Indicator Selection for Time Series State DetectionProceedings of the ACM on Management of Data10.1145/37096983:1(1-25)Online publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1145/3709698
Wu SGuan J(2024)Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points AnalysisAlgorithms10.3390/a1705021717:5(217)Online publication date: 16-May-2024
https://doi.org/10.3390/a17050217
Wang CYuan YZhou TCai Z(2024)Detecting State Correlations between Heterogeneous Time SeriesProceedings of the 2024 2nd International Conference on Advances in Artificial Intelligence and Applications10.1145/3712623.3712645(131-137)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3712623.3712645
Show More Cited By

Index Terms

CoFlux: robustly correlating KPIs by fluctuations for service troubleshooting
1. Networks
  1. Network services
    1. Network monitoring
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance

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IWQoS '19: Proceedings of the International Symposium on Quality of Service

June 2019

420 pages

ISBN:9781450367783

DOI:10.1145/3326285

Copyright © 2019 ACM.

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Publication History

Published: 24 June 2019

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Beijing National Research Center for Information Science and Technology (BNRist)
Okawa Research Grant

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IWQoS '19

IWQoS '19: IEEE/ACM International Symposium on Quality of Service

June 24 - 25, 2019

Arizona, Phoenix

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Cited By

Wang CZhou TChen LZhao SCai Z(2025)ISSD: Indicator Selection for Time Series State DetectionProceedings of the ACM on Management of Data10.1145/37096983:1(1-25)Online publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1145/3709698
Wu SGuan J(2024)Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points AnalysisAlgorithms10.3390/a1705021717:5(217)Online publication date: 16-May-2024
https://doi.org/10.3390/a17050217
Wang CYuan YZhou TCai Z(2024)Detecting State Correlations between Heterogeneous Time SeriesProceedings of the 2024 2nd International Conference on Advances in Artificial Intelligence and Applications10.1145/3712623.3712645(131-137)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3712623.3712645
Wang JPeng CXu LSha Y(2024)A Service Anomaly Detection and Root Cause Location Method for Complex Power Systems2024 4th Power System and Green Energy Conference (PSGEC)10.1109/PSGEC62376.2024.10721013(1031-1035)Online publication date: 22-Aug-2024
https://doi.org/10.1109/PSGEC62376.2024.10721013
Gu WSun XLiu JHuo YChen ZZhang JGu JYang YLyu M(2024)KPIRoot: Efficient Monitoring Metric-based Root Cause Localization in Large-scale Cloud Systems2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00046(403-414)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00046
Chen YXiao BJia HXia H(2024)Fast and Robust Localization of Multi-dimensional Root Causes in Online Service2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692632(85-89)Online publication date: 22-Mar-2024
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Yang JGuo YChen YZhao Y(2023)Hi-RCA: A Hierarchy Anomaly Diagnosis Framework Based on Causality and Correlation AnalysisApplied Sciences10.3390/app13221212613:22(12126)Online publication date: 8-Nov-2023
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Sun YCheng DYang TJi YZhang SZhu MXiong XFan QLiang MPei DMa TChen Y(2023)Efficient and Robust KPI Outlier Detection for Large-Scale DatacentersIEEE Transactions on Computers10.1109/TC.2023.327228872:10(2858-2871)Online publication date: Oct-2023
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Li DZhang SSun YGuo YChe ZChen SZhong ZLiang MShao MLi MLiu SZhang YPei D(2023)An Empirical Analysis of Anomaly Detection Methods for Multivariate Time Series2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE59848.2023.00014(57-68)Online publication date: 9-Oct-2023
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