Identifying Recurrent and Unknown Performance Issues

Meng Hui LIM; Jian Guang Lou; Hongyu Zhang; Qiang Fu; Andrew Beng Jin Teoh; Qingwei Lin; Rui Ding; Dongmei Zhang

doi:10.1109/ICDM.2014.96

Identifying Recurrent and Unknown Performance Issues

Meng Hui LIM, Jian Guang Lou, Hongyu Zhang, Qiang Fu, Andrew Beng Jin Teoh, Qingwei Lin, Rui Ding, Dongmei Zhang

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

23 Citations (Scopus)

Abstract

For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.

Original language	English
Title of host publication	Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
Editors	Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Publisher	IEEE
Pages	320-329
Number of pages	10
Edition	January
ISBN (Electronic)	9781479943029
DOIs	https://doi.org/10.1109/ICDM.2014.96
Publication status	Published - 1 Jan 2014
Event	14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China Duration: 14 Dec 2014 → 17 Dec 2014

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
Number	January
Volume	2015-January
ISSN (Print)	1550-4786

Conference

Conference	14th IEEE International Conference on Data Mining, ICDM 2014
Country/Territory	China
City	Shenzhen
Period	14/12/14 → 17/12/14

Scopus Subject Areas

Engineering(all)

User-Defined Keywords

automated diagnosis
duplication detection
Issue identification
metrics
performance

Access to Document

10.1109/ICDM.2014.96

Cite this

LIM, M. H., Lou, J. G., Zhang, H., Fu, Q., Teoh, A. B. J., Lin, Q., Ding, R., & Zhang, D. (2014). Identifying Recurrent and Unknown Performance Issues. In R. Kumar, H. Toivonen, J. Pei, J. Zhexue Huang, & X. Wu (Eds.), Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014 (January ed., pp. 320-329). Article 7023349 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2015-January, No. January). IEEE. https://doi.org/10.1109/ICDM.2014.96

LIM, Meng Hui ; Lou, Jian Guang ; Zhang, Hongyu et al. / Identifying Recurrent and Unknown Performance Issues. Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. editor / Ravi Kumar ; Hannu Toivonen ; Jian Pei ; Joshua Zhexue Huang ; Xindong Wu. January. ed. IEEE, 2014. pp. 320-329 (Proceedings - IEEE International Conference on Data Mining, ICDM; January).

@inproceedings{6775ee36ba574549926b597c4db9a9f4,

title = "Identifying Recurrent and Unknown Performance Issues",

abstract = "For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.",

keywords = "automated diagnosis, duplication detection, Issue identification, metrics, performance",

author = "LIM, {Meng Hui} and Lou, {Jian Guang} and Hongyu Zhang and Qiang Fu and Teoh, {Andrew Beng Jin} and Qingwei Lin and Rui Ding and Dongmei Zhang",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 14th IEEE International Conference on Data Mining, ICDM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",

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LIM, MH, Lou, JG, Zhang, H, Fu, Q, Teoh, ABJ, Lin, Q, Ding, R & Zhang, D 2014, Identifying Recurrent and Unknown Performance Issues. in R Kumar, H Toivonen, J Pei, J Zhexue Huang & X Wu (eds), Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. January edn, 7023349, Proceedings - IEEE International Conference on Data Mining, ICDM, no. January, vol. 2015-January, IEEE, pp. 320-329, 14th IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, 14/12/14. https://doi.org/10.1109/ICDM.2014.96

Identifying Recurrent and Unknown Performance Issues. / LIM, Meng Hui; Lou, Jian Guang; Zhang, Hongyu et al.
Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. ed. / Ravi Kumar; Hannu Toivonen; Jian Pei; Joshua Zhexue Huang; Xindong Wu. January. ed. IEEE, 2014. p. 320-329 7023349 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2015-January, No. January).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Identifying Recurrent and Unknown Performance Issues

AU - LIM, Meng Hui

AU - Lou, Jian Guang

AU - Zhang, Hongyu

AU - Fu, Qiang

AU - Teoh, Andrew Beng Jin

AU - Lin, Qingwei

AU - Ding, Rui

AU - Zhang, Dongmei

PY - 2014/1/1

Y1 - 2014/1/1

N2 - For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.

AB - For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.

KW - automated diagnosis

KW - duplication detection

KW - Issue identification

KW - metrics

KW - performance

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M3 - Conference proceeding

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EP - 329

BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014

A2 - Kumar, Ravi

A2 - Toivonen, Hannu

A2 - Pei, Jian

A2 - Zhexue Huang, Joshua

A2 - Wu, Xindong

PB - IEEE

T2 - 14th IEEE International Conference on Data Mining, ICDM 2014

Y2 - 14 December 2014 through 17 December 2014

ER -

LIM MH, Lou JG, Zhang H, Fu Q, Teoh ABJ, Lin Q et al. Identifying Recurrent and Unknown Performance Issues. In Kumar R, Toivonen H, Pei J, Zhexue Huang J, Wu X, editors, Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014. January ed. IEEE. 2014. p. 320-329. 7023349. (Proceedings - IEEE International Conference on Data Mining, ICDM; January). doi: 10.1109/ICDM.2014.96

Identifying Recurrent and Unknown Performance Issues

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