research-article

UBL: unsupervised behavior learning for predicting performance anomalies in virtualized cloud systems

Authors:

Daniel Joseph Dean,

Xiaohui GuAuthors Info & Claims

ICAC '12: Proceedings of the 9th international conference on Autonomic computing

Pages 191 - 200

https://doi.org/10.1145/2371536.2371572

Published: 18 September 2012 Publication History

Abstract

Infrastructure-as-a-Service (IaaS) clouds are prone to performance anomalies due to their complex nature. Although previous work has shown the effectiveness of using statistical learning to detect performance anomalies, existing schemes often assume labelled training data, which requires significant human effort and can only handle previously known anomalies. We present an Unsupervised Behavior Learning (UBL) system for IaaS cloud computing infrastructures. UBL leverages Self-Organizing Maps to capture emergent system behaviors and predict unknown anomalies. For scalability, UBL uses residual resources in the cloud infrastructure for behavior learning and anomaly prediction with little add-on cost. We have implemented a prototype of the UBL system on top of the Xen platform and conducted extensive experiments using a range of distributed systems. Our results show that UBL can predict performance anomalies with high accuracy and achieve sufficient lead time for automatic anomaly prevention. UBL supports large-scale infrastructure-wide behavior learning with negligible overhead.

References

[1]

Amazon elastic compute cloud. http://aws.amazon.com/ec2/.

[2]

Apache Hadoop System. http://hadoop.apache.org/core/.

[3]

Httperf. http://code.google.com/p/httperf/.

[4]

RUBiS: Rice University Bidding System. http://rubis.ow2.org.

[5]

The IRCache Project. http://www.ircache.net/.

[6]

Virtual computing lab. http://vcl.ncsu.edu/.

[7]

P. Barham, A. Donnelly, R. Isaacs, and R. Mortier. Using magpie for request extraction and workload modelling. In Proc. of OSDI, 2004.

Digital Library

[8]

P. Barham and et al. Xen and the Art of Virtualization. In Proc. of SOSP, 2003.

Digital Library

[9]

S. Bhatia, A. Kumar, M. E. Fiuczynski, and L. Peterson. Lightweight, high-resolution monitoring for troubleshooting production systems. In Proc. of OSDI, 2008.

Digital Library

[10]

P. Bodik, M. Goldszmidt, and A. Fox. Hilighter: Automatically building robust signatures of performance behavior for small- and large-scale systems. In Proc. of SysML, 2008.

Digital Library

[11]

D. Breitgand, M. B.-Yehuda, M. Factor, H. Kolodner, V. Kravtsov, and D. Pelleg. NAP: a building block for remediating performance bottlenecks via black box network analysis. In Proc. ICAC, 2009.

Digital Library

[12]

S. K. Cha, I. Moraru, J. Jang, J. Truelove, D. Brumley, and D. G. Andersen. Splitscreen: enabling efficient, distributed malware detection. In Proc. of NSDI, 2010.

Digital Library

[13]

L. Cherkasova, K. Ozonat, N. Mi, J. Symons, and E. Smirni. Anomaly? application change? or workload change? In Proc. of DSN, 2008.

[14]

C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. Live migration of virtual machines. In Proc. of NSDI, 2005.

Digital Library

[15]

I. Cohen, M. Goldszmidt, T. Kelly, J. Symons, and J. S. Chase. Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control. In Proc. of OSDI, 2004.

Digital Library

[16]

I. Cohen, S. Zhang, M. Goldszmidt, J. Symons, T. Kelly, and A. Fox. Capturing, indexing, clustering, and retrieving system history. In Proc. of SOSP, 2005.

Digital Library

[17]

S. Duan, S. Babu, and K. Munagala. Fa: A system for automating failure diagnosis. In Proc. of ICDE, 2009.

Digital Library

[18]

B. Gedik, H. Andrade, K.-L. Wu, P. S. Yu, and M. Doo. SPADE: the system s declarative stream processing engine. In Proc. of SIGMOD, 2008.

Digital Library

[19]

Z. Gong and X. Gu. PAC: Pattern-driven Application Consolidation for Efficient Cloud Computing. In Proc. of MASCOTS, 2010.

Digital Library

[20]

X. Gu and H. Wang. Online anomaly prediction for robust cluster systems. In Proc. of ICDE, 2009.

Digital Library

[21]

G. Jiang, H. Chen, and K. Yoshihira. Discovering likely invariants of distributed transaction systems for autonomic system management. In Proc. of ICAC, 2006.

Digital Library

[22]

M. Jiang, M. Munawar, T. Reidemeister, and P. A. S. Ward. Automatic fault detection and diagnosis in complex software systems by information-theoretic monitoring. In Proc. of DSN, 2009.

[23]

M. P. Kasick, J. Tan, R. Gandhi, and P. Narasimhan. Black-box problem diagnosis in parallel file systems. In Proc. of FAST, 2010.

Digital Library

[24]

T. Kohonen, M. R. Schroeder, and T. S. Huang, editors. Self-Organizing Maps. Springer, 3rd edition, 2001.

Digital Library

[25]

A. Makanju, A. N. Zincir-Heywood, and E. E. Milios. Fast entropy based alert detection in super computer. In Proc. of DSN, 2010.

Digital Library

[26]

I. T. Olliffe. Principal Component Analysis. Springer-Verlag, 2002.

[27]

R. Powers, M. Goldszmidt, and I. Cohen. Short term performance forecasting in enterprise systems. In Proc. of KDD, 2005.

Digital Library

[28]

P. Reynolds, J. Wiener, J. Mogul, M. Aguilera, and A. Vahdat. Wap5: black-box performance debugging for wide-area systems. In Proc. of WWW, 2006.

Digital Library

[29]

K. Shen, C. Stewart, C. Li, and X. Li. Reference-driven performance anomaly identification. In Proc. of SIGMETRICS, 2009.

Digital Library

[30]

C. Stewart, T. Kelly, and A. Zhang. Exploiting nonstationarity for performance prediction. In Proc. of Eurosys, 2007.

Digital Library

[31]

C. Stewart and K. Shen. Performance modeling and system management for multi-component online service. In Proc. of NSDI, 2005.

Digital Library

[32]

P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, 2005.

Digital Library

[33]

Y. Tan, X. Gu, and H. Wang. Adaptive system anomaly prediction for large-scale hosting infrastructures. In Proc. of PODC, 2010.

Digital Library

[34]

Y. Tan, H. Nguyen, Z. Shen, X. Gu, C. Venkatramani, and D. Rajan. PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems. In Proc. of ICDCS, 2012.

Digital Library

[35]

C. Wang, V. Talwar, K. Schwan, and P. Ranganathan. Online detection of utility cloud anomalies using metric distributions. In Proc. of NOMS, 2010.

[36]

A. W. Williams, S. M. Pertet, and P. Narasimah. Tiresias: Black-box failure prediction in distributed systems. In Proc. of IPDPS, 2007.

Cited By

Sruthi PGuo ZChu DChen ZZhang Y(2024)Demystifying the Fight Against Complexity: A Comprehensive Study of Live Debugging Activities in Production Cloud SystemsProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698568(341-360)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698568
Luo HZheng YChen KZhao S(2024)Probabilistic Temporal Fusion Transformers for Large-Scale KPI Anomaly DetectionIEEE Access10.1109/ACCESS.2024.335320112(9123-9137)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3353201
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
https://doi.org/10.3390/app132212126
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Index Terms

UBL: unsupervised behavior learning for predicting performance anomalies in virtualized cloud systems
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. General and reference
  1. Cross-computing tools and techniques
    1. Reliability

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Published In

cover image ACM Conferences

ICAC '12: Proceedings of the 9th international conference on Autonomic computing

September 2012

222 pages

ISBN:9781450315203

DOI:10.1145/2371536

General Chair:
Dejan Milojicic
HP Labs
,
Program Chairs:
Dongyan Xu
Purdue University
,
Vanish Talwar
HP Labs

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 September 2012

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ICAC '12: 9th International Conference on Autonomic Computing

September 18 - 20, 2012

California, San Jose, USA

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Cited By

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https://dl.acm.org/doi/10.1145/3698038.3698568
Luo HZheng YChen KZhao S(2024)Probabilistic Temporal Fusion Transformers for Large-Scale KPI Anomaly DetectionIEEE Access10.1109/ACCESS.2024.335320112(9123-9137)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3353201
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
https://doi.org/10.3390/app132212126
Yang NShi YSu ZWang XYan ZKong F(2023)FSFP: A Fine-Grained Online Service System Performance Fault Prediction Method Based on Cross-attention2023 30th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC60848.2023.00018(81-90)Online publication date: 4-Dec-2023
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