Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds

Peiyun Zhang; Sheng Shu; Mengchu Zhou

doi:10.1109/SMC.2018.00686

Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds

Peiyun Zhang, Sheng Shu, Mengchu Zhou

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Fault detection is a crucial technology to improve the performance of cloud systems. Its fixed detection cycle tends to be problematic since it faces high overhead if a small detection cycle is used for well-performing services; while risks missing many faults if a large cycle is adopted for some poorly-performing services. To solve such problems, an algorithm for adaptively adjusting dynamic detection cycle is proposed to decrease the overhead and increase fault detection performance in a cloud environment. It shortens a detection cycle for cloud systems with large fault probability, thus boosting fault detection performance. Otherwise, it increases it, thus decreasing the overhead. The algorithm is based on the proposed detection model by using a decision tree and support vector machine to increase detection performance. Experimental results show that the method is feasible and effective in comparison with some representative methods.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4047-4052
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00686
State	Published - Jan 16 2019
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: Oct 7 2018 → Oct 10 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	10/7/18 → 10/10/18

All Science Journal Classification (ASJC) codes

Information Systems
Information Systems and Management
Health Informatics
Artificial Intelligence
Computer Networks and Communications
Human-Computer Interaction

Keywords

Cloud computing
detection model
dynamic cycle adjusting
fault detection

Access to Document

10.1109/SMC.2018.00686

Cite this

Zhang, P., Shu, S., & Zhou, M. (2019). Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 4047-4052). [8616683] (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00686

Zhang, Peiyun ; Shu, Sheng ; Zhou, Mengchu. / Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4047-4052 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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title = "Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds",

abstract = "Fault detection is a crucial technology to improve the performance of cloud systems. Its fixed detection cycle tends to be problematic since it faces high overhead if a small detection cycle is used for well-performing services; while risks missing many faults if a large cycle is adopted for some poorly-performing services. To solve such problems, an algorithm for adaptively adjusting dynamic detection cycle is proposed to decrease the overhead and increase fault detection performance in a cloud environment. It shortens a detection cycle for cloud systems with large fault probability, thus boosting fault detection performance. Otherwise, it increases it, thus decreasing the overhead. The algorithm is based on the proposed detection model by using a decision tree and support vector machine to increase detection performance. Experimental results show that the method is feasible and effective in comparison with some representative methods.",

keywords = "Cloud computing, detection model, dynamic cycle adjusting, fault detection",

author = "Peiyun Zhang and Sheng Shu and Mengchu Zhou",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China under Grants 61472005 and 61201252; CERNET Innovation Project under Grant NGII20160207. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

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month = jan,

day = "16",

doi = "10.1109/SMC.2018.00686",

language = "English (US)",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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Zhang, P, Shu, S & Zhou, M 2019, Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616683, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 4047-4052, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 10/7/18. https://doi.org/10.1109/SMC.2018.00686

Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds. / Zhang, Peiyun; Shu, Sheng; Zhou, Mengchu.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4047-4052 8616683 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds

AU - Zhang, Peiyun

AU - Shu, Sheng

AU - Zhou, Mengchu

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China under Grants 61472005 and 61201252; CERNET Innovation Project under Grant NGII20160207. Publisher Copyright: © 2018 IEEE.

PY - 2019/1/16

Y1 - 2019/1/16

N2 - Fault detection is a crucial technology to improve the performance of cloud systems. Its fixed detection cycle tends to be problematic since it faces high overhead if a small detection cycle is used for well-performing services; while risks missing many faults if a large cycle is adopted for some poorly-performing services. To solve such problems, an algorithm for adaptively adjusting dynamic detection cycle is proposed to decrease the overhead and increase fault detection performance in a cloud environment. It shortens a detection cycle for cloud systems with large fault probability, thus boosting fault detection performance. Otherwise, it increases it, thus decreasing the overhead. The algorithm is based on the proposed detection model by using a decision tree and support vector machine to increase detection performance. Experimental results show that the method is feasible and effective in comparison with some representative methods.

AB - Fault detection is a crucial technology to improve the performance of cloud systems. Its fixed detection cycle tends to be problematic since it faces high overhead if a small detection cycle is used for well-performing services; while risks missing many faults if a large cycle is adopted for some poorly-performing services. To solve such problems, an algorithm for adaptively adjusting dynamic detection cycle is proposed to decrease the overhead and increase fault detection performance in a cloud environment. It shortens a detection cycle for cloud systems with large fault probability, thus boosting fault detection performance. Otherwise, it increases it, thus decreasing the overhead. The algorithm is based on the proposed detection model by using a decision tree and support vector machine to increase detection performance. Experimental results show that the method is feasible and effective in comparison with some representative methods.

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KW - detection model

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Zhang P, Shu S, Zhou M. Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4047-4052. 8616683. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00686

Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds

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All Science Journal Classification (ASJC) codes

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