Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds

Document Type

Conference Proceeding

Publication Date

7-2-2018

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.

Identifier

85062221529 (Scopus)

ISBN

[9781538666500]

Publication Title

Proceedings 2018 IEEE International Conference on Systems Man and Cybernetics Smc 2018

External Full Text Location

https://doi.org/10.1109/SMC.2018.00686

First Page

4047

Last Page

4052

Grant

NGII20160207

Fund Ref

National Natural Science Foundation of China

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