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Early fault detection in IaaS cloud computing based on fuzzy logic and prediction technique

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Abstract

Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (IaaS) cloud computing is a challenge due to the complexity of service providing. By definition, the availability can be maintained by coupling with the fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them adequately consider the fault detection aspect, which is critical to issue the appropriate recovery actions just in time. In this paper, based on a rigorous analysis on the nature of failures, we would like to introduce a method to early identify the faults occurring in the IaaS system. By engaging fuzzy logic algorithm and prediction technique, the proposed approach can provide better performance in terms of accuracy and reaction rate, which subsequently enhances the system reliability.

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Acknowledgements

This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2014R1A2A2A01003914 and the Industrial Core Technology Development Program (10049079, Development of mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea). This work is also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2011-0030079). Corresponding author is Prof. Sungyoung Lee.

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Authors and Affiliations

  1. Computer Engineering Department, Kyung Hee University, Suwon, 446-701, Korea

    Dinh-Mao Bui, Thien Huynh-The & Sungyoung Lee

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  1. Dinh-Mao Bui
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  2. Thien Huynh-The
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  3. Sungyoung Lee
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Correspondence to Sungyoung Lee.

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Bui, DM., Huynh-The, T. & Lee, S. Early fault detection in IaaS cloud computing based on fuzzy logic and prediction technique. J Supercomput 74, 5730–5745 (2018). https://doi.org/10.1007/s11227-017-2053-3

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