International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.11(16) (2020)

  • A Review on the Tools and Techniques for Effective Failure Detection and Prediction in Cloud Computing

    Saleh M. Alqahtani, Hamza Arishi (College of Computing and Informatics, Saudi Electronic University, SAUDI ARABIA).

    Disciplinary: Computer and Information Sciences, Digital Business Management and Service.

    ➤ FullText

    DOI: 10.14456/ITJEMAST.2020.315

    Keywords: Cloud service; Proactive fault tolerance; Reactive fault tolerance; Cloud computing threat; Cloud computing risks; Cloud computing vulnerabilities, Cloud fault detection.

    Abstract
    Cloud Computing is a model that supports services and computer resources like computing power, storage, and bandwidth to deliver IT services in the organization. With the increasing usage of the Internet and mobile devices, cloud computing has also become a widely adopted model of delivering services through the Internet. Cloud computing is a combination of self-service and automatic computing. The main problem is the complexity of detecting a fault in a cloud-based system due to its large-scale integration and dynamic nature of the cloud. In this paper, we discuss cloud computing in the light of failure detection and prevention to build a reliable cloud-based system that can detect and prevent failures before exploitation.

    Paper ID: 11A16F

    Cite this article:

    Alqahtani, S.M., Arishi, H.(2020). A Review on the Tools and Techniques for Effective Failure Detection and Prediction in Cloud Computing. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(16), 11A16F, 1-10. http://doi.org/10.14456/ITJEMAST.2020.315



References:

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Gao, W., Alqahtani, A. S., Mubarakali, A., & Mavaluru, D. (2019). Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Engineering with Computers, 1-8.

Gill, S. S., & Buyya, R. (2018). Failure management for reliable cloud computing: A taxonomy, model, and future directions. Computing in Science & Engineering.

Moges, F. F., & Abebe, S. L. (2019). Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework. Journal of Cloud Computing, 8(1), 2.

Pannu, H. S., Liu, J., Guan, Q., & Fu, S. (2012). AFD: Adaptive failure detection system for cloud computing infrastructures. In 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC) (pp. 71-80).

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Shihab, L. A. (2020). Technological Tools for Data Security in the Treatment of Data Reliability in Big Data Environments. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(9), 11A9M, 1-13.

Suryateja, P. S. (2018). Threats and vulnerabilities of cloud computing: A review. International Journal of Computer Sciences and Engineering, 6(3), 297-302.

Talia, D. (2019). A view of programming scalable data analysis: from clouds to exascale. Journal of Cloud Computing, 8(1), 4.

Tchernykh, A., Schwiegelsohn, U., Talbi, E. G., & Babenko, M. (2019). Towards understanding uncertainty in cloud computing with risks of confidentiality, integrity, and availability. Journal of Computational Science, 36, 100581.



Other issues:
Vol.11(16)(2020)
Vol.11(15)(2020)
Vol.11(14)(2020)
Vol.11(13)(2020)
Vol.11(12)(2020)
Vol.11(11)(2020)
Vol.11(10)(2020)
Vol.11(9)(2020)
Vol.11(8)(2020)
Vol.11(7)(2020)
Vol.11(6)(2020)
Vol.11(5)(2020)
Vol.11(4)(2020)
Vol.11(3)(2020)
Vol.11(2)(2020)
Vol.11(1)(2020)
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