This paper is published in Volume-4, Issue-5, 2018
Area
Cloud computing, Fault tolerance, Machine learning
Author
Abhishek Gupta
Co-authors
Aashish Mamgain
Org/Univ
Bharati Vidyapeeth's College of Engineering, New Delhi, Delhi, India
Pub. Date
07 September, 2018
Paper ID
V4I5-1171
Publisher
Keywords
Fault tolerance, Faulty node, Cloud computing, Proactive technique, Machine learning, NaïVe bayes

Citationsacebook

IEEE
Abhishek Gupta, Aashish Mamgain. Machine learning based approach for fault tolerance in cloud computing, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Abhishek Gupta, Aashish Mamgain (2018). Machine learning based approach for fault tolerance in cloud computing. International Journal of Advance Research, Ideas and Innovations in Technology, 4(5) www.IJARIIT.com.

MLA
Abhishek Gupta, Aashish Mamgain. "Machine learning based approach for fault tolerance in cloud computing." International Journal of Advance Research, Ideas and Innovations in Technology 4.5 (2018). www.IJARIIT.com.

Abstract

Cloud computing is a rising area that is currently engaged towards many of the IT industries. Bearing with the cloud architecture is the difficult challenge for us. This challenge can be achievable by fault tolerance and monitoring. Methods/Statistical Analysis: Fault Tolerance (FT) facilitates the process or the component to work smoothly even though in the occurrence of the failure. Monitoring is a procedure in which the fault is predicted before it occurs. Proactive and reactive measures can take place to run the cloud environment with tolerance in failure occurrence. Reviewing the potential of FT and monitoring services is to make out the technique that serves in certain purpose. Fault tolerance systems are important for both providers of cloud services and customers. Findings: Based on this idea, this paper provides the different or diverse fault tolerance and monitoring mechanism to improve the reliability in a cloud environment. Applications/Improvements: It presents the information about the various techniques and methods used in the FT and also a future research direction in cloud FT.
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