Efficient and Reliable Data Recovery Technique in Cloud Computing
Internet of Things and Cloud Computing
Volume 5, Issue 5-1, September 2017, Pages: 13-18
Received: Jul. 4, 2017; Accepted: Jul. 5, 2017; Published: Aug. 25, 2017
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Praveen S. Challagidad, Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot, India
Ambika S. Dalawai, Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot, India
Mahantesh N. Birje, Center for Post Graduate Studies, Visvesvaraya Technological University (VTU), Belagavi, India
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Cloud computing provides accessing of any kind of services dynamically over Internet on demand basis. One of the most significant service that is being provided is storage as a service. Cloud customer can store any amount of data into cloud storage results to huge amount of data at the datacenter. The data may get deleted by man-made disaster (either CSP or customer itself without their knowledge) or by natural disasters (either earth quakes or volcanoes) from the datacenters. Nowadays, data has been generated in large quantity that requires the data recovery services or techniques. Therefore there is a requirement for designing an efficient data recovery technique to recover the lost data. Many researchers have proposed different data recovery techniques but they lack in efficiency and reliability. In this paper, a multi-server system based on Enriched Genetic Algorithm to recover the lost data by using four cloud backup servers is discussed. To achieve reliability the proposed technique provides the flexibility for the user to collect information from any backup server when main cloud server loses its data and is unable to provide data to users.
Cloud Computing, Data Recovery, Backup, Data Restore
To cite this article
Praveen S. Challagidad, Ambika S. Dalawai, Mahantesh N. Birje, Efficient and Reliable Data Recovery Technique in Cloud Computing, Internet of Things and Cloud Computing. Special Issue: Advances in Cloud and Internet of Things. Vol. 5, No. 5-1, 2017, pp. 13-18. doi: 10.11648/j.iotcc.s.2017050501.13
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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