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Literature Review on Energy Minimization with Effective Data Partitioning for Fail Safe Cloud Storage and Computation

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Zareena Sameer, Anoop S.
10.5120/ijca2016908198

Zareena Sameer and Anoop S.. Article: Literature Review on Energy Minimization with Effective Data Partitioning for Fail Safe Cloud Storage and Computation. International Journal of Computer Applications 133(16):1-3, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Zareena Sameer and Anoop S.},
	title = {Article: Literature Review on Energy Minimization with Effective Data Partitioning for Fail Safe Cloud Storage and Computation},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {16},
	pages = {1-3},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Mobile Cloud Computing is an inheritance and emergence of cloud computing and mobile computing.The ever increasing usage of mobile devices and the need for users to handle applications that require high computation power has lead to the emergence of mobile cloud computing.The full potential of a mobile cloud can be realized only if its inherent hurdles such as resource scarcity, mobility and node failures can be overcome.Even then, in the case of dynamic networks, the challenges of reliability and energy efficiency remain largely unaddressed.These two challenges are addressed in a combined manner by using k-out of n computing.In the proposed scheme the mobile devices retrieve or process data in the most energy efficient and fail safe method by using effective data partitioning in the cloud and k-out of n servers.The n servers are chosen based on their topology and fault tolerance of the nodes.The k-out of n computing ensures that a system of n components operate correctly as long as k or more components work.

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Keywords

Energy-Efficient Computing, Fault-Tolerant Computing