Computation Offloading in Hand-Held Devices Using Ternary Decision Maker in Accountance with Time and Energy

Chourasiya, Neelesh L.; Singh, Tanu Preet

doi:10.1007/978-981-10-8198-9_62

Cited by 1 publication

(1 citation statement)

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“…Here channel is gained by using priority function. Chourasiya and Singh (2018) proposed novel scheme Ternary Decision Maker (TDM) to select appropriate target device either phone or cloud for offloading process in order to obtain reduced computing power in mobile devices.…”

Section: Review Of Literaturementioning

confidence: 99%

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

Veeraiyan¹,

Kousika²,

Senthilkumar³

et al. 2020

Journal of Computer Science

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Offloading the execution of heavy computational modules from mobile devices to Mobile Cloud Computing (MCC) is inevitable in today's era as it mainly focuses in consuming less battery power and execution time. But, the problem incurred with identifying the most optimal cloud device to map each module still remains a challenge in cloud computing environment. In this paper, a novel MCC offloading framework is proposed to fasten the allocation and execution of high computational modules that runs in the mobile device, effectively on the cloud. The framework employs K-Means clustering algorithm to group the nearest cloud virtual machines that best suits for executing modules of software running in the mobile. The objective of the paper is to maximize the energy savings by extending the battery life and execution speed of mobile device when executing heavy computational modules. The optimal selection of cloud device is attained by grouping the requirements of each module with the nearest cloud devices offering the same requirements using K-Means Algorithm. The proposed framework is compared with the existing mobile computation offloading frameworks with respect to energy saving, execution time and energy consumption. The results show that the proposed work executes the modules of computationally intensive modules in minimum time span with maximized energy savings than the existing frameworks.

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Section: Review Of Literaturementioning

confidence: 99%