In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the
computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the
computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
%0 Journal Article
%1 noauthororeditor
%A Nancy Arya, Sunita Choudhary
%A S.Taruna,
%D 2019
%J International Journal of Computer Networks & Communications (IJCNC)
%K Cloud Computational Computing, Dynamic Energy Mobile Offloading, Optimization. Task
%N 5
%P 59-78
%R 10.5121/ijcnc.2019.11504
%T ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENT
%U http://aircconline.com/ijcnc/V11N5/11519cnc04.pdf
%V 11
%X In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the
computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the
computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
@article{noauthororeditor,
abstract = {In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the
computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the
computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.},
added-at = {2019-10-24T11:09:32.000+0200},
author = {Nancy Arya, Sunita Choudhary and S.Taruna},
biburl = {https://www.bibsonomy.org/bibtex/285b4175895d5cfa82d3fa9114f377d09/laimbee},
doi = {10.5121/ijcnc.2019.11504},
interhash = {869ffae3687b94c0dc384a1c040f9735},
intrahash = {85b4175895d5cfa82d3fa9114f377d09},
issn = {ISSN 0974 - 9322 (Online) ; 0975 - 2293 (Print)},
journal = {International Journal of Computer Networks & Communications (IJCNC) },
keywords = {Cloud Computational Computing, Dynamic Energy Mobile Offloading, Optimization. Task},
month = {September},
number = 5,
pages = {59-78},
timestamp = {2019-10-24T11:09:32.000+0200},
title = {ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENT},
url = {http://aircconline.com/ijcnc/V11N5/11519cnc04.pdf},
volume = 11,
year = 2019
}