Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization

INTRODUCTION: The internet of mobile things is subjected to execute on data centers such as cloudlet, cloud servers and also on devices; it solves the problem of multi-objective optimization and tries to discover active scheduling with low energy consumption, execution time and cost. OBJECTIVES: To...

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Autores principales: P. Anusha, R.V. Balan
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Publicado: European Alliance for Innovation (EAI) 2022
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Acceso en línea:https://doaj.org/article/1ad929f473794ba3928e736e5d029321
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spelling oai:doaj.org-article:1ad929f473794ba3928e736e5d0293212021-11-30T11:07:32ZEfficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization2032-944X10.4108/eai.8-7-2021.170288https://doaj.org/article/1ad929f473794ba3928e736e5d0293212022-01-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.8-7-2021.170288https://doaj.org/toc/2032-944XINTRODUCTION: The internet of mobile things is subjected to execute on data centers such as cloudlet, cloud servers and also on devices; it solves the problem of multi-objective optimization and tries to discover active scheduling with low energy consumption, execution time and cost. OBJECTIVES: To alleviate the conflicts between the support constraint of ‘smart phones and customers' requests of diminishing idleness as well as extending battery life, it spikes a well-known wave of offloading portable application for execution to brought together server farms, for example, haze hubs and cloud workers.METHODS: The test to develop the methodology for mobile phones, with enhanced IoT execution in cloud-edge registering. Then, to assess the feasibility of our proposed process, tests and simulations are carried out.RESULTS: The simulator is used to test the algorithm, and the outcomes show that our calculations can lesser over 18% energy utilization.CONCLUSION: The optimization approaches using PSO and GA based on simulation data, with the standard genetic algorithm providing the highest overall value for mission offloading in fog nodes using multi-objectives. With the assumption of various workflow models as single and multi-objective in data centers as cloud servers, fog nodes, and within computers, we extracted the analytic results of energy usage, delay efficiency, and cost. Then formulated the multi-objective problem with different constraints and solved it using various scheduling algorithms based on the obtained data.P. AnushaR.V. BalanEuropean Alliance for Innovation (EAI)articlecloud-edge computingcloudletsfog nodesoptimizationScienceQMathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENEAI Endorsed Transactions on Energy Web, Vol 9, Iss 37 (2022)
institution DOAJ
collection DOAJ
language EN
topic cloud-edge computing
cloudlets
fog nodes
optimization
Science
Q
Mathematics
QA1-939
Electronic computers. Computer science
QA75.5-76.95
spellingShingle cloud-edge computing
cloudlets
fog nodes
optimization
Science
Q
Mathematics
QA1-939
Electronic computers. Computer science
QA75.5-76.95
P. Anusha
R.V. Balan
Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization
description INTRODUCTION: The internet of mobile things is subjected to execute on data centers such as cloudlet, cloud servers and also on devices; it solves the problem of multi-objective optimization and tries to discover active scheduling with low energy consumption, execution time and cost. OBJECTIVES: To alleviate the conflicts between the support constraint of ‘smart phones and customers' requests of diminishing idleness as well as extending battery life, it spikes a well-known wave of offloading portable application for execution to brought together server farms, for example, haze hubs and cloud workers.METHODS: The test to develop the methodology for mobile phones, with enhanced IoT execution in cloud-edge registering. Then, to assess the feasibility of our proposed process, tests and simulations are carried out.RESULTS: The simulator is used to test the algorithm, and the outcomes show that our calculations can lesser over 18% energy utilization.CONCLUSION: The optimization approaches using PSO and GA based on simulation data, with the standard genetic algorithm providing the highest overall value for mission offloading in fog nodes using multi-objectives. With the assumption of various workflow models as single and multi-objective in data centers as cloud servers, fog nodes, and within computers, we extracted the analytic results of energy usage, delay efficiency, and cost. Then formulated the multi-objective problem with different constraints and solved it using various scheduling algorithms based on the obtained data.
format article
author P. Anusha
R.V. Balan
author_facet P. Anusha
R.V. Balan
author_sort P. Anusha
title Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization
title_short Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization
title_full Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization
title_fullStr Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization
title_full_unstemmed Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization
title_sort efficient power management in mobile computing with edge server offloading using multi-objective optimization
publisher European Alliance for Innovation (EAI)
publishDate 2022
url https://doaj.org/article/1ad929f473794ba3928e736e5d029321
work_keys_str_mv AT panusha efficientpowermanagementinmobilecomputingwithedgeserveroffloadingusingmultiobjectiveoptimization
AT rvbalan efficientpowermanagementinmobilecomputingwithedgeserveroffloadingusingmultiobjectiveoptimization
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