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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European Alliance for Innovation (EAI)
2022
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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) |
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cloud-edge computing cloudlets fog nodes optimization Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 |
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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 |
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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. |
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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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