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: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
European Alliance for Innovation (EAI)
2022
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| Acceso en línea: | https://doaj.org/article/1ad929f473794ba3928e736e5d029321 |
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| Sumario: | 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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