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

Anusha, P. and Balan, R.V. Siva (2021) Efficient Power Management in Mobile Computing with Edge Server Offloading Using Multi-Objective Optimization. EAI Endorsed Transactions on Energy Web. e43. ISSN 2032-944X

[thumbnail of eai.8-7-2021.170288.pdf]
Available under License Creative Commons Attribution No Derivatives.

Download (1MB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Cloud-edge computing, Cloudlets, Fog nodes, Optimization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 20 Jul 2021 09:50
Last Modified: 20 Jul 2021 09:50
URI: https://eprints.eudl.eu/id/eprint/4879

Actions (login required)

View Item
View Item