Single-service Workflow Computational Offloading with Constrained Subtasks in The Multi-Edge Server Scenario

Energy consumption is one of the main costs of running MEC (Multi-access Edge Computing) systems, and single-service workflows are composed of multiple constrained subtasks that incur a significant portion of energy consumption when offloading these subtasks to different edge nodes to complete compu...

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Bibliographic Details
Published in2024 4th International Conference on Neural Networks, Information and Communication (NNICE) pp. 851 - 854
Main Authors Zhang, Bingjie, Yang, Yanhong
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.01.2024
Subjects
Artificial neural networks
Computational modeling
Computational Offloading
Costs
Energy consumption
Energy Optimization
Game theory
Game Theory Genetic Algorithm
Multi-access edge computing
Servers
Single-Service Workflow
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DOI10.1109/NNICE61279.2024.10498416

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Summary:Energy consumption is one of the main costs of running MEC (Multi-access Edge Computing) systems, and single-service workflows are composed of multiple constrained subtasks that incur a significant portion of energy consumption when offloading these subtasks to different edge nodes to complete computation. We propose an improved genetic algorithm GTGA (Game Theory Genetic Algorithm) to find an offloading scheme to minimize the energy consumption of single-service workflow computational offloading, so that each workflow in the terminal generated service workflow can achieve the goal of minimum energy consumption when calculating offloading.
DOI:10.1109/NNICE61279.2024.10498416