Single-service Workflow Computational Offloading with Constrained Subtasks in The Multi-Edge Server Scenario
- Resource Type
- Conference
- Authors
- Zhang, Bingjie; Yang, Yanhong
- Source
- 2024 4th International Conference on Neural Networks, Information and Communication (NNICE) Neural Networks, Information and Communication (NNICE), 2024 4th International Conference on. :851-854 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Energy consumption
Multi-access edge computing
Costs
Computational modeling
Artificial neural networks
Servers
Game theory
Multi-access Edge Computing
Computational Offloading
Game Theory Genetic Algorithm
Single-Service Workflow
Energy Optimization
- Language
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.