DRL-Based Dependent Task Offloading Strategies with Multi-Server Collaboration in Multi-Access Edge Computing
نویسندگان
چکیده
Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output some is input others. Most existing research on computational offloading does not consider dependency task and uses convex relaxation or heuristic algorithms to solve problem, which lacks adaptability suitable for dynamic environment fast fading channels. Therefore, this paper, optimization problem modeled as a Markov Decision Process (MDP) multi-user multi-server MEC environments, dependent are represented by Directed Acyclic Graph (DAG). Combined with Soft Actor–Critic (SAC) algorithm Deep Reinforcement Learning (DRL) theory, an intelligent scheme proposed. Under condition resource constraint, each can be offloaded corresponding server through centralized control, greatly reduces service delay terminal energy consumption. The experimental results show that converges quickly stably, its effect better than methods, verifies effectiveness algorithm.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13010191