Dynamic Task Offloading Scheme for Edge Computing via Meta-Reinforcement Learning  

作　　者：Jiajia Liu Peng Xie Wei Li Bo Tang Jianhua Liu 

机构地区：[1]Teacher Development and Teaching Evaluation Center,Civil Aviation Flight University of China,Guanghan,618307,China [2]Institute of Electronics and Electrical Engineering,Civil Aviation Flight University of China,Guanghan,618307,China

出　　处：《Computers, Materials & Continua》2025年第2期2609-2635,共27页计算机、材料和连续体(英文)

基　　金：funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).

摘　　要：As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.

关 键 词：Edge computing adaptive META task offloading joint optimization 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]