Jiajia Liu1,*, Peng Xie2, Wei Li2, Bo Tang2, Jianhua Liu2
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2609-2635, 2025, DOI:10.32604/cmc.2024.058810
- 17 February 2025
Abstract 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… More >
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