.png){kind=icon}
返回
An Energy-Efficient Dynamic Offloading Algorithm for Edge Computing Based on Deep Reinforcement Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3452190.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Mobile edge computing (MEC) represents a promising computing paradigm within Artificial Intelligence Generated Content (AIGC), offering users instant, customized, and personalized services. However, with the continued growth of the AIGC user base and the expansion of service demands, edge computing nodes are required to handle an increasing number and complexity of offloading tasks, leading to significant energy consumption issues in edge systems. This paper introduces a distributed task offloading framework (EE-A2C) that utilizes the Advantage Actor-Critic algorithm to enhance energy efficiency in edge cloud environments. This framework facilitates distributed interactions among multiple agents and edge environments with communication queues, aiming to minimize average energy consumption and latency for AIGC users. Secondly, to achieve adaptive and efficient task offloading decisions, we have developed a complex reward-sharing model based on latency and energy consumption. Finally, we have also incorporated LSTM to enhance the model's ability to capture critical information about energy consumption, thereby promoting more robust decision-making. Compared to eight state-of-the-art energy-saving algorithms, the proposed EE-A2C framework more effectively utilizes the computing power of edge nodes, significantly reduces average energy consumption and latency, and enhances the energy efficiency of the edge cloud system.
Keyword:
MEC
deep reinforcement learning
edge computing
energy efficiency
task offloading
MEC
deep reinforcement learning
edge computing
energy efficiency
task offloading
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks用于无线供电的移动边缘计算网络中在线计算卸载的深度强化学习
OAAI
OAAI分享收藏
查看更多内容