.png){kind=icon}
返回
A Novel Hybrid-ARPPO Algorithm for Dynamic Computation Offloading in Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2022.3188928.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Applications consisting of a group of modular tasks can be offloaded to the multiaccess edge computing (MEC) for lower delay and energy consumption. In a dynamic MEC system, the fine-grained cooperative and dynamic offloading solution is necessary for the scenario of reusing tasks among devices. Considering the transmission cooperation, shared wireless bandwidth, and changing task queues on devices and edge servers, we formulate a joint offloading optimization problem to minimize the long-term average task execution cost. Although deep reinforcement learning (DRL) is a popular method for the dynamic problem, existing DRL algorithms are not suitable for our problem because of the hybrid discrete-continuous action spaces and constraints among action dimensions. Therefore, we propose a hybrid average reward proximal policy optimization (hybrid-ARPPO) algorithm to jointly optimize the offloading decisions, cooperative transmission ratios, and edge server assignments. First, we decompose our offloading problem into two subproblems. One is a tractable linear programming problem for continuous transmission ratios, and the other is a Markov decision process (MDP) only with discrete actions for offloading decisions and server assignments. Second, we take the expected average reward as the performance measure and deprecate the discount factor, which can reduce the work of tuning algorithms. Third, we design an action mask layer in the policy network of hybrid-ARPPO to filter invalid actions. Extensive experiments show the effectiveness of our hybrid-ARPPO in different system scales and task arrival patterns.
Keyword:
Computation offloading
deep reinforcement learning (DRL)
multiaccess edge computing (MEC)
reusable tasks
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge ComputingD2D-Enabled移动-边缘计算联合任务分配与资源分配
OAAI
OAAI分享收藏Optimal Energy Efficiency With Delay Constraints for Multi-Layer Cooperative Fog Computing NetworksOAAI
OAAI分享收藏Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading智能边缘: 利用深度模仿学习实现移动边缘计算卸载
OAAI
OAAI分享收藏Nucleolar changes after microinjection of antibodies to RNA polymerase I into the nucleus of mammalian cells.png)
ChromosomaOAAI
ChromosomaIF0
OAAI分享收藏Deep Reinforcement Learning for Energy-Efficient Computation Offloading in Mobile-Edge Computing面向移动边缘计算节能计算卸载的深度强化学习
PREAI
分享收藏
查看更多内容