.png){kind=icon}
返回
Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization
.png){kind=icon}
OA
AI
DOI：10.26599/TST.2023.9010058.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Edge computing nodes undertake an increasing number of tasks with the rise of business density. Therefore, how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge. This study proposes an edge task scheduling approach based on an improved Double Deep Q Network (DQN), which is adopted to separate the calculations of target Q values and the selection of the action in two networks. A new reward function is designed, and a control unit is added to the experience replay unit of the agent. The management of experience data are also modified to fully utilize its value and improve learning efficiency. Reinforcement learning agents usually learn from an ignorant state, which is inefficient. As such, this study proposes a novel particle swarm optimization algorithm with an improved fitness function, which can generate optimal solutions for task scheduling. These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level. The proposed algorithm is compared with six other methods in simulation experiments. Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.
Keyword:
edge computing
task scheduling
reinforcement learning
makespan
Double Deep Q Network (DQN)
期刊
T
IF:
3.5
论文数:
987
被引数:
2.5K
机构
引用论文
.png){kind=icon}
Representation and Reinforcement Learning for Task Scheduling in Edge Computing边缘计算中任务调度的表示和强化学习
PREAI
分享收藏Prosumer in smart grids based on intelligent edge computing: A review on Artificial Intelligence Scheduling Techniques基于智能边缘计算的智能电网中消费者: 人工智能调度技术综述
OAAI
OAAI分享收藏GARLSched: Generative adversarial deep reinforcement learning task scheduling optimization for large-scale high performance computing systemsGARLSched: 大规模高性能计算系统的生成对抗深度强化学习任务调度优化
PREAI
分享收藏分享收藏CoPace: Edge Computation Offloading and Caching for Self-Driving With Deep Reinforcement LearningOAAI
OAAI分享收藏
查看更多内容