边缘计算使能星地协同网络下的服务部署机制研究
2021-06-28 作者:卢华,段雪飞,李斌 阅读量:
边缘计算使能星地协同网络下的服务部署机制研究 - 中兴通讯技术
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边缘计算使能星地协同网络下的服务部署机制研究

作者:卢华,段雪飞,李斌 阅读量:1018

边缘计算使能星地协同网络下的服务部署机制研究

卢华,段雪飞,李斌
(1. 广东省新一代通信与网络创新研究院,中国 广州 510663;2. 中兴通讯股份有限公司,中国 深圳 518057)

摘要:在移动边缘计算( MEC)与星地协同网络(STIN)融合的网络架构中,针对卫星网络和边缘计算对时延与资源敏感的特点,以最大化用户服务质量(QoS)为目标,提出基于强化学习的深度Q网络(DQN)算法部署机制。将部署问题描述为一个马尔可夫决策过程( MDP),并把卫星节点的状态和部署行为分别建模为DQN中的状态和动作。通过卫星的计算资源与卫星和用户的通信时延给出奖励值,在神经网络中训练以优化部署行为,进而实现最优部署策略,并对提出的算法做仿真。与其他算法对比的结果表明,在相同的优化目标条件下,DQN算法有较好的性能。  
关键词:边缘计算;服务部署;强化学习  


Service Deployment Mechanism in Edge Computing Enabled Satellite Terrestrial Integrated Network

LU Hua, DUAN Xuefei, LI Bin
(1. Guangdong Communications & Networks Institute, Guangzhou 510663, China; 2. ZTE Corporation, Shenzhen 518057, China)

Abstract: In the network architecture of mobile edge computing (MEC) and satellite terrestrial integrated network (STIN), the satellite network and edge computing are sensitive to delay and resources. To maximize user's quality of service (QoS), a deployment mechanism based on the reinforcement learning deep Q network (DQN) algorithm is proposed. The deployment problem is described as a Markov Decision Process (MDP). The state and deployment behavior of the satellite nodes are modeled as the state and action in the DQN. The reward value is given by the satellite computing resources and the communication delay between the satellite and the user. Training in the neural network to optimize the deployment behavior achieves the optimal deployment strategy. The proposed algorithm is simulated and compared with other algorithms. The result shows that under the same optimization target conditions, the DQN algorithm has better performance.
Keywords: edge computing; service deployment; reinforcement learning

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