摘要
随着网络技术的不断发展,基于Fat-Tree的网络拓扑结构分布式网络控制模式逐渐显露出其局限性,软件定义数据中心网络(software-defined data center network,SDCN)技术作为Fat-Tree网络拓扑的改进技术,受到越来越多研究者的关注。首先搭建了一个SDCN中的边缘计算架构和基于移动边缘计算(mobileedge computing,MEC)平台三层服务架构的任务卸载模型,结合移动边缘计算平台的实际应用场景,利用同策略经验回放和熵正则改进传统的深度Q网络(deep Q-leaning network,DQN)算法,优化了MEC平台的任务卸载策略,并设计了实验对基于同策略经验回放和熵正则的改进深度Q网络算法(improved DQN algorithm based on same strategy empirical playback and entropy regularization,RSS2E-DQN)和其他3种算法在负载均衡、能耗、时延、网络使用量几个方面进行对比分析,验证了改进算法在上述4个方面具有更优越的性能。
With the continuous development of network technology,the network topology distributed network control mode based on Fat-Tree gradually reveals its limitations.Software-defined data center network(SDCN)technology,as an improved technology of Fat-Tree network topology,has attracted more and more researchers’attention.Firstly,an edge computing architecture in SDCN and a task offloading model based on the three-layer service architecture of the mobile edge computing(MEC)platform were built,combined with the actual application scenarios of the MEC platform.Through the same strategy experience playback and entropy regularization,the traditional deep Q-leaning network(DQN)algorithm was improved,and the task offloading strategy of MEC platform was optimized.An im-proved DQN algorithm based on same strategy empirical playback and entropy regularization(RSS2E-DQN)was compared with three other algorithms in load balancing,energy consumption,delay and network usage.It is verified that the improved algorithm has better performance in the above four aspects.
作者
蒋守花
王以伍
JIANG Shouhua;WANG Yiwu(Modern Education Technology Center,Chengdu Medical College,Chengdu 610500,China)
出处
《电信科学》
北大核心
2024年第2期96-106,共11页
Telecommunications Science
基金
四川省高等学校人文社会科学重点研究基地·四川省教育信息化应用与发展研究中心项目(No.JYXX23-002)
成都医学院校基金科研项目(No.CYSYB23-02)。
关键词
软件定义数据中心网络
深度强化学习
边缘计算任务卸载
同策略经验回放
熵正则
software-defined data center network
deep reinforcement learning
edge computing task offloading
replay the same strategy experience
entropy regularity
