In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to im...In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.展开更多
Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers ...Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.展开更多
针对车辆边缘计算(Vehicular Edge Computing,VEC)卸载与资源分配过程中由于边缘服务器资源受限导致的时延增大的问题,提出一种基于深度强化学习的计算卸载与资源分配(Compute Offload and Resource Allocation Based on Deep Q-Netwrk,...针对车辆边缘计算(Vehicular Edge Computing,VEC)卸载与资源分配过程中由于边缘服务器资源受限导致的时延增大的问题,提出一种基于深度强化学习的计算卸载与资源分配(Compute Offload and Resource Allocation Based on Deep Q-Netwrk,CORADQN)算法。构建VEC网络架构,通过拆分计算密集型车载任务及利用空闲服务车辆的计算资源,将计算任务分别卸载至边缘服务器、空闲服务车辆和本地车辆进行处理,以降低VEC网络系统的总时延。将计算卸载与资源分配转化为多约束优化问题,并将平均奖励作为样本的优先级进行采样,从而提高样本的利用率,加快算法收敛速度。仿真结果表明,相较于完全本地(ALL-Local)算法、完全边缘(ALL-Edge)算法、联邦卸载(Federated Offloading Scheme,FOS)算法及深度Q学习(Deep Q-Network,DQN)算法,所提算法能够最小化VEC网络的系统时延。展开更多
基金supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No.2021R1C1C1013133)supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP)grant funded by the Korea Government (MSIT) (RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for The Smart City)supported by the Soonchunhyang University Research Fund.
文摘In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.
文摘Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.
文摘针对车辆边缘计算(Vehicular Edge Computing,VEC)卸载与资源分配过程中由于边缘服务器资源受限导致的时延增大的问题,提出一种基于深度强化学习的计算卸载与资源分配(Compute Offload and Resource Allocation Based on Deep Q-Netwrk,CORADQN)算法。构建VEC网络架构,通过拆分计算密集型车载任务及利用空闲服务车辆的计算资源,将计算任务分别卸载至边缘服务器、空闲服务车辆和本地车辆进行处理,以降低VEC网络系统的总时延。将计算卸载与资源分配转化为多约束优化问题,并将平均奖励作为样本的优先级进行采样,从而提高样本的利用率,加快算法收敛速度。仿真结果表明,相较于完全本地(ALL-Local)算法、完全边缘(ALL-Edge)算法、联邦卸载(Federated Offloading Scheme,FOS)算法及深度Q学习(Deep Q-Network,DQN)算法,所提算法能够最小化VEC网络的系统时延。
