移动边缘计算(Mobile Edge Computing,MEC)是一种利用靠近移动设备的边缘节点提供的计算能力,来提升性能的前沿技术。现有的一些先进的计算卸载方法,已能够支持在MEC环境中基于函数粒度进行动态卸载。函数即服务(Function as a Service,...移动边缘计算(Mobile Edge Computing,MEC)是一种利用靠近移动设备的边缘节点提供的计算能力,来提升性能的前沿技术。现有的一些先进的计算卸载方法,已能够支持在MEC环境中基于函数粒度进行动态卸载。函数即服务(Function as a Service,FaaS)作为无服务架构的一种经典范式,提供了一种在函数粒度上构建和拓展应用程序的新方式。相比传统的方式,FaaS提供了理想的资源弹性。OpenFaaS作为当下流行的开源FaaS项目,为FaaS平台的搭建提供了良好的基础。将先进的计算卸载方法与FaaS解决方案(OpenFaaS)进行整合,是有意义且具有挑战的。为此,文中设计并实现了一个基于OpenFaaS的多边缘管理框架,该框架实现了对多个边缘上OpenFaaS的搭建与状态管理。同时,对于需要部署的函数,将其重构并部署到OpenFaaS上,在运行时能够灵活地在多个OpenFaaS间调度函数执行。针对5个实际的Java智能应用对该框架进行了评估,结果表明该框架可以有效管理多个边缘,且与本地运行相比,该框架平均可节省10.49%~49.36%的响应时间。展开更多
Machine-type communication (MTC) devices provide a broad range of data collection especially on the massive data generated environments such as urban, industrials and event-enabled areas. In dense deployments, the dat...Machine-type communication (MTC) devices provide a broad range of data collection especially on the massive data generated environments such as urban, industrials and event-enabled areas. In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. With the limitation of computational resources at the centralized aggregator, some grouped MTC devices data offloaded to the nearby base station collocated with the mobile edge-computing server. As a sensing capability adopted on MTC devices, we use a power exponential function model to compute a correlation coefficient existing between the MTC devices. Based on this framework, we compare the energy consumption when all data processed locally at centralized aggregator or offloaded at mobile edge computing server with optimal solution obtained by the brute force method. Then, the simulation results revealed that the proposed k-means grouping technique reduce the energy consumption at centralized aggregator while satisfying the required completion time.展开更多
能源互联网是以固有电力系统为基础,结合大量智能终端、传感设备、云数据中心等连接构成的多层次网络系统。随着第五代移动通信技术(5th Generation Mobile Networks,5G)与能源互联网建设的快速发展,网络边缘设备数量呈现爆炸式增长。...能源互联网是以固有电力系统为基础,结合大量智能终端、传感设备、云数据中心等连接构成的多层次网络系统。随着第五代移动通信技术(5th Generation Mobile Networks,5G)与能源互联网建设的快速发展,网络边缘设备数量呈现爆炸式增长。然而终端设备处理能力不足、资源有限等问题使得海量细粒度的用户侧数据无法得到有效应用。针对目前电力系统云计算模式中存在的诸如充分实现高带宽下的低延迟等问题,研究基于能源互联网的边缘计算系统,对其网络功能模块进行研究设计,使其能够合理调配电网中边缘节点资源进行计算卸载,以保障终端用户的服务质量(Quality of Service,QoS)。设计了一种基于Stackelberg博弈的卸载决策方法,将边缘云与用户终端分别视为博弈领导者和跟随者,证明该博弈存在使得效用最优的纳什均衡解。通过仿真分析,证明该卸载决策方法具有更优的性能。展开更多
文摘移动边缘计算(Mobile Edge Computing,MEC)是一种利用靠近移动设备的边缘节点提供的计算能力,来提升性能的前沿技术。现有的一些先进的计算卸载方法,已能够支持在MEC环境中基于函数粒度进行动态卸载。函数即服务(Function as a Service,FaaS)作为无服务架构的一种经典范式,提供了一种在函数粒度上构建和拓展应用程序的新方式。相比传统的方式,FaaS提供了理想的资源弹性。OpenFaaS作为当下流行的开源FaaS项目,为FaaS平台的搭建提供了良好的基础。将先进的计算卸载方法与FaaS解决方案(OpenFaaS)进行整合,是有意义且具有挑战的。为此,文中设计并实现了一个基于OpenFaaS的多边缘管理框架,该框架实现了对多个边缘上OpenFaaS的搭建与状态管理。同时,对于需要部署的函数,将其重构并部署到OpenFaaS上,在运行时能够灵活地在多个OpenFaaS间调度函数执行。针对5个实际的Java智能应用对该框架进行了评估,结果表明该框架可以有效管理多个边缘,且与本地运行相比,该框架平均可节省10.49%~49.36%的响应时间。
文摘Machine-type communication (MTC) devices provide a broad range of data collection especially on the massive data generated environments such as urban, industrials and event-enabled areas. In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. With the limitation of computational resources at the centralized aggregator, some grouped MTC devices data offloaded to the nearby base station collocated with the mobile edge-computing server. As a sensing capability adopted on MTC devices, we use a power exponential function model to compute a correlation coefficient existing between the MTC devices. Based on this framework, we compare the energy consumption when all data processed locally at centralized aggregator or offloaded at mobile edge computing server with optimal solution obtained by the brute force method. Then, the simulation results revealed that the proposed k-means grouping technique reduce the energy consumption at centralized aggregator while satisfying the required completion time.
文摘能源互联网是以固有电力系统为基础,结合大量智能终端、传感设备、云数据中心等连接构成的多层次网络系统。随着第五代移动通信技术(5th Generation Mobile Networks,5G)与能源互联网建设的快速发展,网络边缘设备数量呈现爆炸式增长。然而终端设备处理能力不足、资源有限等问题使得海量细粒度的用户侧数据无法得到有效应用。针对目前电力系统云计算模式中存在的诸如充分实现高带宽下的低延迟等问题,研究基于能源互联网的边缘计算系统,对其网络功能模块进行研究设计,使其能够合理调配电网中边缘节点资源进行计算卸载,以保障终端用户的服务质量(Quality of Service,QoS)。设计了一种基于Stackelberg博弈的卸载决策方法,将边缘云与用户终端分别视为博弈领导者和跟随者,证明该博弈存在使得效用最优的纳什均衡解。通过仿真分析,证明该卸载决策方法具有更优的性能。
