By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal serv...By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.展开更多
针对工业物联网中业务需求多样性和服务质量(Quality of Service,QoS)要求差异性导致的网络资源利用低问题,提出一种基于深度强化学习的网络切片资源分配策略。该策略运用深度强化学习优化网络切片资源分配的准入控制,通过智能体在特定...针对工业物联网中业务需求多样性和服务质量(Quality of Service,QoS)要求差异性导致的网络资源利用低问题,提出一种基于深度强化学习的网络切片资源分配策略。该策略运用深度强化学习优化网络切片资源分配的准入控制,通过智能体在特定时间窗口内处理资源请求,并根据不同网络切片的QoS要求及请求准入结果进行资源的动态分配。实验结果表明,所提策略相比基准算法在提高网络收益、资源利用率和接收率方面分别提升了8.33%、9.84%和8.57%。该策略能够在保证服务质量的同时提高整个网络的效率和性能。展开更多
空天地一体化网络作为6G技术的关键组成,在整合天基、空基和地基网络时,面临节点异构性、业务多样性等挑战,进而引发资源分配、竞争及故障风险等问题。基于此,聚焦基于软件定义网络(software defined network,SDN)与网络功能虚拟化(netw...空天地一体化网络作为6G技术的关键组成,在整合天基、空基和地基网络时,面临节点异构性、业务多样性等挑战,进而引发资源分配、竞争及故障风险等问题。基于此,聚焦基于软件定义网络(software defined network,SDN)与网络功能虚拟化(network functions virtualization,NFV)的空天地一体化网络任务部署与恢复,首先阐述了空天地一体化网络系统架构,介绍了各层网络构成、SDN和NFV原理及其相关应用,然后,针对上述挑战,以服务功能链技术为抓手,提出了面向任务的服务功能链优化部署、利用智能算法实现动态调度、通过匹配博弈算法完成失效恢复等策略,最后,构建了一个用例,设定节点部署、服务功能链建模等,验证了所提策略在提升服务功能链完成效率以及应对资源故障方面的有效性,旨在为空天地一体化网络资源管理提供理论基础。展开更多
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900600)the National Natural Science Foundation of China(61971041+2 种基金62001027)the Beijing Natural Science Foundation(M22001)the Technological Innovation Program of Beijing Institute of Technology(2022CX01027).
文摘By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.
文摘针对工业物联网中业务需求多样性和服务质量(Quality of Service,QoS)要求差异性导致的网络资源利用低问题,提出一种基于深度强化学习的网络切片资源分配策略。该策略运用深度强化学习优化网络切片资源分配的准入控制,通过智能体在特定时间窗口内处理资源请求,并根据不同网络切片的QoS要求及请求准入结果进行资源的动态分配。实验结果表明,所提策略相比基准算法在提高网络收益、资源利用率和接收率方面分别提升了8.33%、9.84%和8.57%。该策略能够在保证服务质量的同时提高整个网络的效率和性能。
文摘空天地一体化网络作为6G技术的关键组成,在整合天基、空基和地基网络时,面临节点异构性、业务多样性等挑战,进而引发资源分配、竞争及故障风险等问题。基于此,聚焦基于软件定义网络(software defined network,SDN)与网络功能虚拟化(network functions virtualization,NFV)的空天地一体化网络任务部署与恢复,首先阐述了空天地一体化网络系统架构,介绍了各层网络构成、SDN和NFV原理及其相关应用,然后,针对上述挑战,以服务功能链技术为抓手,提出了面向任务的服务功能链优化部署、利用智能算法实现动态调度、通过匹配博弈算法完成失效恢复等策略,最后,构建了一个用例,设定节点部署、服务功能链建模等,验证了所提策略在提升服务功能链完成效率以及应对资源故障方面的有效性,旨在为空天地一体化网络资源管理提供理论基础。
