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
With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the netw...With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.展开更多
针对如何在部署服务功能链SFC(service function chain)的同时兼顾低能耗与网络负载均衡,提出了一种以节点负载状态预测为基础的SFC部署方法NIR-IACA(improved ant colony algorithm based on node importance ranking)。首先,使用基于...针对如何在部署服务功能链SFC(service function chain)的同时兼顾低能耗与网络负载均衡,提出了一种以节点负载状态预测为基础的SFC部署方法NIR-IACA(improved ant colony algorithm based on node importance ranking)。首先,使用基于粒子群优化的CNN-GRU模型(particle swarm optimization-based CNN-GRU model,PCNN-GRU),结合广义网络温度(GNT)预测网络节点的负载状态,并据此为SFC部署提供备选节点;其次,基于最短路径优先策略的改进蚁群算法(ant colony algorithm,ACA)设计SFC部署节点选择策略(high availability and resource scheduling,HARS)且对选定节点进行虚拟链路映射,优化目标兼顾基础设施网络低能耗与负载均衡的要求。基于Clearwater VNF公开数据集的实验结果表明,提出的NIR-IACA方法与现有的MC-EEVP算法、DPVC算法以及RQAP算法相比平均节省13.09%的能耗,并提高12.98%的负载均衡能力,且在维持相对较高SFC请求的接受率的同时,可以较好地实现SFC部署的能耗与负载均衡联合优化。展开更多
软件定义网络(Software Defined Network,SDN)和网络功能虚拟化(Network Function Virtualization,NFV)已经成为新一代网络体系结构的新范式。SDN和NFV可以有效地提高部署和管理服务功能链(Service Function Chains,SFCs)的灵活性。文...软件定义网络(Software Defined Network,SDN)和网络功能虚拟化(Network Function Virtualization,NFV)已经成为新一代网络体系结构的新范式。SDN和NFV可以有效地提高部署和管理服务功能链(Service Function Chains,SFCs)的灵活性。文中融合SDN和NFV,并应用到网络切片的资源优化中,为未来的5G三大业务场景实现定制服务的有效部署,提出了一种基于SDN和NFV的网络切片资源优化算法。首先针对5G uRLLC,eMBB,mMTC三大应用场景的不同业务需求,将底层的物理节点按照功能类型划分为三个虚拟子网的节点集合。然后根据不同应用场景的不同服务功能链的要求,分别对每一类的网络切片进行建模,形成混合整数线性规划的基于SDN和NFV的网络切片的数学模型,并提出了基于拉格朗日对偶分解的算法,将网络切片的数学模型转换为节点和链路的子问题,再对分解的子问题进行映射方案的求解。仿真结果表明,与以往算法相比,文中提出的算法在资源利用率、接受率、平均执行时间方面具有更好性能。展开更多
服务功能链(Service Function Chain, SFC)部署是实现网络服务灵活多样的关键技术,SFC可靠性是SFC部署工作中的重要指标。现有方法在提高SFC可靠性的同时造成了网络资源的浪费。为了平衡SFC可靠性和网络资源消耗,设计了一种VNF分集式备...服务功能链(Service Function Chain, SFC)部署是实现网络服务灵活多样的关键技术,SFC可靠性是SFC部署工作中的重要指标。现有方法在提高SFC可靠性的同时造成了网络资源的浪费。为了平衡SFC可靠性和网络资源消耗,设计了一种VNF分集式备份(VNF Diversity Backup, VDB)机制,利用有限的网络资源改良SFC,将低可靠VNF实例拆分为两个副本实例,并对VNF副本实例进行交叉备份。在SFC部署阶段,提出了一种基于VDB机制的服务功能链部署方法,根据VDB机制的改良结果及网络拓扑属性构建多阶段图,采用基于Viterbi的动态规划算法在多阶段图中搜索最优部署路径。此外,引入评价指标备份性价比来衡量SFC可靠性和网络资源消耗的平衡效果。仿真结果表明,所提方法有效地平衡了可靠性和网络资源消耗,并且优化了传输时延。展开更多
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。
基金This work was supported by the Key Research and Development(R&D)Plan of Heilongjiang Province of China(JD22A001).
文摘With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.
文摘针对如何在部署服务功能链SFC(service function chain)的同时兼顾低能耗与网络负载均衡,提出了一种以节点负载状态预测为基础的SFC部署方法NIR-IACA(improved ant colony algorithm based on node importance ranking)。首先,使用基于粒子群优化的CNN-GRU模型(particle swarm optimization-based CNN-GRU model,PCNN-GRU),结合广义网络温度(GNT)预测网络节点的负载状态,并据此为SFC部署提供备选节点;其次,基于最短路径优先策略的改进蚁群算法(ant colony algorithm,ACA)设计SFC部署节点选择策略(high availability and resource scheduling,HARS)且对选定节点进行虚拟链路映射,优化目标兼顾基础设施网络低能耗与负载均衡的要求。基于Clearwater VNF公开数据集的实验结果表明,提出的NIR-IACA方法与现有的MC-EEVP算法、DPVC算法以及RQAP算法相比平均节省13.09%的能耗,并提高12.98%的负载均衡能力,且在维持相对较高SFC请求的接受率的同时,可以较好地实现SFC部署的能耗与负载均衡联合优化。
文摘软件定义网络(Software Defined Network,SDN)和网络功能虚拟化(Network Function Virtualization,NFV)已经成为新一代网络体系结构的新范式。SDN和NFV可以有效地提高部署和管理服务功能链(Service Function Chains,SFCs)的灵活性。文中融合SDN和NFV,并应用到网络切片的资源优化中,为未来的5G三大业务场景实现定制服务的有效部署,提出了一种基于SDN和NFV的网络切片资源优化算法。首先针对5G uRLLC,eMBB,mMTC三大应用场景的不同业务需求,将底层的物理节点按照功能类型划分为三个虚拟子网的节点集合。然后根据不同应用场景的不同服务功能链的要求,分别对每一类的网络切片进行建模,形成混合整数线性规划的基于SDN和NFV的网络切片的数学模型,并提出了基于拉格朗日对偶分解的算法,将网络切片的数学模型转换为节点和链路的子问题,再对分解的子问题进行映射方案的求解。仿真结果表明,与以往算法相比,文中提出的算法在资源利用率、接受率、平均执行时间方面具有更好性能。
