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
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
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。
