A low-Earth-orbit(LEO)satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking.However,the large variability of the geographic distribution of the Ea...A low-Earth-orbit(LEO)satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking.However,the large variability of the geographic distribution of the Earth’s population leads to an uneven service volume distribution of access service.Moreover,the limitations on the resources of satellites are far from being able to serve the traffic in hotspot areas.To enhance the forwarding capability of satellite networks,we first assess how hotspot areas under different load cases and spatial scales significantly affect the network throughput of an LEO satellite network overall.Then,we propose a multi-region cooperative traffic scheduling algorithm.The algorithm migrates low-grade traffic from hotspot areas to coldspot areas for forwarding,significantly increasing the overall throughput of the satellite network while sacrificing some latency of end-to-end forwarding.This algorithm can utilize all the global satellite resources and improve the utilization of network resources.We model the cooperative multi-region scheduling of large-scale LEO satellites.Based on the model,we build a system testbed using OMNET++to compare the proposed method with existing techniques.The simulations show that our proposed method can reduce the packet loss probability by 30%and improve the resource utilization ratio by 3.69%.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
针对无线传感网络在实际应用中网络能耗不均匀、传感器节点容易失效以及网络生命周期短的问题,提出一种基于聚类树的负载平衡算法(load balancing algorithm based on cluster tree, LBACT)。基于聚类树将网络中的节点划分为多层结构,...针对无线传感网络在实际应用中网络能耗不均匀、传感器节点容易失效以及网络生命周期短的问题,提出一种基于聚类树的负载平衡算法(load balancing algorithm based on cluster tree, LBACT)。基于聚类树将网络中的节点划分为多层结构,通过立即转发机制降低聚类树的高度;使用平衡算法对构造的聚类树进行多轮次负载平衡,在汇聚节点的控制下,通过每个节点多次运行负载平衡算法,使网络中的节点负载平衡。仿真结果表明,相比LEACH(low-energy adaptive clustering hierarchy)和CBSHA(component based self-healing approach),提出的算法能够有效均衡节点能耗,延长网络的稳定期、生命周期,提高网络的吞吐量。展开更多
算力供给的代际异构性与供应链安全需求,促使异构算力成为AI基础设施的新趋势。然而,在异构混合训练场景中,基于融合以太网的RDMA版本2(RDMA over converged Ethernet version 2,RoCEv2)方案存在负载均衡与拥塞控制缺陷,在模型训练的并...算力供给的代际异构性与供应链安全需求,促使异构算力成为AI基础设施的新趋势。然而,在异构混合训练场景中,基于融合以太网的RDMA版本2(RDMA over converged Ethernet version 2,RoCEv2)方案存在负载均衡与拥塞控制缺陷,在模型训练的并行通信中性能欠佳;而现有高性能同构智算网络方案因设备异构与集合通信库(collective communication library,CCL)闭源难以部署。为此,提出了面向异构算力场景的高性能智算网络解决方案——智能控制以太网(intelligent control Ethernet,ICE)。该方案基于RoCEv2协议体系,在避免对设备、CCL进行深度定制的前提下,将异构通信库信息采集、集中控制器与端侧自主控制相结合,实现全局最优路径规划及全局主动拥塞控制,显著提升异构并行通信性能。真实物理环境实验表明,ICE可提升集合通信性能最高达47%。ICE为异构智算网络建设提供了开创性、易部署的解决方案。展开更多
海上异构无线网络覆盖下的业务需求和节点多种多样,这就要求网络资源在分配时需要兼顾匹配性和网络性能。相邻多节点在选择无线网络时,易产生策略冲突并大量接入同一网络,进而导致网络负载不均衡、接入阻塞率高和服务质量(quality of se...海上异构无线网络覆盖下的业务需求和节点多种多样,这就要求网络资源在分配时需要兼顾匹配性和网络性能。相邻多节点在选择无线网络时,易产生策略冲突并大量接入同一网络,进而导致网络负载不均衡、接入阻塞率高和服务质量(quality of service,Qos)降低。针对以上问题提出一种基于网络评价和时隙分配的网络接入选择算法,设计网络评价模块、时隙分配模块和接入决策模块,通过业务评价和阻塞率估计对可选网络进行综合评价以提高匹配性,根据节点执行业务的优先级对多节点进行时隙分配,依序优化网络接入策略。仿真结果表明,相对于负载均衡和多属性决策算法,所提算法的节点平均接入阻塞率降低了32.09%,20.87%,节点平均吞吐量提升了76.90%,33.25%,网络的负载均衡性和传输效率均得到有效提升。展开更多
基金This work was supported by the National Key R&D Program of China(2021YFB2900604).
文摘A low-Earth-orbit(LEO)satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking.However,the large variability of the geographic distribution of the Earth’s population leads to an uneven service volume distribution of access service.Moreover,the limitations on the resources of satellites are far from being able to serve the traffic in hotspot areas.To enhance the forwarding capability of satellite networks,we first assess how hotspot areas under different load cases and spatial scales significantly affect the network throughput of an LEO satellite network overall.Then,we propose a multi-region cooperative traffic scheduling algorithm.The algorithm migrates low-grade traffic from hotspot areas to coldspot areas for forwarding,significantly increasing the overall throughput of the satellite network while sacrificing some latency of end-to-end forwarding.This algorithm can utilize all the global satellite resources and improve the utilization of network resources.We model the cooperative multi-region scheduling of large-scale LEO satellites.Based on the model,we build a system testbed using OMNET++to compare the proposed method with existing techniques.The simulations show that our proposed method can reduce the packet loss probability by 30%and improve the resource utilization ratio by 3.69%.
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。
文摘针对无线传感网络在实际应用中网络能耗不均匀、传感器节点容易失效以及网络生命周期短的问题,提出一种基于聚类树的负载平衡算法(load balancing algorithm based on cluster tree, LBACT)。基于聚类树将网络中的节点划分为多层结构,通过立即转发机制降低聚类树的高度;使用平衡算法对构造的聚类树进行多轮次负载平衡,在汇聚节点的控制下,通过每个节点多次运行负载平衡算法,使网络中的节点负载平衡。仿真结果表明,相比LEACH(low-energy adaptive clustering hierarchy)和CBSHA(component based self-healing approach),提出的算法能够有效均衡节点能耗,延长网络的稳定期、生命周期,提高网络的吞吐量。
文摘算力供给的代际异构性与供应链安全需求,促使异构算力成为AI基础设施的新趋势。然而,在异构混合训练场景中,基于融合以太网的RDMA版本2(RDMA over converged Ethernet version 2,RoCEv2)方案存在负载均衡与拥塞控制缺陷,在模型训练的并行通信中性能欠佳;而现有高性能同构智算网络方案因设备异构与集合通信库(collective communication library,CCL)闭源难以部署。为此,提出了面向异构算力场景的高性能智算网络解决方案——智能控制以太网(intelligent control Ethernet,ICE)。该方案基于RoCEv2协议体系,在避免对设备、CCL进行深度定制的前提下,将异构通信库信息采集、集中控制器与端侧自主控制相结合,实现全局最优路径规划及全局主动拥塞控制,显著提升异构并行通信性能。真实物理环境实验表明,ICE可提升集合通信性能最高达47%。ICE为异构智算网络建设提供了开创性、易部署的解决方案。
文摘海上异构无线网络覆盖下的业务需求和节点多种多样,这就要求网络资源在分配时需要兼顾匹配性和网络性能。相邻多节点在选择无线网络时,易产生策略冲突并大量接入同一网络,进而导致网络负载不均衡、接入阻塞率高和服务质量(quality of service,Qos)降低。针对以上问题提出一种基于网络评价和时隙分配的网络接入选择算法,设计网络评价模块、时隙分配模块和接入决策模块,通过业务评价和阻塞率估计对可选网络进行综合评价以提高匹配性,根据节点执行业务的优先级对多节点进行时隙分配,依序优化网络接入策略。仿真结果表明,相对于负载均衡和多属性决策算法,所提算法的节点平均接入阻塞率降低了32.09%,20.87%,节点平均吞吐量提升了76.90%,33.25%,网络的负载均衡性和传输效率均得到有效提升。
