Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power network.Tasked with bo...Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power network.Tasked with both packet transmission and data processing,it requires joint optimization of communications and computing.Considering the diverse requirements of applications,we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network.Different from traditional routing protocols,additional metrics related to computing are taken into consideration in the proposed policy.Based on the multi-attribute decision theory and the fuzzy logic theory,we propose two routing selection algorithms,the Fuzzy Logic-Based Routing(FLBR)algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making(l PMADM)algorithm.Simulation results show that the proposed policy could achieve better performance in average processing delay,user satisfaction,and load balancing compared with existing works.展开更多
Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security.However,the traditional FL model in communication sce...Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security.However,the traditional FL model in communication scenarios,whether for uplink or downlink communications,may give rise to several network problems,such as bandwidth occupation,additional network latency,and bandwidth fragmentation.In this paper,we propose an adaptive chained training approach(Fed ACT)for FL in computing power networks.First,a Computation-driven Clustering Strategy(CCS)is designed.The server clusters clients by task processing delays to minimize waiting delays at the central server.Second,we propose a Genetic-Algorithm-based Sorting(GAS)method to optimize the order of clients participating in training.Finally,based on the table lookup and forwarding rules of the Segment Routing over IPv6(SRv6)protocol,the sorting results of GAS are written into the SRv6 packet header,to control the order in which clients participate in model training.We conduct extensive experiments on two datasets of CIFAR-10 and MNIST,and the results demonstrate that the proposed algorithm offers improved accuracy,diminished communication costs,and reduced network delays.展开更多
企业数字化转型中,应用上云只是手段,如何在满足用户体验情况下,面向不同应用提升算网基础设施的资源效率和运营效率才是目标。随着2C流量见顶,高效算力服务成为基础网络发展的另一个目标。为此,IP网络对业务质量的作用将从保障型向“...企业数字化转型中,应用上云只是手段,如何在满足用户体验情况下,面向不同应用提升算网基础设施的资源效率和运营效率才是目标。随着2C流量见顶,高效算力服务成为基础网络发展的另一个目标。为此,IP网络对业务质量的作用将从保障型向“有效型”转变,从IP路由向“算力路由”转变。围绕企业广域网(Wide Area Network,WAN)场景和多种典型应用,研究新型算力连接和路由技术,提出基于业务优先级调度、接入和服务一体化调度的企业广域网算力连接试点方案。该方案通过控制与转发部分的创新,探索算力连接的服务化和差异化,技术赋能“算力网络化”,并在运营商和企业合作项目开展试点验证,验证结果表明,从改善企业应用体验和算力资源效率的角度,显著提升了IP网络的传输有效性。展开更多
文摘Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power network.Tasked with both packet transmission and data processing,it requires joint optimization of communications and computing.Considering the diverse requirements of applications,we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network.Different from traditional routing protocols,additional metrics related to computing are taken into consideration in the proposed policy.Based on the multi-attribute decision theory and the fuzzy logic theory,we propose two routing selection algorithms,the Fuzzy Logic-Based Routing(FLBR)algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making(l PMADM)algorithm.Simulation results show that the proposed policy could achieve better performance in average processing delay,user satisfaction,and load balancing compared with existing works.
基金supported by the National Key R&D Program of China(No.2021YFB2900200)。
文摘Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security.However,the traditional FL model in communication scenarios,whether for uplink or downlink communications,may give rise to several network problems,such as bandwidth occupation,additional network latency,and bandwidth fragmentation.In this paper,we propose an adaptive chained training approach(Fed ACT)for FL in computing power networks.First,a Computation-driven Clustering Strategy(CCS)is designed.The server clusters clients by task processing delays to minimize waiting delays at the central server.Second,we propose a Genetic-Algorithm-based Sorting(GAS)method to optimize the order of clients participating in training.Finally,based on the table lookup and forwarding rules of the Segment Routing over IPv6(SRv6)protocol,the sorting results of GAS are written into the SRv6 packet header,to control the order in which clients participate in model training.We conduct extensive experiments on two datasets of CIFAR-10 and MNIST,and the results demonstrate that the proposed algorithm offers improved accuracy,diminished communication costs,and reduced network delays.
文摘企业数字化转型中,应用上云只是手段,如何在满足用户体验情况下,面向不同应用提升算网基础设施的资源效率和运营效率才是目标。随着2C流量见顶,高效算力服务成为基础网络发展的另一个目标。为此,IP网络对业务质量的作用将从保障型向“有效型”转变,从IP路由向“算力路由”转变。围绕企业广域网(Wide Area Network,WAN)场景和多种典型应用,研究新型算力连接和路由技术,提出基于业务优先级调度、接入和服务一体化调度的企业广域网算力连接试点方案。该方案通过控制与转发部分的创新,探索算力连接的服务化和差异化,技术赋能“算力网络化”,并在运营商和企业合作项目开展试点验证,验证结果表明,从改善企业应用体验和算力资源效率的角度,显著提升了IP网络的传输有效性。
