The security issue is always the most important concern of networked client-server application. On the putpose to build the secure group communication among of a group of client users and one server, in this paper, we...The security issue is always the most important concern of networked client-server application. On the putpose to build the secure group communication among of a group of client users and one server, in this paper, we will present a new password-based group key agreement protocol. Our protocol will meet simplicity, efficiency, and many desired security properties.展开更多
为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性...为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性,并大幅减少冷启动时间。DFS-Cache包括基于虚拟内存重映射的缓存碎片整理机制和基于生存时间(TTL)的缓存空间管理策略。前者基于NVM可被内存控制器直接寻址的特性,动态修改虚拟地址和物理地址之间的映射关系,实现零拷贝的内存碎片整理;后者是一种冷热分离的分组管理策略,借助重映射的缓存碎片整理机制,提升缓存空间的管理效率。实验采用真实的Intel傲腾持久性内存设备,对比商用的分布式文件系统MooseFS和GlusterFS,采用Fio和Filebench等标准测试程序,DFS-Cache最高能提升5.73倍和1.89倍的系统吞吐量。展开更多
This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneous...This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.展开更多
Federated learning has emerged as a distributed learning paradigm by training at each client and aggregat-ing at a parameter server.System heterogeneity hinders stragglers from responding to the server in time with hu...Federated learning has emerged as a distributed learning paradigm by training at each client and aggregat-ing at a parameter server.System heterogeneity hinders stragglers from responding to the server in time with huge com-munication costs.Although client grouping in federated learning can solve the straggler problem,the stochastic selection strategy in client grouping neglects the impact of data distribution within each group.Besides,current client grouping ap-proaches make clients suffer unfair participation,leading to biased performances for different clients.In order to guaran-tee the fairness of client participation and mitigate biased local performances,we propose a federated dynamic client selec-tion method based on data representativity(FedSDR).FedSDR clusters clients into groups correlated with their own lo-cal computational efficiency.To estimate the significance of client datasets,we design a novel data representativity evalua-tion scheme based on local data distribution.Furthermore,the two most representative clients in each group are selected to optimize the global model.Finally,the DYNAMIC-SELECT algorithm updates local computational efficiency and data representativity states to regroup clients after periodic average aggregation.Evaluations on real datasets show that FedS-DR improves client participation by 27.4%,37.9%,and 23.3%compared with FedAvg,TiFL,and FedSS,respectively,tak-ing fairness into account in federated learning.In addition,FedSDR surpasses FedAvg,FedGS,and FedMS by 21.32%,20.4%,and 6.90%,respectively,in local test accuracy variance,balancing the performance bias of the global model across clients.展开更多
基金Supported by the National Natural Science Foun-dation of China (60572155) the National Natural Science Founda-tion of China for Distinguished Young Scholars (60225007)
文摘The security issue is always the most important concern of networked client-server application. On the putpose to build the secure group communication among of a group of client users and one server, in this paper, we will present a new password-based group key agreement protocol. Our protocol will meet simplicity, efficiency, and many desired security properties.
文摘为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性,并大幅减少冷启动时间。DFS-Cache包括基于虚拟内存重映射的缓存碎片整理机制和基于生存时间(TTL)的缓存空间管理策略。前者基于NVM可被内存控制器直接寻址的特性,动态修改虚拟地址和物理地址之间的映射关系,实现零拷贝的内存碎片整理;后者是一种冷热分离的分组管理策略,借助重映射的缓存碎片整理机制,提升缓存空间的管理效率。实验采用真实的Intel傲腾持久性内存设备,对比商用的分布式文件系统MooseFS和GlusterFS,采用Fio和Filebench等标准测试程序,DFS-Cache最高能提升5.73倍和1.89倍的系统吞吐量。
文摘This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.
基金This work is supported by the National Key Research and Development Program of China under Grant No.2022YFC3005401the Key Research and Development Program of Yunnan Province of China under Grant No.202203AA080009+1 种基金the Transformation Program of Scientific and Technological Achievements of Jiangsu Province of China under Grant No.BA2021002the Key Research and Development Program of Jiangsu Province of Chin under Grant No.BE2020729.
文摘Federated learning has emerged as a distributed learning paradigm by training at each client and aggregat-ing at a parameter server.System heterogeneity hinders stragglers from responding to the server in time with huge com-munication costs.Although client grouping in federated learning can solve the straggler problem,the stochastic selection strategy in client grouping neglects the impact of data distribution within each group.Besides,current client grouping ap-proaches make clients suffer unfair participation,leading to biased performances for different clients.In order to guaran-tee the fairness of client participation and mitigate biased local performances,we propose a federated dynamic client selec-tion method based on data representativity(FedSDR).FedSDR clusters clients into groups correlated with their own lo-cal computational efficiency.To estimate the significance of client datasets,we design a novel data representativity evalua-tion scheme based on local data distribution.Furthermore,the two most representative clients in each group are selected to optimize the global model.Finally,the DYNAMIC-SELECT algorithm updates local computational efficiency and data representativity states to regroup clients after periodic average aggregation.Evaluations on real datasets show that FedS-DR improves client participation by 27.4%,37.9%,and 23.3%compared with FedAvg,TiFL,and FedSS,respectively,tak-ing fairness into account in federated learning.In addition,FedSDR surpasses FedAvg,FedGS,and FedMS by 21.32%,20.4%,and 6.90%,respectively,in local test accuracy variance,balancing the performance bias of the global model across clients.
