The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learnin...The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of users.However,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL training.With the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL process.Through the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing works.In this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter server.Our approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost efficiency.The FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training process.Theoretical analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstrate a reduction in training round-to-accuracy by 12%–20%compared to the Federated Averaging(FedAvg)with random reshuffling method under unique server.Furthermore,FedAdaSS effectively mitigates performance loss caused by low client engagement,reducing the loss indicator by 50%.展开更多
Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of s...Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.展开更多
This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers vi...This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers via the Simple Mail Transfer Protocol(SMTP).As a web server,it handles Transmission Control Protocol(TCP)connection requests from browsers,receives HTTP commands and email data,and temporarily stores the emails in a file.Simultaneously,as an SMTP client,the program establishes a TCP connection with the actual mail server,sends SMTP commands,and transmits the previously saved emails.In addition,we also analyzed security issues and the efficiency and availability of this server,providing insights into the design of SMTP mail servers.展开更多
讨论了基于M atlab W eb Server的M atlab网络应用开发原理,介绍了M atlab W eb程序处理的一般流程和相关配置文件的详细配置方法,并给出M atlab W eb开发中的两个关键问题:通过输入模块从HTML页面获取输入参数和通过输出模块生成包括...讨论了基于M atlab W eb Server的M atlab网络应用开发原理,介绍了M atlab W eb程序处理的一般流程和相关配置文件的详细配置方法,并给出M atlab W eb开发中的两个关键问题:通过输入模块从HTML页面获取输入参数和通过输出模块生成包括输出数据和图片的HTML文件.利用M atlab W eb Server环境实现了远程控制实验室的控制效果仿真,并以二维图形的输出形式显示仿真结果,为网上控制实验室的建立提供了控制参数选择以及试验结果验证参照.本远程数据处理方法可推广应用到不同的远程数据处理领域,具有很高的推广价值.展开更多
ArcGIS Server可以构建Web应用、Web服务,ArcGIS Server的出现为网络地图服务提供了一个全新的途径。以ArcGIS Server10.0为平台研究网络地图服务系统的设计与实现,基于B/S三层混合模式,采用ArcGIS REST API和ArcGIS API For Flex,将Arc...ArcGIS Server可以构建Web应用、Web服务,ArcGIS Server的出现为网络地图服务提供了一个全新的途径。以ArcGIS Server10.0为平台研究网络地图服务系统的设计与实现,基于B/S三层混合模式,采用ArcGIS REST API和ArcGIS API For Flex,将ArcSDE作为空间数据引擎,SQLServer作为数据库进行空间数据管理,设计和实现一个具有基本地图操作功能、地图定位、查询、空间分析的地图网络发布系统,为地图的网络服务奠定基础。展开更多
基金supported in part by the National Natural Science Foundation of China under Grant U22B2005,Grant 62372462.
文摘The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of users.However,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL training.With the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL process.Through the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing works.In this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter server.Our approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost efficiency.The FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training process.Theoretical analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstrate a reduction in training round-to-accuracy by 12%–20%compared to the Federated Averaging(FedAvg)with random reshuffling method under unique server.Furthermore,FedAdaSS effectively mitigates performance loss caused by low client engagement,reducing the loss indicator by 50%.
文摘Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.
文摘This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers via the Simple Mail Transfer Protocol(SMTP).As a web server,it handles Transmission Control Protocol(TCP)connection requests from browsers,receives HTTP commands and email data,and temporarily stores the emails in a file.Simultaneously,as an SMTP client,the program establishes a TCP connection with the actual mail server,sends SMTP commands,and transmits the previously saved emails.In addition,we also analyzed security issues and the efficiency and availability of this server,providing insights into the design of SMTP mail servers.
文摘讨论了基于M atlab W eb Server的M atlab网络应用开发原理,介绍了M atlab W eb程序处理的一般流程和相关配置文件的详细配置方法,并给出M atlab W eb开发中的两个关键问题:通过输入模块从HTML页面获取输入参数和通过输出模块生成包括输出数据和图片的HTML文件.利用M atlab W eb Server环境实现了远程控制实验室的控制效果仿真,并以二维图形的输出形式显示仿真结果,为网上控制实验室的建立提供了控制参数选择以及试验结果验证参照.本远程数据处理方法可推广应用到不同的远程数据处理领域,具有很高的推广价值.
文摘ArcGIS Server可以构建Web应用、Web服务,ArcGIS Server的出现为网络地图服务提供了一个全新的途径。以ArcGIS Server10.0为平台研究网络地图服务系统的设计与实现,基于B/S三层混合模式,采用ArcGIS REST API和ArcGIS API For Flex,将ArcSDE作为空间数据引擎,SQLServer作为数据库进行空间数据管理,设计和实现一个具有基本地图操作功能、地图定位、查询、空间分析的地图网络发布系统,为地图的网络服务奠定基础。
