We study a novel replication mechanism to ensure service continuity against multiple simultaneous server failures.In this mechanism,each item represents a computing task and is replicated intoξ+1 servers for some int...We study a novel replication mechanism to ensure service continuity against multiple simultaneous server failures.In this mechanism,each item represents a computing task and is replicated intoξ+1 servers for some integerξ≥1,with workloads specified by the amount of required resources.If one or more servers fail,the affected workloads can be redirected to other servers that host replicas associated with the same item,such that the service is not interrupted by the failure of up toξservers.This requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading,and determining the optimal method for reserving capacity becomes a key issue.Unlike existing algorithms that assume that no two servers share replicas of more than one item,we first formulate capacity reservation for a general arbitrary scenario.Due to the combinatorial nature of this problem,finding the optimal solution is difficult.To this end,we propose a Generalized and Simple Calculating Reserved Capacity(GSCRC)algorithm,with a time complexity only related to the number of items packed in the server.In conjunction with GSCRC,we propose a robust replica packing algorithm with capacity optimization(RobustPack),which aims to minimize the number of servers hosting replicas and tolerate multiple server failures.Through theoretical analysis and experimental evaluations,we show that the RobustPack algorithm can achieve better performance.展开更多
Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integra...Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.展开更多
In recent years,the number of known disease genes has exponentially grown due to the widespread adoption of cost-effective massive parallel sequencing.To identify new disease variants,especially in monogenic disorders...In recent years,the number of known disease genes has exponentially grown due to the widespread adoption of cost-effective massive parallel sequencing.To identify new disease variants,especially in monogenic disorders,a frequency-based filtering approach has proved very useful(Dopazo et al.,2016).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
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
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
目的 设计一个基于移动物联网(Mobile Internet of Things,MIoT)的健康管理平台,实现医疗设备的智能化管理。方法 基于MIoT的健康管理平台构建由感知层、网络层、平台层以及应用层组成的系统架构,感知层通过三维加速传感器与射频识别标...目的 设计一个基于移动物联网(Mobile Internet of Things,MIoT)的健康管理平台,实现医疗设备的智能化管理。方法 基于MIoT的健康管理平台构建由感知层、网络层、平台层以及应用层组成的系统架构,感知层通过三维加速传感器与射频识别标签实现数据采集,网络层运用5G切片技术结合无线入侵检测系统和无线网络控制器传输数据,云平台集成实时流处理与批量分析引擎,应用层通过智能算法实现医疗设备的智能化管理。比较基于MIoT的健康管理平台应用前后医疗设备调配次数、设备调配响应时间、调配差错台数、设备平均维修周期、设备终末维护合格率、运维支出成本以及维修维保金额。结果 基于MIoT的健康管理平台应用后,医疗设备使用率、医疗设备调配次数、设备终末维护合格率与平台应用前比较均显著提升,差异有统计学意义(P<0.05),设备调配响应时间、调配差错台数、设备平均维修周期、运维支出成本、维修维保金额均显著降低,差异有统计学意义(P<0.05)。结论 基于MIoT的健康管理平台在医疗设备智能化管理中能够显著提升医疗设备使用效率,减少医疗设备的维护成本,为医院医疗设备的智能化管理提供参考。展开更多
基金supported in part by the National Key R&D Program of China under No.2023YFB2703800the National Science Foundation of China under Grants U22B2027,62172297,62102262,61902276 and 62272311+3 种基金Tianjin Intelligent Manufacturing Special Fund Project under Grants 20211097the China Guangxi Science and Technology Plan Project(Guangxi Science and Technology Base and Talent Special Project)under Grant AD23026096(Application Number 2022AC20001)Henan Provincial Natural Science Foundation of China under Grant 622RC616CCF-Nsfocus Kunpeng Fund Project under Grants CCF-NSFOCUS202207。
文摘We study a novel replication mechanism to ensure service continuity against multiple simultaneous server failures.In this mechanism,each item represents a computing task and is replicated intoξ+1 servers for some integerξ≥1,with workloads specified by the amount of required resources.If one or more servers fail,the affected workloads can be redirected to other servers that host replicas associated with the same item,such that the service is not interrupted by the failure of up toξservers.This requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading,and determining the optimal method for reserving capacity becomes a key issue.Unlike existing algorithms that assume that no two servers share replicas of more than one item,we first formulate capacity reservation for a general arbitrary scenario.Due to the combinatorial nature of this problem,finding the optimal solution is difficult.To this end,we propose a Generalized and Simple Calculating Reserved Capacity(GSCRC)algorithm,with a time complexity only related to the number of items packed in the server.In conjunction with GSCRC,we propose a robust replica packing algorithm with capacity optimization(RobustPack),which aims to minimize the number of servers hosting replicas and tolerate multiple server failures.Through theoretical analysis and experimental evaluations,we show that the RobustPack algorithm can achieve better performance.
基金This work was supported by the Ministry of Education and China Mobile Research Fund Project(MCM20200102)the 173 Project(No.2019-JCJQ-ZD-342-00)+2 种基金the National Natural Science Foundation of China(No.U19A2081)the Fundamental Research Funds for the Central Universities(No.2023SCU12129)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129).
文摘Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.
基金funded by Instituto de Salud Carlos Ill(IsCll)and co-funded by the European Union(grants Pl16/00612 and PI20/01200 to MCS)Junta de Andalucia-Consejeria de Salud y Consumo(grant PIER-0468-2019 to MCS)+4 种基金MCS has been supported by ISCIII(JR15/00042)Junta de Andalucia-Consejeria de Salud y Consumo(B-0005-2017)JD has been supported by grants PID2020-117979RB-100 from the Spanish Ministry of Science and Innovation and IMP/00019 from the Instituto de Salud Carlos III(ISCIII)RC has been supported by Junta de Andalucia-Consejeria de Salud y Familias(RH-0052-2021)by the European Union,European Social Fund(FSE)2014-2020.
文摘In recent years,the number of known disease genes has exponentially grown due to the widespread adoption of cost-effective massive parallel sequencing.To identify new disease variants,especially in monogenic disorders,a frequency-based filtering approach has proved very useful(Dopazo et al.,2016).
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.
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.
文摘目的 设计一个基于移动物联网(Mobile Internet of Things,MIoT)的健康管理平台,实现医疗设备的智能化管理。方法 基于MIoT的健康管理平台构建由感知层、网络层、平台层以及应用层组成的系统架构,感知层通过三维加速传感器与射频识别标签实现数据采集,网络层运用5G切片技术结合无线入侵检测系统和无线网络控制器传输数据,云平台集成实时流处理与批量分析引擎,应用层通过智能算法实现医疗设备的智能化管理。比较基于MIoT的健康管理平台应用前后医疗设备调配次数、设备调配响应时间、调配差错台数、设备平均维修周期、设备终末维护合格率、运维支出成本以及维修维保金额。结果 基于MIoT的健康管理平台应用后,医疗设备使用率、医疗设备调配次数、设备终末维护合格率与平台应用前比较均显著提升,差异有统计学意义(P<0.05),设备调配响应时间、调配差错台数、设备平均维修周期、运维支出成本、维修维保金额均显著降低,差异有统计学意义(P<0.05)。结论 基于MIoT的健康管理平台在医疗设备智能化管理中能够显著提升医疗设备使用效率,减少医疗设备的维护成本,为医院医疗设备的智能化管理提供参考。
