Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is ...Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is a solution to managing cloud system data from the heterogeneous resources. This paper discusses the monitoring and collection of the heterogeneous resources, studies the adaptive system, and proposes a real-time extensible distributed framework of monitoring data processing. Based on this framework, a system of monitoring data distribution, publication and subscription is proposed. The simulation results show that the proposed mechanism can adaptively determine the distribution action of monitoring data flow, and effectively reduce the costs for data monitoring and distribution.展开更多
The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing sy...The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing systems.In the context of Industry 4.0,smart factories leverage advanced technologies such as IoT,cloud computing,artificial intelligence(AI),and data analytics to enable real-time decision-making,predictive maintenance,and optimization of resources.This paper presents an overview of a smart factory architecture that combines IoT devices,cloud platforms,and data integration layers to create a flexible,scalable,and efficient manufacturing system.The proposed architecture is designed to enhance operational performance,energy efficiency,and resource management while ensuring system scalability and flexibility to meet changing market demands.Additionally,the paper discusses key optimization techniques,including edge computing,predictive maintenance,and cloud-based analytics,which contribute to the overall effectiveness of smart factory operations.The integration of these technologies promises to deliver significant improvements in productivity,cost savings,and sustainability in manufacturing environments.展开更多
As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage p...As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.展开更多
In the cloud age, heterogeneous application modes on large-scale infrastructures bring about the chal- lenges on resource utilization and manageability to data cen- ters. Many resource and runtime management systems a...In the cloud age, heterogeneous application modes on large-scale infrastructures bring about the chal- lenges on resource utilization and manageability to data cen- ters. Many resource and runtime management systems are developed or evolved to address these challenges and rele- vant problems from different perspectives. This paper tries to identify the main motivations, key concerns, common fea- tures, and representative solutions of such systems through a survey and analysis. A typical kind of these systems is gener- alized as the consolidated cluster system, whose design goal is identified as reducing the overall costs under the quality of service premise. A survey on this kind of systems is given, and the critical issues concerned by such systems are sum- marized as resource consolidation and runtime coordination. These two issues are analyzed and classified according to the design styles and external characteristics abstracted from the surveyed work. Five representative consolidated cluster systems from both academia and industry are illustrated and compared in detail based on the analysis and classifications. We hope this survey and analysis to be conducive to both de- sign implementation and technology selection of this kind of systems, in response to the constantly emerging challenges on infrastructure and application management in data centers.展开更多
基金the Scientific Research Foundation of Zhejiang Provincial Education Department of China(No.Y201431192)
文摘Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is a solution to managing cloud system data from the heterogeneous resources. This paper discusses the monitoring and collection of the heterogeneous resources, studies the adaptive system, and proposes a real-time extensible distributed framework of monitoring data processing. Based on this framework, a system of monitoring data distribution, publication and subscription is proposed. The simulation results show that the proposed mechanism can adaptively determine the distribution action of monitoring data flow, and effectively reduce the costs for data monitoring and distribution.
文摘The design and optimization of smart factory architectures integrating the Internet of Things(IoT)and cloud computing have emerged as a crucial factor in enhancing the efficiency and agility of modern manufacturing systems.In the context of Industry 4.0,smart factories leverage advanced technologies such as IoT,cloud computing,artificial intelligence(AI),and data analytics to enable real-time decision-making,predictive maintenance,and optimization of resources.This paper presents an overview of a smart factory architecture that combines IoT devices,cloud platforms,and data integration layers to create a flexible,scalable,and efficient manufacturing system.The proposed architecture is designed to enhance operational performance,energy efficiency,and resource management while ensuring system scalability and flexibility to meet changing market demands.Additionally,the paper discusses key optimization techniques,including edge computing,predictive maintenance,and cloud-based analytics,which contribute to the overall effectiveness of smart factory operations.The integration of these technologies promises to deliver significant improvements in productivity,cost savings,and sustainability in manufacturing environments.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22,MCIN/AEI/10.13039/501100011033by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.
文摘In the cloud age, heterogeneous application modes on large-scale infrastructures bring about the chal- lenges on resource utilization and manageability to data cen- ters. Many resource and runtime management systems are developed or evolved to address these challenges and rele- vant problems from different perspectives. This paper tries to identify the main motivations, key concerns, common fea- tures, and representative solutions of such systems through a survey and analysis. A typical kind of these systems is gener- alized as the consolidated cluster system, whose design goal is identified as reducing the overall costs under the quality of service premise. A survey on this kind of systems is given, and the critical issues concerned by such systems are sum- marized as resource consolidation and runtime coordination. These two issues are analyzed and classified according to the design styles and external characteristics abstracted from the surveyed work. Five representative consolidated cluster systems from both academia and industry are illustrated and compared in detail based on the analysis and classifications. We hope this survey and analysis to be conducive to both de- sign implementation and technology selection of this kind of systems, in response to the constantly emerging challenges on infrastructure and application management in data centers.
