With the increasingly fierce competition among communication operators,it is more and more important to make an accurate prediction of potential off grid users.To solve the above problem,it is inevitable to consider t...With the increasingly fierce competition among communication operators,it is more and more important to make an accurate prediction of potential off grid users.To solve the above problem,it is inevitable to consider the effectiveness of learning algo rithms,the efficiency of data processing,and other factors.Therefore,in this paper,we,from the practical application point of view,propose a potential customer off grid predic tion system based on Spark,including data pre processing,feature selection,model build ing,and effective display.Furthermore,in the research of off grid system,we use the Spark parallel framework to improve the gcForest algorithm which is a novel decision tree ensemble approach.The new parallel gcForest algorithm can be used to solve practical problems,such as the off grid prediction problem.Experiments on two real world datasets demonstrate that the proposed prediction system can handle large scale data for the off grid user prediction problem and the proposed parallel gcForest can achieve satisfying per formance.展开更多
Cooperation of multi-domain massively parallel processor systems in computing grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, ne...Cooperation of multi-domain massively parallel processor systems in computing grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, network adaptability and scalability raise the challenge for the international design of multisite job scheduling models and algorithms. It presents multisite job scheduling schema through the introduction of multisite job scheduling model and the performance model under the grid environment. It introduces two job multisite and cooperative scheduling models and algorithms with the core of the optimal and greedy-heuristic resource selection strategies. Meanwhile, compared with single and multisite cooperative scheduling models and algorithms introduced by Sabin, Yahyapour and other persons, the validity and advance of the scheduling model and the performance model herein are proved.展开更多
Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate des...Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.展开更多
随着物联网(Internet of Things,IoT)和5G的发展,智能电网对低延迟、高可靠的数据通信需求不断增加。文章探讨了智能边缘计算在智能电网通信中的应用与部署,涵盖智能电表数据采集和电力设备监控与维护等场景。通过优化智能边缘计算节点...随着物联网(Internet of Things,IoT)和5G的发展,智能电网对低延迟、高可靠的数据通信需求不断增加。文章探讨了智能边缘计算在智能电网通信中的应用与部署,涵盖智能电表数据采集和电力设备监控与维护等场景。通过优化智能边缘计算节点的选址、硬件配置和网络架构设计,实现数据本地化处理和实时上传,减少数据传输延迟和网络负载。此外,文章从计算资源分配优化、任务调度与资源分配优化、数据传输优化等3方面提出了性能优化方法。通过实验验证,文章提出的智能边缘计算在智能电网中的应用部署与性能优化方法提升了数据处理效率和系统可靠性,展现出重要的应用价值。展开更多
基金supported by ZTE Industry-Academia-Research Cooperationthe National Key Research and Development Program of China under Grant No.2017YFB1002104+1 种基金the National Natural Science Foundation of China under Grant Nos.U1836206,U1811461,and 61773361the Project of Youth Innovation Promotion Association CAS under Grant No.2017146
文摘With the increasingly fierce competition among communication operators,it is more and more important to make an accurate prediction of potential off grid users.To solve the above problem,it is inevitable to consider the effectiveness of learning algo rithms,the efficiency of data processing,and other factors.Therefore,in this paper,we,from the practical application point of view,propose a potential customer off grid predic tion system based on Spark,including data pre processing,feature selection,model build ing,and effective display.Furthermore,in the research of off grid system,we use the Spark parallel framework to improve the gcForest algorithm which is a novel decision tree ensemble approach.The new parallel gcForest algorithm can be used to solve practical problems,such as the off grid prediction problem.Experiments on two real world datasets demonstrate that the proposed prediction system can handle large scale data for the off grid user prediction problem and the proposed parallel gcForest can achieve satisfying per formance.
基金This work was supported in part by the National Natural Science Foundation of China （Grant No. 90412001） the National Grand Fundamental Research 973 Program of China （Grant No. G2005CB321806）.
文摘Cooperation of multi-domain massively parallel processor systems in computing grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, network adaptability and scalability raise the challenge for the international design of multisite job scheduling models and algorithms. It presents multisite job scheduling schema through the introduction of multisite job scheduling model and the performance model under the grid environment. It introduces two job multisite and cooperative scheduling models and algorithms with the core of the optimal and greedy-heuristic resource selection strategies. Meanwhile, compared with single and multisite cooperative scheduling models and algorithms introduced by Sabin, Yahyapour and other persons, the validity and advance of the scheduling model and the performance model herein are proved.
基金This work was supported in part by the Natural Science Foundation of the Education Department of Henan Province(Grant 22A520025)the National Natural Science Foundation of China(Grant 61975053)the National Key Research and Development of Quality Information Control Technology for Multi-Modal Grain Transportation Efficient Connection(2022YFD2100202).
文摘Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.
文摘随着物联网(Internet of Things,IoT)和5G的发展,智能电网对低延迟、高可靠的数据通信需求不断增加。文章探讨了智能边缘计算在智能电网通信中的应用与部署,涵盖智能电表数据采集和电力设备监控与维护等场景。通过优化智能边缘计算节点的选址、硬件配置和网络架构设计,实现数据本地化处理和实时上传,减少数据传输延迟和网络负载。此外,文章从计算资源分配优化、任务调度与资源分配优化、数据传输优化等3方面提出了性能优化方法。通过实验验证,文章提出的智能边缘计算在智能电网中的应用部署与性能优化方法提升了数据处理效率和系统可靠性,展现出重要的应用价值。
