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Cloud computing for integrated stochastic groundwater uncertainty analysis

International Journal of Digital Earth 2013年第4期6卷 313-337页

作者： Yong Liu Alexander Y.Sun Keith Nelson Wesley E.Hipke National Center for Supercomputing Applications University of Illinois at Urbana-ChampaignUrbanaILUSA Bureau of Economic Geology University of Texas at AustinAustinTXUSA Arizona Department of Water Resources PhoenixAZUSA

One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.

关键词： MODFLOW groundwatersustainability uncertaintyanalysis global sensitivity analysis cloudcomputing WindowsAzure Dropbox alternative conceptual model cyberinfrastructure eScience

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Parallel Web Mining System Based on Cloud Platform

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ZTE Communications 2012年第4期10卷 45-53页

作者： Shengmei Luo Qing He Lixia Liu Xiang Ao Ning Li Fuzhen Zhuang Pre-Research department of ZTE Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithms to be able to handle large-scale, high-dimensional text data. Cloud computing involves the delivery of computing and storage as a service to a heterogeneous community of recipients, Recently, it has aroused much interest in industry and academia. Most previous works on cloud platforms only focus on the parallel algorithms for structured data. In this paper, we focus on the parallel implementation of web-mining algorithms and develop a parallel web-mining system that includes parallel web crawler; parallel text extract, transform and load （ETL） and modeling; and parallel text mining and application subsystems. The complete system enables variable real-world web-mining applications for mass data.

关键词： web mining large scale high volume high dimension cloudcomputing

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Solving Dynamic Spectrum Management Problem Based on Cloud Computing Using Genetic Algorithm

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Journal of Electronic Science and Technology 2013年第2期11卷 132-139页

作者： Ping-Liang Chen Yu-Cheng Lin Shin-Jia Chen Department of Multimedia and M-Commerce Kainan University Department of Multimedia and M-Commerce Kainan University

With the rapid development of wireless sensor network （WSN）, the demands of limited radio frequency spectrum rise sharply, thereby dealing with the frequency assignment of WSN scientifically and efficiently becomes a popular topic. To improve the frequency utilization rate in WSN, a spectrum management system for WSN combined with cloud computing technology should be considered. From the optimization point of view, the study of dynamic spectrum management can be divided into three kinds of methods, including Nash equilibrium, social utility maximization, and competitive economy equilibrium. In this paper, we propose a genetic algorithm based approach to allocate the power spectrum dynamically. The objective is to maximize the sum of individual Shannon utilities with the background interference and crosstalk consideration. Compared to the approach in [1], the experimental result shows better balance between efficiency and effectiveness of our approach.

关键词： Terms-Background interference cloudcomputing crosstalk dynamic spectrum management genetic algorithm Shannon utility wireless sensornetwork.

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云计算技术应用实践

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中国新通信 2021年第6期23卷 112-113页

作者：冯子煦中国联合网络通信有限公司佛山市分公司

云计算(cloudcomputing)是信息时代虚拟化、网络化、共享化的重要产物。目前Openstack架构是较流行的云计算核心架构,作为一种开源架构,被广泛应用。下文就以Openstack为核心的云计算结构做简要介绍,帮助大家了解云计算基础结构知识的... 详细信息

云计算(cloudcomputing)是信息时代虚拟化、网络化、共享化的重要产物。目前Openstack架构是较流行的云计算核心架构,作为一种开源架构,被广泛应用。下文就以Openstack为核心的云计算结构做简要介绍,帮助大家了解云计算基础结构知识的同时简要介绍云计算的一些应用实践场景和应用优势。

关键词： cloudcomputing Openstack keystone swift glance cinder nova Neutron

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