基于服务器虚拟化的网络GIS集群关键技术研究
|
摘要
地理信息系统(GIS)与互联网的结合,拓展了GIS的新领域和新途径,极大的促进了地理空间信息的应用推广,让跨地区和跨行业的空间信息共享更加方便,也使基于地理信息的大众化应用得以快速发展。计算机硬件性能的快速提高,软件领域新构架新算法的推出,以及GIS学科的不断进步,都推动了网络GIS的快速发展。网络GIS应用的不断深入,带来了一些新的问题和挑战,表现有：互联网巨大的用户量和GIS海量数据给网络GIS应用带来很大的性能压力；软件落后于硬件发展的现状,使得新硬件对网络GIS系统性能提升作用受限；传统的网络GIS软件和开发模型不足以满足GIS应用快速开发的需求。
网络GIS集群继承了计算机集群的特点,保证了服务和应用的稳定,极大的缓解了服务端压力,同时方便扩展。负载均衡策略和算法是集群的一项关键技术,算法的改进对提升集群性能和稳定性意义重大,也是各研究领域包括网络GIS的研究热点。服务器虚拟化技术提供了建设集群的新方法,利用它可以很便利的使用各类服务器和PC来构建虚拟集群,已经有不少学者和GIS厂商的研究人员提出了构建虚拟化网络GIS集群的方法和模型,各自有其特色和不足。
本文结合国家科技支撑计划项目“地理空间信息工具集网络服务平台研发”,主要围绕基于服务器虚拟化的网络GIS集群的应用模型和关键技术的实现方法,重点研究网络GIS集群模型的构建方法、虚拟资源的分配技术、负载均衡调度策略和算法、服务与接口模型等一系列关键问题。在此基础上设计结构合理的网络GIS集群模型,解决集群中虚拟资源的动态分配问题,实现符合GIS应用特点的负载均衡算法,设计简明实用的网络GIS服务与接口模型。
本文具体的研究工作如下：
(1)研究分析了服务器虚拟化技术、网络GIS以及集群技术的研究现状和发展趋势,指出虚拟化和集群在提高硬件资源利用率、系统性能和可用性等方面具有独特的优势,是提高网络GIS集群可靠性和性能的有效技术手段。同时虚拟化和集群发展中存在着一些问题和难点,例如性能损耗、负载调度等,都成为GIS集群优化工作所要研究和解决的问题。
(2)研究了虚拟化网络GIS集群结构模型。在研究服务器虚拟化的特点和实现的技术层次基础上,指出虚拟化技术是提高服务器硬件尤其是处理器利用率的有效手段,在基于多核处理器的微小型服务器迅速发展的当前,构建于廉价服务器之上的集群系统能够处理时空复杂度高的计算密集型问题,并可应用于GIS领域。以国内外主流GIS平台提供商的集群结构为研究目标做了分析和对比,结合服务器虚拟化技术,提出了一种网络GIS集群结构模型,该模型采用处理器核心、操作系统、GIS服务实例数量之间的1：1：1映射关系建立集群,并用实验测试的方法将其与单物理服务器多GIS服务实例的方案进行对比。
(3)研究了虚拟化网络GIS集群资源分配技术。分析了虚拟机资源分配的一般方法和虚拟机放置策略,提出了一种基于集群负载预测的资源动态分配技术。对集群的中长期历史负载进行了时间序列建模,并以一组气象数据为实验对象进行了预测建模实验,在负载预测的基础上设计了虚拟机放置时机决策的策略和算法,以及基于最少优先策略的虚拟机放置目标选择算法,并描述了上述算法流程和伪代码。设计了基于分配算法的实验场景并在气象应用上进行测试,对分配技术进行验证。
(4)研究了虚拟化网络GIS集群负载均衡算法。首先研究了遗传算法特点,以及它在负载均衡调度上的应用情况,表明它对网络GIS负载均衡调度同样具备适应性和和鲁棒性。根据GIS运算和应用的特殊性和复杂性,建立了基于任务优先级和执行时间的GIS任务模型,作为遗传算法基因编码依据,设计了用于负载均衡的遗传算法,包括适应度函数、遗传算子以及控制参数,并形成了算法流程。最后将算法插入网络GIS集群负载均衡模块进行了实验测试。
(5)研究了虚拟化网络GIS集群服务与接口模型。在研究空间数据共享问题和面向服务构架的问题基础上,针对SOA结构厚重,数据模型复杂的不足,提出了基于ROA的轻量级GIS集群服务与接口模型,以及基于RIA的跨浏览器的客户端模型,降低了接口模型复杂度,方便用户使用。
(6)研究了虚拟化网络GIS集群服务原型系统。设计并建立了基于服务器虚拟化技术的网络GIS集群原型框架,从具体技术实现的角度进行了原型的分析与描述。以气象行业某应用为研究和实验对象,将虚拟化网络GIS集群原型付诸实践,并进行性能测试,验证集群原型的可行性和合理性。
The combination of GIS and Internet has expanded new fields and new channels of GIS, and promoted the application and extension of geographic spatial information dramatically, which not only makes it more convenient to share the spatial information cross-regionally and cross-industrially, but also makes it more rapidly to develop the popularization of geographic information. The rapid development of WebGIS has been pushed forward owing to the great enhancement of the computer hardware performance, the promotion of new frameworks and new algorisms in software field, and continuous improvement of GIS subject. The unceasing penetration of the WebGIS application has brought about some new problems and challenges, for instance:the large numbers of Internet users and the mass data of GIS have made GIS application under great performance pressure; since software development lags behind hardware development at present, the role that new hardware plays in promoting WebGIS performance has been limited; the traditional WebGIS software and development model couldn't satisfy the demand of GIS rapid development.
WebGIS cluster has inherited the characteristics of computer cluster, which ensure the stability of service and application, release the server pressure dramatically, and make it more convenient to extend. Load balancing strategies and algorithms are one of the key technologies of cluster. The improvement of algorism is essential to promote cluster performance and stability, which is also the research focus of various research fields including WebGIS. The server virtualization, which has provided new methods for cluster construction, can be used to establish virtual cluster based on different servers and PCs much easily. Many scholars and researchers from GIS industries have put forward new methods and models to establish virtual WebGIS despite the fact that they all have their own advantages and limitations.
On the basis of the project supported by the national science and technology, this paper focuses on the WebGIS cluster application models and the key technology realization methods based on the server virtualization, lays emphasis on such important issues as WebGIS cluster model construction, virtual resource allocation technology, load balancing strategies and algorisms, and service and interface models and so on. On this basis, the structurally proper WebGIS cluster model has been designed; the dynamic allocation problem of virtual resource in cluster has been solved; the load balancing algorism according with the features of GIS application has been realized; and the simple and applicable WebGIS service and interface model has been designed.
The details of this study are stated as follows:
(1) In this study, the present research conditions and tendencies of the server virtualization, WebGIS and cluster technology have been analyzed, which concludes the advantages of virtualization and cluster in promoting hardware resource utilization rate, system performance and usability and so on. Therefore, it makes virtualization and cluster effective technical means in enhancing WebGIS cluster reliability and performance. At the same time, some problems and difficulties in virtualization and cluster development, such as performance loss and load scheduling and so forth, must be studied and solved.
(2) This paper then studies the structure model of WebGIS virtualization cluster. On the basis of studying the features and realization technologies of server virtualization, virtualization technology has turned out to be an effective method to improve the utilization rate of the server hardware, especially that of the processor. Under the current circumstances that the micro server based on multi-core processor has gained rapid development, the cluster system built on cheap server can deal with computational density problems of high time-space complexity, and can also be applied in GIS field. Analysis and comparison have been made between the cluster structures developed differently by national and international mainstream GIS platform providers. A structure model of WebGIS cluster has been proposed in connection with server virtualization technology. This model constructs cluster with1:1:1mapping among the numbers of processor core, OS, and GIS service instance. It has been compared with the solution of singular physical server with multi GIS service instance by experimental tests.
(3) This paper also studies the resource allocation technology of WebGIS virtualization cluster. By analyzing ordinary methods of virtual machine resource allocation and placement strategies of virtual machine, a dynamic allocation technology of recourse based on cluster load prediction has been proposed. The time series modeling of the medium and long term cluster load history has been realized. A set of meteorological data has been experimented to test and verify this modeling. Strategies and algorisms of the virtual machine placement timing have been designed on the basis of load prediction. The algorism of virtual machine placement target selection has been put forward based on the least first strategy. The above algorism procedure and false code have been described. The experimental scene based on allocation algorism has been designed and tested on meteorological application, and the allocation technology has been verified.
(4) The load balancing algorism of WebGIS virtualization cluster has been studied too. The features of genetic algorism and its application to load balancing scheduling have been studied at first, which indicates that it is adaptable and robust in WebGIS load balancing scheduling. According to the particularity and complexity of GIS algorism and application, the GIS task model has been constructed as the basis for genetic coding of genetic algorism based on the task priority and execution time. The genetic algorism of load balancing, including the fitness function, genetic operator and control parameter, has been designed, and algorism procedure has been formed. At last the algorism has been inserted in the load balancing module of WebGIS cluster to be experimented and tested.
(5) The service and interface model of WebGIS virtualization cluster has been studied further. On the basis of studying the problems of the spatial data sharing and service frameworks, the service and interface model of lightweight GIS cluster based on ROA and the cross-browser client model based on RIA have been put forward to solve the problems of heavy SOA structure and complicated data model, lowering the complexity of interface model so that the customers can use it at ease.
(6) At last the service prototype system of WebGIS virtualization cluster has been studied. The WebGIS cluster prototype framework based on server virtualization technology has been designed and constructed. The analysis and description of this prototype have been made from the aspect of specific technology accomplishment. Certain application in meteorological industry has been studied and experimented in order to put WebGIS virtualization cluster prototype into practice, make performance tests and verify the feasibility and rationality of cluster prototype.
网络GIS集群继承了计算机集群的特点,保证了服务和应用的稳定,极大的缓解了服务端压力,同时方便扩展。负载均衡策略和算法是集群的一项关键技术,算法的改进对提升集群性能和稳定性意义重大,也是各研究领域包括网络GIS的研究热点。服务器虚拟化技术提供了建设集群的新方法,利用它可以很便利的使用各类服务器和PC来构建虚拟集群,已经有不少学者和GIS厂商的研究人员提出了构建虚拟化网络GIS集群的方法和模型,各自有其特色和不足。
本文结合国家科技支撑计划项目“地理空间信息工具集网络服务平台研发”,主要围绕基于服务器虚拟化的网络GIS集群的应用模型和关键技术的实现方法,重点研究网络GIS集群模型的构建方法、虚拟资源的分配技术、负载均衡调度策略和算法、服务与接口模型等一系列关键问题。在此基础上设计结构合理的网络GIS集群模型,解决集群中虚拟资源的动态分配问题,实现符合GIS应用特点的负载均衡算法,设计简明实用的网络GIS服务与接口模型。
本文具体的研究工作如下：
(1)研究分析了服务器虚拟化技术、网络GIS以及集群技术的研究现状和发展趋势,指出虚拟化和集群在提高硬件资源利用率、系统性能和可用性等方面具有独特的优势,是提高网络GIS集群可靠性和性能的有效技术手段。同时虚拟化和集群发展中存在着一些问题和难点,例如性能损耗、负载调度等,都成为GIS集群优化工作所要研究和解决的问题。
(2)研究了虚拟化网络GIS集群结构模型。在研究服务器虚拟化的特点和实现的技术层次基础上,指出虚拟化技术是提高服务器硬件尤其是处理器利用率的有效手段,在基于多核处理器的微小型服务器迅速发展的当前,构建于廉价服务器之上的集群系统能够处理时空复杂度高的计算密集型问题,并可应用于GIS领域。以国内外主流GIS平台提供商的集群结构为研究目标做了分析和对比,结合服务器虚拟化技术,提出了一种网络GIS集群结构模型,该模型采用处理器核心、操作系统、GIS服务实例数量之间的1：1：1映射关系建立集群,并用实验测试的方法将其与单物理服务器多GIS服务实例的方案进行对比。
(3)研究了虚拟化网络GIS集群资源分配技术。分析了虚拟机资源分配的一般方法和虚拟机放置策略,提出了一种基于集群负载预测的资源动态分配技术。对集群的中长期历史负载进行了时间序列建模,并以一组气象数据为实验对象进行了预测建模实验,在负载预测的基础上设计了虚拟机放置时机决策的策略和算法,以及基于最少优先策略的虚拟机放置目标选择算法,并描述了上述算法流程和伪代码。设计了基于分配算法的实验场景并在气象应用上进行测试,对分配技术进行验证。
(4)研究了虚拟化网络GIS集群负载均衡算法。首先研究了遗传算法特点,以及它在负载均衡调度上的应用情况,表明它对网络GIS负载均衡调度同样具备适应性和和鲁棒性。根据GIS运算和应用的特殊性和复杂性,建立了基于任务优先级和执行时间的GIS任务模型,作为遗传算法基因编码依据,设计了用于负载均衡的遗传算法,包括适应度函数、遗传算子以及控制参数,并形成了算法流程。最后将算法插入网络GIS集群负载均衡模块进行了实验测试。
(5)研究了虚拟化网络GIS集群服务与接口模型。在研究空间数据共享问题和面向服务构架的问题基础上,针对SOA结构厚重,数据模型复杂的不足,提出了基于ROA的轻量级GIS集群服务与接口模型,以及基于RIA的跨浏览器的客户端模型,降低了接口模型复杂度,方便用户使用。
(6)研究了虚拟化网络GIS集群服务原型系统。设计并建立了基于服务器虚拟化技术的网络GIS集群原型框架,从具体技术实现的角度进行了原型的分析与描述。以气象行业某应用为研究和实验对象,将虚拟化网络GIS集群原型付诸实践,并进行性能测试,验证集群原型的可行性和合理性。
The combination of GIS and Internet has expanded new fields and new channels of GIS, and promoted the application and extension of geographic spatial information dramatically, which not only makes it more convenient to share the spatial information cross-regionally and cross-industrially, but also makes it more rapidly to develop the popularization of geographic information. The rapid development of WebGIS has been pushed forward owing to the great enhancement of the computer hardware performance, the promotion of new frameworks and new algorisms in software field, and continuous improvement of GIS subject. The unceasing penetration of the WebGIS application has brought about some new problems and challenges, for instance:the large numbers of Internet users and the mass data of GIS have made GIS application under great performance pressure; since software development lags behind hardware development at present, the role that new hardware plays in promoting WebGIS performance has been limited; the traditional WebGIS software and development model couldn't satisfy the demand of GIS rapid development.
WebGIS cluster has inherited the characteristics of computer cluster, which ensure the stability of service and application, release the server pressure dramatically, and make it more convenient to extend. Load balancing strategies and algorithms are one of the key technologies of cluster. The improvement of algorism is essential to promote cluster performance and stability, which is also the research focus of various research fields including WebGIS. The server virtualization, which has provided new methods for cluster construction, can be used to establish virtual cluster based on different servers and PCs much easily. Many scholars and researchers from GIS industries have put forward new methods and models to establish virtual WebGIS despite the fact that they all have their own advantages and limitations.
On the basis of the project supported by the national science and technology, this paper focuses on the WebGIS cluster application models and the key technology realization methods based on the server virtualization, lays emphasis on such important issues as WebGIS cluster model construction, virtual resource allocation technology, load balancing strategies and algorisms, and service and interface models and so on. On this basis, the structurally proper WebGIS cluster model has been designed; the dynamic allocation problem of virtual resource in cluster has been solved; the load balancing algorism according with the features of GIS application has been realized; and the simple and applicable WebGIS service and interface model has been designed.
The details of this study are stated as follows:
(1) In this study, the present research conditions and tendencies of the server virtualization, WebGIS and cluster technology have been analyzed, which concludes the advantages of virtualization and cluster in promoting hardware resource utilization rate, system performance and usability and so on. Therefore, it makes virtualization and cluster effective technical means in enhancing WebGIS cluster reliability and performance. At the same time, some problems and difficulties in virtualization and cluster development, such as performance loss and load scheduling and so forth, must be studied and solved.
(2) This paper then studies the structure model of WebGIS virtualization cluster. On the basis of studying the features and realization technologies of server virtualization, virtualization technology has turned out to be an effective method to improve the utilization rate of the server hardware, especially that of the processor. Under the current circumstances that the micro server based on multi-core processor has gained rapid development, the cluster system built on cheap server can deal with computational density problems of high time-space complexity, and can also be applied in GIS field. Analysis and comparison have been made between the cluster structures developed differently by national and international mainstream GIS platform providers. A structure model of WebGIS cluster has been proposed in connection with server virtualization technology. This model constructs cluster with1:1:1mapping among the numbers of processor core, OS, and GIS service instance. It has been compared with the solution of singular physical server with multi GIS service instance by experimental tests.
(3) This paper also studies the resource allocation technology of WebGIS virtualization cluster. By analyzing ordinary methods of virtual machine resource allocation and placement strategies of virtual machine, a dynamic allocation technology of recourse based on cluster load prediction has been proposed. The time series modeling of the medium and long term cluster load history has been realized. A set of meteorological data has been experimented to test and verify this modeling. Strategies and algorisms of the virtual machine placement timing have been designed on the basis of load prediction. The algorism of virtual machine placement target selection has been put forward based on the least first strategy. The above algorism procedure and false code have been described. The experimental scene based on allocation algorism has been designed and tested on meteorological application, and the allocation technology has been verified.
(4) The load balancing algorism of WebGIS virtualization cluster has been studied too. The features of genetic algorism and its application to load balancing scheduling have been studied at first, which indicates that it is adaptable and robust in WebGIS load balancing scheduling. According to the particularity and complexity of GIS algorism and application, the GIS task model has been constructed as the basis for genetic coding of genetic algorism based on the task priority and execution time. The genetic algorism of load balancing, including the fitness function, genetic operator and control parameter, has been designed, and algorism procedure has been formed. At last the algorism has been inserted in the load balancing module of WebGIS cluster to be experimented and tested.
(5) The service and interface model of WebGIS virtualization cluster has been studied further. On the basis of studying the problems of the spatial data sharing and service frameworks, the service and interface model of lightweight GIS cluster based on ROA and the cross-browser client model based on RIA have been put forward to solve the problems of heavy SOA structure and complicated data model, lowering the complexity of interface model so that the customers can use it at ease.
(6) At last the service prototype system of WebGIS virtualization cluster has been studied. The WebGIS cluster prototype framework based on server virtualization technology has been designed and constructed. The analysis and description of this prototype have been made from the aspect of specific technology accomplishment. Certain application in meteorological industry has been studied and experimented in order to put WebGIS virtualization cluster prototype into practice, make performance tests and verify the feasibility and rationality of cluster prototype.
引文
[1]以网络为中心计算的未来——记IBM总裁郭士纳的讲话[J].电子出版,1996,07：8-11.
[2]常乐,彰晖,陈宏盛,WebGIS应用系统设计[J],计算机工程,2001,27(4)
[3]李颖君.GIS技术与应用的现状和对策[A].湖南省测绘学会.全国测绘科技信息网中南分网第二十五次学术交流会论文集[C].湖南省测绘学会：,2011：4.
[4]Lucent Technologies. Web ProForum tutorial:DWDM. October 2009, http://www.webproforum.com/acrobat/dwdm.pdf
[5]Dan Herington, Virtual Server Environment.[M],2005
[6]虚拟化技术发展史,http://www.sina.com.cn2008年01月08日
[7]什么是虚拟化?,http://cio.it168.com/f/2007-01-24/200701241409097.shtml
[8]虚拟化,http://baike.baidu.com/view/729629.htm
[9]浅谈虚拟化技术与应用现状,http://club.topsage.com/thread-1755898-1-1.html
[10]服务器虚拟化应用现状调查报告,http://publish.itl68.com/2006/1025/20061025005604.shtml,http://server.itl68.com/a2008/ 1112/210/000000210743.shtml,http://virtual.it168.com/a2010/0128/844/000000844892_4.sht ml
[11]刘孟全.服务器虚拟化相关问题分析[A].广西计算机学会.广西计算机学会2009年年会论文集[C].广西计算机学会：,2009：2.
[12]服务器虚拟化的五大安全问题,http://it.sohu.com/20090601/n264268963.shtml
[13]R.Buyya.High Performance Cluster Computing:Architectures and Systems.[M].Upper Saddle River, NJ,USA:Prentice Hal PTR,1999.
[14]Linux HPC Cluster Installation, IBM Redbooks, http://www.redbooks.ibm.com/
[15]LINUX的集群系统简介及现状,http://www.stuhack.com/linux/jiqun/3.html
[16]TOP500, http://en.wikipedia.org/wiki/TOP500
[17]Linux优势凸现集群应用增加,ittp://group. vsharing.com/News/Diary.aspx?id= 10017
[18]Symmetric multiprocessing,http://en.wikipedia.org/wiki/Symmetric_multiprocessing
[19]Luiz Andre Barroso, Jeffrey Dean, and Urs Hdlzle, Web Search for a Planet:The Google Cluster Architecture, IEEE Micro, Vol.23, No.2, March 2003, pp.22-28.
[20]王映辉,王英杰,计算模式的演变与大规模软件构架技术[J],计算机工程与应用,2003,29
[21]唐建智,任应超,杨崇俊等,基于FastCGI的分布式集群WebGIS研究[J],计算机应用研究,2010,8
[22]刘志辉,吴凡,石绥祥等,基于ESRI系列产品实现地理信息的网上发布,测绘信息与工程,2002,3
[23]陈述彭,鲁学军,周成虎,地理信息系统导论[M],北京：科学出版社,1999.
[24]李久刚,唐新明,汪汇兵,刘正军.REST架构的WebGIS技术研究与实现[J].测绘科学,2011,(3).
[25]姚鹤岭,基REST的GIS Web服务研究[D],2005
[26]如何应对服务器虚拟化带来的五大难题?.http://hb.qq.com/a/20111017/000156.htm
[27]王恩泉,中国版Google Earth的空间数据组织与管理研究[D],2007
[28]罗显刚,数字地球三维空间信息服务关键技术研究[D],2010
[29]Service-oriented architecture,http://en.wikipedia.org/wiki/Service-oriented_architecture
[30]张锋叶,杨钰,朱美正.基于WebServices的GIS多层体系结构研究[J].计算机应用,2006,26(3)：749-751.
[31]REST, http://zh.wikipedia.org/zh-cn/REST
[32]Foster, I., Kesselman, C.. The Grid:Blueprint fora New Computing Infrastructure. Morgan Kaufmann Publishers, Inc.,San Francisco, CA,1999.
[33]方金云,何建邦,网格GIS体系结构及其实现技术[J],地球信息科学,2002(4),36-42
[34]云计算,http://zh.wikipedia.org/wiki/%E4%BA%91%E8%AE%A1%E7%AE%97
[35]ESRI,ArcGIS 10产品白皮书,2010,ESRI
[36]宋关福,三维GIS的困境与出路,2010(01)
[37]宋关福.地理信息系统平台软件技术展望[N].中国测绘报,2009-04-28006.
[38]吴边,吴信才.Cloud GIS关键技术研究[J].计算机工程与设计,2011,v.32,No.29204：1342-1346.
[39]Shvachko,K., Kuang, H., Radia,S.,The Hadoop distributed file system,2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010,2010,2010 IEEE 26th
[40]王晓川.自调度集群网络服务的研究与实现[D],长沙：国防科技大学,2006.
[41]章文嵩.可伸缩网络服务的研究与实现[D],长沙：国防科学技术大学,2000.
[42]ArcGIS Server, http://en.wikipedia.org/wiki/ArcGIS_Server.
[43]ArcGIS For Server, http://www.esri.com/software/arcgis/arcgisserver/index.html
[44]梁晓松,游雄,王珂珂.面向服务的ArcGIS Server架构研究[J].测绘科学,2009,(.3).
[45]SuperMap服务式GIS平台:SuperMap iServer,http://www.supermap.com.cn/html/sofewaresmall_26.html#
[46]叶雨.SuperMap服务器产品部署优化[A]..[C].:2009：491-497.
[47]张琦.多核系统中的程序性能优化研究[D].中国科学技术大学：中国科学技术大学,2010.
[48]Moore's law (摩尔定律),http://en.wikipedia.org/wiki/Moore%27s_law
[49]何军,王飙.通用多核处理器发展现状和趋势研究[A].张民选.国防科技大学出版社[C].：国防科技大学出版社,2011：41-47.
[50]NVIDIA White Paper, NVIDIA's Next Generation CUDA Compute Architecture: Fermi.2009.
[51]HuangKai,XuZhiwei,Scalable Parallel Computing:Technology,Architecture,Programming,Beijing:China Machine Press,1999
[52]陈国良.1994.并行算法的设计与分析[M].北京：高等教育出版社.
[53]Intel,多核多线程技术,1ittp://software.intel.com/zh-cn/articles/book-Multicore-Multithread-Technology/
[54]刘钰峰.现代操作系统调度策略研究[D].湖南大学：湖南大学,2002.
[55]操作系统对多核处理器的支持方 法,http://software.inte.com/zh-cn/articles/book-Multicore-Multithread-Technology_operating_system_support_for_multicore_processors_methods/
[56]Parallel Computing,http://en.wikipedia.org/wiki/Parallel_computing
[57]王辉,孙枫,刘希斌.GIS应用系统中复杂多边形并行处理技术[J].哈尔滨工程大学学报,1999,(5).
[58]项涵宇,刘婷,晏磊,潘征,刘岳峰.基于并行计算结构的实时交通地理信息系统技术研究[A]..[C].：,2008：649-657.
[59]靳华中,孟令奎,王显.集群环境下并行GIS的体系结构设计[J].地理空间信息,2005,(5).
[60]罗英伟,汪小林,熊国民,万钧,许卓群.GIS的合作与并行[A]..[C].:,2003：37-45.
[61]赵春宇.高性能并行GIS中矢量空间数据存取与处理关键技术研究[D],武汉大学：武汉大学,2006.
[62]商蕾,高孝洪.集群技术在Web_GIS领域中的应用[J].武汉理工大学学报(交通科学与工程版),2002,26(6)：822-825.
[63]商宏杰,马义忠,高彦.基于LVS的WebGIS集群系统的体现结构设计[J].中国科技论文在线,2008,(12)：1372-1376
[64]章华军,朱美正,李艳明.地理空间信息服务集群技术研究与实现[J].微计算机信息,2009,(34)：134-136.
[65]黄颖,郭明强,罗显刚,等.WebGIS中GIS服务器负载均衡研究与实现[J].测绘科学,2009,(1)：182-183,105.
[66]李忠民,喻占武,朱莉.基于空间数据内容的动态负载均衡方法[J].武汉大学学报(信息科学版),2009,(5).
[67]Holland,J. H. Outline for a logical theory of adaptive systems[J]. Journal of the Association for Computing Machinery,1962,9(3):297-314.
[68]Fogel, L. J. Owens, A.J., and Walsh, M. J. Artificial intelligence through simulated evolution. New York:John Wiley & Sons,1966.
[69]Holland, J. H. Adaptation in Natural and artificial system:An introductory analysis with applications to biology, control, and artificial intelligence[M].1st edition, Ann Arbor, M1; The University of Michigan Press,1975.
[70]李敏强,寇纪淞,林丹.遗传算法的基本理论与应用[M].北京：科学出版社.2002.
[71]Goldberg, D. E. Computer-aided gas pipeline operation using genetic algorithm and rule learning[M]. Doctoral dissertation, No.8402282, Department of Civil Engineering, University of Michigan,1983.
[72]Goldberg, D. E. Genetic Algorithm in Search, optimization and machine learning[M]. MA: Addsion-Weslery Publishing Company,1989.
[73]Davis, L. (ed.). Handbook of genetic algorithms[M]. Van Nostrand Reinhold, New York, 1991.
[74]Grefenstette, J. J. Optimization of control parameters for genetic algorithms[J]. IEEE Transaction on Systems, Man, and Cybernetics,1986,16(1):122-128.
[75]Bauer, R. J. Genetic algorithms and investment strategies[M]. New York:John Wiley & Sons, Inc.,1994.
[76]Srinivas, M., and Patnaik, L. M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Transaction on Systems, Man, and Cybernetics,1994,24(4):656-667.
[77]Koza, J.R., Genetic Programming:On the Programming of Computers by Means of Natural Selection[M], MIT Press,1992.
[78]Yeung, D. Y. A neural network approach to constructive induction[J]. In Proceedings of the 8th International Conference on Machine Learning, Evanston, IL:Morgan Kaufmann,1991: 228-232
[79]Muhlenbein, H., Schomisch, M., and Borm, J. The parallel genetic algorithm as function prtimizer. In Proceedings of the Fourth International Conference on Genetic Algorithms(ICGA), Belew, R. K., and Booker, L. B, Sna Mateo, CA:Morgan Kaufmann, 1991:271-278.
[80]Stender, J.(Ed). Parallel genetic algorithms:theory and application. Amsterdam:IOS Press, 1993.
[81]Kuo, T., and Hwang, S. A genetic algorithm with disruptive selection, IEEE Transactions on Systems, Man and Cybernetics Part B:Cybernetics,1996,26(2):65-69.
[82]Syswerda, G. Uniform crossover in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithm(ICGA 3), Schaffer, J.D., CA:Morgan Kaufmann Publishers,1989:2-9.
[83]Rechenberg, I. Evolutions strategies:Optimierung techniscner systeme nach prinzipien der biologischen evolution. Stuttgart:Frommann-Holzboog Verlag,1973.
[84]Hesser, J., and Manner, R. Towards an optimal mutation probability for genetic algorithms. In Proceedings of the first Workshop on Parallel Problem Solving From Nature,1990:23-32.
[85]Goldberg, D. E. Genetic Algorithm and Walsh Functions, Part 1&2. Complex Systems, 1989,3:129-171.
[86]Spears, W. M., and De Jong, K. A. Dining with Gas:operator lunch theorem. In[140],1997.
[87]戴晓明,陈治纲,冯瑞,茅雪飞,邵惠鹤.基于改进模式提取变异算子的遗传算法[J].上海交通大学学报,2002,(8).
[88]Baumgartner, J. P. and Cook D. J. Genetic-based solution to load balancing in parallel computers[J], Proceedings-ACM Computer Science Conference,1994,157-164.
[89]Lee, S. H. and Hwang, C. S. Dynamic load balancing approach using genetic algorithm in distributed systems[J]. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC,1998,639-644.
[90]Zomaya, A.Y. and Teh, Y. H. Observations on using genetic algorithms for dynamic load-balancing. IEEE Transactions on Parallel and Distributed Systems,2001,899-911.
[91]Sahoo, B., Mohapatra, S. and Jena, S. K. A genetic algorithm based dynamic load balancing scheme for heterogeneous distributed systems. Proceedings of the 2008 International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2008,2008,499-505.
[92]Mohammadzadeh, J.,Moeinzadeh, M. H.;,Sharifian, R. S. and Mahdavi, L. Scheduling dynamic load-balancing in parallel and distributed computers using modified genetic algorithm with time dependent fitness function. Proceedings-2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009,2009,894-898.
[93]Brinkhoff,T., Kriegel, H. P., Schneider,R. Comparison of approximations of complex objects used for approximation-based query processing in spatial database systems. Proceedings-International Conference on Data Engineering.1993,40-49.
[94]蒋江.异构集群系统中基于进程迁移机制的负载平衡算法的研究[D].国防科学技术大学：国防科学技术大学,2002.
[95]二八定律,http://baike.baidu.com/view/40591.htm.
[96]Arts,E. and Korst,J. Simulated annealing and boltzmann machine. New York:Wiley & Son, 1989.
[97]余燕芳,陆军.基于改进遗传算法的服务器端负载均衡算法[J].微电子学与计算机,2007,(7).
[98]刘阳,童小念.基于遗传模拟退火算法的网络负载均衡研究[J].计算机与数字工程,2008,(9).
[99]曹兰,梁梁,全秀祥.遗传算法在负载均衡系统中的应用研究[J].四川理工学院学报(自然科学版),2011,(1).
[100]朱莉,沈未名,李锐,徐胜勇.利用遗传算法的网络GIS集群服务器动态负载均衡算法[J].武汉大学学报(信息科学版),2011,(6).
[101]SuperMap OGDC,http://www.supermap.com.cn/html/sofewarebig_8.html.
[102]张晓东,陈华斌,池天河,张彦.应用Web服务技术实现GIS互操作[J].林业科学,2006,(S1).
[103]MapGIS IGSS, http://www.mapgis.com.cn/templates/ci2/index.aspx?nodeid=806&page=ContentPagee&conte ntid=2247.
[104]GML, http://en.wikipedia.org/wiki/Geography_Markup_Language.
[105]Web Service, http://en.wikipedia.org/wiki/Web_service.
[106]SO A, http://en.wikipedia.org/wiki/Service-oriented_architecture.
[107]ManojPaul, S.K. Ghosh. An Approach for Service Oriented Discovery and Retrieval of Spatial Data[A]. International Workshop on Service Oriented software Engineering[C]. Shanghai:ACMPress,2006:88-94.
[108]汪小林,庞潇,王海波,罗英伟.基于SOA的GIS应用设计与实现[A].[C].2009：169-181.
[109]熊自诚.基于SOA的Web GIS技术研究[D].武汉理工大学：武汉理工大学,2009.
[110]李圣文,龚君芳,吴信才.基于SOA的GIS应用框架[J].地球科学(中国地质大学学报),2010,(3).
[111]余萍,杨威,张蕾.基于SOA架构的电力GIS共享平台设计[J].河北电力技术,2010,(4).
[112]刘纪平,陶坤旺,王亮,侯婧熠.基于SOA的电子政务空间地理信息服务与实现[J].辽宁工程技术大学学报,2006,(S1).
[113]毛峰,刘婷,刘仁义,刘南,张丰.基于REST面向资源的地理信息服务设计[J].计算机 工程,2011,(8).
[114]姚刚.面向资源的Web GIS架构设计研究[A].中国地理学会百年庆典[C].：,2009：319.
[115]Open GIS Consortium, The OpenGIS Abstract Specification [EB/OL]. http://www.opengis.org/.
[116]Manoj Paul and S. K. Ghosh, An approach for service oriented discovery and retrieval of spatial data[C], Proceedings of the 2006 international workshop on Service-oriented software engineering,2006.
[117]陈述彭,鲁学军,周成虎.地理信息系统导论[M].科学出版社,1999.
[118]吴信才.地理信息系统原理与方法[M],电子工业出版社,2002.
[119]钟耳顺,刘利.我国地理信息产业现状分析[J].测绘科学.2008,33(001)：17-20.
[120]崔荣波,许晓兵,陆颖.一种虚拟化的服务器集群模型[J].计算机系统应用,2011,20(3)：173-177.
[121]辛军,陈康,郑纬民,虚拟化的集群资源管理技术研究[J].计算机科学与探索,2010,4(4)：324-329
[122]商蕾,高孝洪,集群技术在Web_GIS领域中的应用[J].武汉理工大学学报(交通科学与工程版),2002；26(6)：822-825.
[123]祝飞,李锐,喻占武.利用集群技术构建Web_GIS平台的研究[J].微计算机信息.2006.2(3)：193-195.
[124]贾树泽.并行算法在气象卫星地面应用系统中的运用[D].中国气象科学研究院：中国气象科学研究院,2009.
[125]雷霆.基于Rails的虚拟机集群监控系统的设计与实现[D].西安电子科技大学：西安电子科技大学,2010.
[126]贾树泽,杨军,施进明,赵金雁.新一代气象卫星资料处理系统并行调度算法研究与应用[J].气象科技,2010,(1).
[127]张超VMware虚拟化服务器的构建方法与展望[J].通信技术,2010,(9).
[128]黄方,刘定生,李国庆,曾怡,于文洋,王绍刚,刘鹏.基于网格的高性能地理信息系统初探[J].地理信息世界,2007,(4).
[129]马庆勋.网络空间实时信息处理关键技术研究与实现[D].中国地质大学(北京)：中国地质大学(北京),2007.
[130]贺全进.兰州市WebGIS系统设计与实现[D].兰州大学：兰州大学,2007.
[131]杨晓春,智明,秦国锋,李启炎.WebGIS服务的动态调度策略[A].刘晓平,张继福,李琳.中国科学技术大学出版社[C].：中国科学技术大学出版社,2006：.
[132]朱江,张立立,曾志明,钟耳顺.WebGIS服务器场的负载平衡算法设计[J].计算机工程,2006,(9).
[133]孟令奎.空间信息网络化存取的理论与关键技术研究[Z].湖北省：武汉大学.
[134]汪鹏.基于MapGIS的地图服务系统的设计与实现[D].中国地质大学：中国地质大学,2007.
[135]朱莉,沈未名,潘少明,李锐.集成缓存与预取的网络GIS体系结构研究[A]..Institute of Electrical and Electronics Engineers, Inc.[C].:Institute of Electrical and Electronics Engineers, Inc.,2010:.
[136]曾武,王浩,徐正全,潘少明.网络地理信息系统中的栅格数据集群缓存[J].华中科技大学学报(自然科学版),2009,(9).
[137]陈小平,李利军.WebGIS系统中决定用户响应速度的因素[J].计算机仿真,2003,(9).
[138]贺全进.兰州市WebGIS系统设计与实现[D].兰州大学：兰州大学,2007.
[139]Cardosa M, korupolu M R, Singh A. Shares and Urilities based Power Consolidation in Virtualized Server Enviroments. In:Porceedings of IFIP/IEEE Integrated Network Management.2009,327-334.
[140]Timothy Wood, Prashant J. Shenoy, Arun Venkataramani, Mazin S. Yousif.Black-box and Gray-box Strategies for Virtual Machine Migration. Symposium on Networked Systems Design and Implementation, NSDI 2007.
[141]Gueyoung Jung, Kaustubh R. Joshi, Matti A. Hiltunen, Richard D. Schlichting,Capon Pu. Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments. International Conference on Autonomic Computing,ICAO 2008.
[142]G Wei, AV Vasilakos, Y Zheng, N Xiong. A game-theoretic method of fair resource allocation for cloud computing services. The Journal of Supercomputing,2010-Springer.
[143]Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul,Christian Limpach, Ian Pratt, Andrew Warfield. Live Migration of Virtual Machines.2nd Symposium on Networked Systems Design and Implementation,NSDI2005.
[144]Anton Beloglazov, Rajkumar Buyya.2010. Energy Efficient Resource Management in Virtualized Cloud Data Centers. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID'10).
[145]Lagar-Cavilla H A, Whitney J A, Scannell A, et al. SnowFlock:Rapid Virtual Machine Cloning for Cloud Computing[C]. Proceeding of EuroSys 2009, Nuremberg, Germany,2009.
[146]. Vrable M, Ma J, Chen J, et al.Scalability, Fidelity, and Containment in the Potemkin Virtual Honeyfarm". Proceedings of the 20th ACM Symposium on Operating System Principles, Brighton, UK, October 2005.
[147]Jong-Geun Park, Jin-Mee Kim, Hoon Choi, Young-Choon Woo, Virtual machine migration in self-managing virtualized server environments, Advanced Communication Technology,2009. ICACT 2009.11th
[148]周文煜,陈华平,杨寿保,方君.基于虚拟机迁移的虚拟机集群资源调度[J],华中科技大学学报(自然科学版),2011,v.39；No.335S1：130-133.
[149]Akshat Verma, Puneet Ahuja, and Anindya Neogi.2008. pMapper:power and migration cost aware application placement in virtualized systems. In Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware (Middleware'08), Springer-Verlag New York, Inc., New York, NY, USA,243-264.
[150]Zhang H, Jung Uuo-Fei, Yoshihira K. Intelligent workload factoring for a hybrid cloud computing model//Proceedings of 2009 IEEE Congress on Services. Los Angeles,2009: 701-708.
[151]许伟,陈志刚,曾志文,华强胜.分布式系统中主机负载预测的一种普适性方法[J].计 算机工程与应用,2004,06：181-183+204.
[152]P Dinda, D O'Halloran. The Statistical Properties of Host Load[J]. Scientific Programming, Also available as CMU Technical Report CMU-CS-TR-98-175,1999.
[153]聚类分析,http://zh.wikipedia.org/wiki/聚类分析,2012.
[2]常乐,彰晖,陈宏盛,WebGIS应用系统设计[J],计算机工程,2001,27(4)
[3]李颖君.GIS技术与应用的现状和对策[A].湖南省测绘学会.全国测绘科技信息网中南分网第二十五次学术交流会论文集[C].湖南省测绘学会：,2011：4.
[4]Lucent Technologies. Web ProForum tutorial:DWDM. October 2009, http://www.webproforum.com/acrobat/dwdm.pdf
[5]Dan Herington, Virtual Server Environment.[M],2005
[6]虚拟化技术发展史,http://www.sina.com.cn2008年01月08日
[7]什么是虚拟化?,http://cio.it168.com/f/2007-01-24/200701241409097.shtml
[8]虚拟化,http://baike.baidu.com/view/729629.htm
[9]浅谈虚拟化技术与应用现状,http://club.topsage.com/thread-1755898-1-1.html
[10]服务器虚拟化应用现状调查报告,http://publish.itl68.com/2006/1025/20061025005604.shtml,http://server.itl68.com/a2008/ 1112/210/000000210743.shtml,http://virtual.it168.com/a2010/0128/844/000000844892_4.sht ml
[11]刘孟全.服务器虚拟化相关问题分析[A].广西计算机学会.广西计算机学会2009年年会论文集[C].广西计算机学会：,2009：2.
[12]服务器虚拟化的五大安全问题,http://it.sohu.com/20090601/n264268963.shtml
[13]R.Buyya.High Performance Cluster Computing:Architectures and Systems.[M].Upper Saddle River, NJ,USA:Prentice Hal PTR,1999.
[14]Linux HPC Cluster Installation, IBM Redbooks, http://www.redbooks.ibm.com/
[15]LINUX的集群系统简介及现状,http://www.stuhack.com/linux/jiqun/3.html
[16]TOP500, http://en.wikipedia.org/wiki/TOP500
[17]Linux优势凸现集群应用增加,ittp://group. vsharing.com/News/Diary.aspx?id= 10017
[18]Symmetric multiprocessing,http://en.wikipedia.org/wiki/Symmetric_multiprocessing
[19]Luiz Andre Barroso, Jeffrey Dean, and Urs Hdlzle, Web Search for a Planet:The Google Cluster Architecture, IEEE Micro, Vol.23, No.2, March 2003, pp.22-28.
[20]王映辉,王英杰,计算模式的演变与大规模软件构架技术[J],计算机工程与应用,2003,29
[21]唐建智,任应超,杨崇俊等,基于FastCGI的分布式集群WebGIS研究[J],计算机应用研究,2010,8
[22]刘志辉,吴凡,石绥祥等,基于ESRI系列产品实现地理信息的网上发布,测绘信息与工程,2002,3
[23]陈述彭,鲁学军,周成虎,地理信息系统导论[M],北京：科学出版社,1999.
[24]李久刚,唐新明,汪汇兵,刘正军.REST架构的WebGIS技术研究与实现[J].测绘科学,2011,(3).
[25]姚鹤岭,基REST的GIS Web服务研究[D],2005
[26]如何应对服务器虚拟化带来的五大难题?.http://hb.qq.com/a/20111017/000156.htm
[27]王恩泉,中国版Google Earth的空间数据组织与管理研究[D],2007
[28]罗显刚,数字地球三维空间信息服务关键技术研究[D],2010
[29]Service-oriented architecture,http://en.wikipedia.org/wiki/Service-oriented_architecture
[30]张锋叶,杨钰,朱美正.基于WebServices的GIS多层体系结构研究[J].计算机应用,2006,26(3)：749-751.
[31]REST, http://zh.wikipedia.org/zh-cn/REST
[32]Foster, I., Kesselman, C.. The Grid:Blueprint fora New Computing Infrastructure. Morgan Kaufmann Publishers, Inc.,San Francisco, CA,1999.
[33]方金云,何建邦,网格GIS体系结构及其实现技术[J],地球信息科学,2002(4),36-42
[34]云计算,http://zh.wikipedia.org/wiki/%E4%BA%91%E8%AE%A1%E7%AE%97
[35]ESRI,ArcGIS 10产品白皮书,2010,ESRI
[36]宋关福,三维GIS的困境与出路,2010(01)
[37]宋关福.地理信息系统平台软件技术展望[N].中国测绘报,2009-04-28006.
[38]吴边,吴信才.Cloud GIS关键技术研究[J].计算机工程与设计,2011,v.32,No.29204：1342-1346.
[39]Shvachko,K., Kuang, H., Radia,S.,The Hadoop distributed file system,2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010,2010,2010 IEEE 26th
[40]王晓川.自调度集群网络服务的研究与实现[D],长沙：国防科技大学,2006.
[41]章文嵩.可伸缩网络服务的研究与实现[D],长沙：国防科学技术大学,2000.
[42]ArcGIS Server, http://en.wikipedia.org/wiki/ArcGIS_Server.
[43]ArcGIS For Server, http://www.esri.com/software/arcgis/arcgisserver/index.html
[44]梁晓松,游雄,王珂珂.面向服务的ArcGIS Server架构研究[J].测绘科学,2009,(.3).
[45]SuperMap服务式GIS平台:SuperMap iServer,http://www.supermap.com.cn/html/sofewaresmall_26.html#
[46]叶雨.SuperMap服务器产品部署优化[A]..[C].:2009：491-497.
[47]张琦.多核系统中的程序性能优化研究[D].中国科学技术大学：中国科学技术大学,2010.
[48]Moore's law (摩尔定律),http://en.wikipedia.org/wiki/Moore%27s_law
[49]何军,王飙.通用多核处理器发展现状和趋势研究[A].张民选.国防科技大学出版社[C].：国防科技大学出版社,2011：41-47.
[50]NVIDIA White Paper, NVIDIA's Next Generation CUDA Compute Architecture: Fermi.2009.
[51]HuangKai,XuZhiwei,Scalable Parallel Computing:Technology,Architecture,Programming,Beijing:China Machine Press,1999
[52]陈国良.1994.并行算法的设计与分析[M].北京：高等教育出版社.
[53]Intel,多核多线程技术,1ittp://software.intel.com/zh-cn/articles/book-Multicore-Multithread-Technology/
[54]刘钰峰.现代操作系统调度策略研究[D].湖南大学：湖南大学,2002.
[55]操作系统对多核处理器的支持方 法,http://software.inte.com/zh-cn/articles/book-Multicore-Multithread-Technology_operating_system_support_for_multicore_processors_methods/
[56]Parallel Computing,http://en.wikipedia.org/wiki/Parallel_computing
[57]王辉,孙枫,刘希斌.GIS应用系统中复杂多边形并行处理技术[J].哈尔滨工程大学学报,1999,(5).
[58]项涵宇,刘婷,晏磊,潘征,刘岳峰.基于并行计算结构的实时交通地理信息系统技术研究[A]..[C].：,2008：649-657.
[59]靳华中,孟令奎,王显.集群环境下并行GIS的体系结构设计[J].地理空间信息,2005,(5).
[60]罗英伟,汪小林,熊国民,万钧,许卓群.GIS的合作与并行[A]..[C].:,2003：37-45.
[61]赵春宇.高性能并行GIS中矢量空间数据存取与处理关键技术研究[D],武汉大学：武汉大学,2006.
[62]商蕾,高孝洪.集群技术在Web_GIS领域中的应用[J].武汉理工大学学报(交通科学与工程版),2002,26(6)：822-825.
[63]商宏杰,马义忠,高彦.基于LVS的WebGIS集群系统的体现结构设计[J].中国科技论文在线,2008,(12)：1372-1376
[64]章华军,朱美正,李艳明.地理空间信息服务集群技术研究与实现[J].微计算机信息,2009,(34)：134-136.
[65]黄颖,郭明强,罗显刚,等.WebGIS中GIS服务器负载均衡研究与实现[J].测绘科学,2009,(1)：182-183,105.
[66]李忠民,喻占武,朱莉.基于空间数据内容的动态负载均衡方法[J].武汉大学学报(信息科学版),2009,(5).
[67]Holland,J. H. Outline for a logical theory of adaptive systems[J]. Journal of the Association for Computing Machinery,1962,9(3):297-314.
[68]Fogel, L. J. Owens, A.J., and Walsh, M. J. Artificial intelligence through simulated evolution. New York:John Wiley & Sons,1966.
[69]Holland, J. H. Adaptation in Natural and artificial system:An introductory analysis with applications to biology, control, and artificial intelligence[M].1st edition, Ann Arbor, M1; The University of Michigan Press,1975.
[70]李敏强,寇纪淞,林丹.遗传算法的基本理论与应用[M].北京：科学出版社.2002.
[71]Goldberg, D. E. Computer-aided gas pipeline operation using genetic algorithm and rule learning[M]. Doctoral dissertation, No.8402282, Department of Civil Engineering, University of Michigan,1983.
[72]Goldberg, D. E. Genetic Algorithm in Search, optimization and machine learning[M]. MA: Addsion-Weslery Publishing Company,1989.
[73]Davis, L. (ed.). Handbook of genetic algorithms[M]. Van Nostrand Reinhold, New York, 1991.
[74]Grefenstette, J. J. Optimization of control parameters for genetic algorithms[J]. IEEE Transaction on Systems, Man, and Cybernetics,1986,16(1):122-128.
[75]Bauer, R. J. Genetic algorithms and investment strategies[M]. New York:John Wiley & Sons, Inc.,1994.
[76]Srinivas, M., and Patnaik, L. M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Transaction on Systems, Man, and Cybernetics,1994,24(4):656-667.
[77]Koza, J.R., Genetic Programming:On the Programming of Computers by Means of Natural Selection[M], MIT Press,1992.
[78]Yeung, D. Y. A neural network approach to constructive induction[J]. In Proceedings of the 8th International Conference on Machine Learning, Evanston, IL:Morgan Kaufmann,1991: 228-232
[79]Muhlenbein, H., Schomisch, M., and Borm, J. The parallel genetic algorithm as function prtimizer. In Proceedings of the Fourth International Conference on Genetic Algorithms(ICGA), Belew, R. K., and Booker, L. B, Sna Mateo, CA:Morgan Kaufmann, 1991:271-278.
[80]Stender, J.(Ed). Parallel genetic algorithms:theory and application. Amsterdam:IOS Press, 1993.
[81]Kuo, T., and Hwang, S. A genetic algorithm with disruptive selection, IEEE Transactions on Systems, Man and Cybernetics Part B:Cybernetics,1996,26(2):65-69.
[82]Syswerda, G. Uniform crossover in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithm(ICGA 3), Schaffer, J.D., CA:Morgan Kaufmann Publishers,1989:2-9.
[83]Rechenberg, I. Evolutions strategies:Optimierung techniscner systeme nach prinzipien der biologischen evolution. Stuttgart:Frommann-Holzboog Verlag,1973.
[84]Hesser, J., and Manner, R. Towards an optimal mutation probability for genetic algorithms. In Proceedings of the first Workshop on Parallel Problem Solving From Nature,1990:23-32.
[85]Goldberg, D. E. Genetic Algorithm and Walsh Functions, Part 1&2. Complex Systems, 1989,3:129-171.
[86]Spears, W. M., and De Jong, K. A. Dining with Gas:operator lunch theorem. In[140],1997.
[87]戴晓明,陈治纲,冯瑞,茅雪飞,邵惠鹤.基于改进模式提取变异算子的遗传算法[J].上海交通大学学报,2002,(8).
[88]Baumgartner, J. P. and Cook D. J. Genetic-based solution to load balancing in parallel computers[J], Proceedings-ACM Computer Science Conference,1994,157-164.
[89]Lee, S. H. and Hwang, C. S. Dynamic load balancing approach using genetic algorithm in distributed systems[J]. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC,1998,639-644.
[90]Zomaya, A.Y. and Teh, Y. H. Observations on using genetic algorithms for dynamic load-balancing. IEEE Transactions on Parallel and Distributed Systems,2001,899-911.
[91]Sahoo, B., Mohapatra, S. and Jena, S. K. A genetic algorithm based dynamic load balancing scheme for heterogeneous distributed systems. Proceedings of the 2008 International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2008,2008,499-505.
[92]Mohammadzadeh, J.,Moeinzadeh, M. H.;,Sharifian, R. S. and Mahdavi, L. Scheduling dynamic load-balancing in parallel and distributed computers using modified genetic algorithm with time dependent fitness function. Proceedings-2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009,2009,894-898.
[93]Brinkhoff,T., Kriegel, H. P., Schneider,R. Comparison of approximations of complex objects used for approximation-based query processing in spatial database systems. Proceedings-International Conference on Data Engineering.1993,40-49.
[94]蒋江.异构集群系统中基于进程迁移机制的负载平衡算法的研究[D].国防科学技术大学：国防科学技术大学,2002.
[95]二八定律,http://baike.baidu.com/view/40591.htm.
[96]Arts,E. and Korst,J. Simulated annealing and boltzmann machine. New York:Wiley & Son, 1989.
[97]余燕芳,陆军.基于改进遗传算法的服务器端负载均衡算法[J].微电子学与计算机,2007,(7).
[98]刘阳,童小念.基于遗传模拟退火算法的网络负载均衡研究[J].计算机与数字工程,2008,(9).
[99]曹兰,梁梁,全秀祥.遗传算法在负载均衡系统中的应用研究[J].四川理工学院学报(自然科学版),2011,(1).
[100]朱莉,沈未名,李锐,徐胜勇.利用遗传算法的网络GIS集群服务器动态负载均衡算法[J].武汉大学学报(信息科学版),2011,(6).
[101]SuperMap OGDC,http://www.supermap.com.cn/html/sofewarebig_8.html.
[102]张晓东,陈华斌,池天河,张彦.应用Web服务技术实现GIS互操作[J].林业科学,2006,(S1).
[103]MapGIS IGSS, http://www.mapgis.com.cn/templates/ci2/index.aspx?nodeid=806&page=ContentPagee&conte ntid=2247.
[104]GML, http://en.wikipedia.org/wiki/Geography_Markup_Language.
[105]Web Service, http://en.wikipedia.org/wiki/Web_service.
[106]SO A, http://en.wikipedia.org/wiki/Service-oriented_architecture.
[107]ManojPaul, S.K. Ghosh. An Approach for Service Oriented Discovery and Retrieval of Spatial Data[A]. International Workshop on Service Oriented software Engineering[C]. Shanghai:ACMPress,2006:88-94.
[108]汪小林,庞潇,王海波,罗英伟.基于SOA的GIS应用设计与实现[A].[C].2009：169-181.
[109]熊自诚.基于SOA的Web GIS技术研究[D].武汉理工大学：武汉理工大学,2009.
[110]李圣文,龚君芳,吴信才.基于SOA的GIS应用框架[J].地球科学(中国地质大学学报),2010,(3).
[111]余萍,杨威,张蕾.基于SOA架构的电力GIS共享平台设计[J].河北电力技术,2010,(4).
[112]刘纪平,陶坤旺,王亮,侯婧熠.基于SOA的电子政务空间地理信息服务与实现[J].辽宁工程技术大学学报,2006,(S1).
[113]毛峰,刘婷,刘仁义,刘南,张丰.基于REST面向资源的地理信息服务设计[J].计算机 工程,2011,(8).
[114]姚刚.面向资源的Web GIS架构设计研究[A].中国地理学会百年庆典[C].：,2009：319.
[115]Open GIS Consortium, The OpenGIS Abstract Specification [EB/OL]. http://www.opengis.org/.
[116]Manoj Paul and S. K. Ghosh, An approach for service oriented discovery and retrieval of spatial data[C], Proceedings of the 2006 international workshop on Service-oriented software engineering,2006.
[117]陈述彭,鲁学军,周成虎.地理信息系统导论[M].科学出版社,1999.
[118]吴信才.地理信息系统原理与方法[M],电子工业出版社,2002.
[119]钟耳顺,刘利.我国地理信息产业现状分析[J].测绘科学.2008,33(001)：17-20.
[120]崔荣波,许晓兵,陆颖.一种虚拟化的服务器集群模型[J].计算机系统应用,2011,20(3)：173-177.
[121]辛军,陈康,郑纬民,虚拟化的集群资源管理技术研究[J].计算机科学与探索,2010,4(4)：324-329
[122]商蕾,高孝洪,集群技术在Web_GIS领域中的应用[J].武汉理工大学学报(交通科学与工程版),2002；26(6)：822-825.
[123]祝飞,李锐,喻占武.利用集群技术构建Web_GIS平台的研究[J].微计算机信息.2006.2(3)：193-195.
[124]贾树泽.并行算法在气象卫星地面应用系统中的运用[D].中国气象科学研究院：中国气象科学研究院,2009.
[125]雷霆.基于Rails的虚拟机集群监控系统的设计与实现[D].西安电子科技大学：西安电子科技大学,2010.
[126]贾树泽,杨军,施进明,赵金雁.新一代气象卫星资料处理系统并行调度算法研究与应用[J].气象科技,2010,(1).
[127]张超VMware虚拟化服务器的构建方法与展望[J].通信技术,2010,(9).
[128]黄方,刘定生,李国庆,曾怡,于文洋,王绍刚,刘鹏.基于网格的高性能地理信息系统初探[J].地理信息世界,2007,(4).
[129]马庆勋.网络空间实时信息处理关键技术研究与实现[D].中国地质大学(北京)：中国地质大学(北京),2007.
[130]贺全进.兰州市WebGIS系统设计与实现[D].兰州大学：兰州大学,2007.
[131]杨晓春,智明,秦国锋,李启炎.WebGIS服务的动态调度策略[A].刘晓平,张继福,李琳.中国科学技术大学出版社[C].：中国科学技术大学出版社,2006：.
[132]朱江,张立立,曾志明,钟耳顺.WebGIS服务器场的负载平衡算法设计[J].计算机工程,2006,(9).
[133]孟令奎.空间信息网络化存取的理论与关键技术研究[Z].湖北省：武汉大学.
[134]汪鹏.基于MapGIS的地图服务系统的设计与实现[D].中国地质大学：中国地质大学,2007.
[135]朱莉,沈未名,潘少明,李锐.集成缓存与预取的网络GIS体系结构研究[A]..Institute of Electrical and Electronics Engineers, Inc.[C].:Institute of Electrical and Electronics Engineers, Inc.,2010:.
[136]曾武,王浩,徐正全,潘少明.网络地理信息系统中的栅格数据集群缓存[J].华中科技大学学报(自然科学版),2009,(9).
[137]陈小平,李利军.WebGIS系统中决定用户响应速度的因素[J].计算机仿真,2003,(9).
[138]贺全进.兰州市WebGIS系统设计与实现[D].兰州大学：兰州大学,2007.
[139]Cardosa M, korupolu M R, Singh A. Shares and Urilities based Power Consolidation in Virtualized Server Enviroments. In:Porceedings of IFIP/IEEE Integrated Network Management.2009,327-334.
[140]Timothy Wood, Prashant J. Shenoy, Arun Venkataramani, Mazin S. Yousif.Black-box and Gray-box Strategies for Virtual Machine Migration. Symposium on Networked Systems Design and Implementation, NSDI 2007.
[141]Gueyoung Jung, Kaustubh R. Joshi, Matti A. Hiltunen, Richard D. Schlichting,Capon Pu. Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments. International Conference on Autonomic Computing,ICAO 2008.
[142]G Wei, AV Vasilakos, Y Zheng, N Xiong. A game-theoretic method of fair resource allocation for cloud computing services. The Journal of Supercomputing,2010-Springer.
[143]Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul,Christian Limpach, Ian Pratt, Andrew Warfield. Live Migration of Virtual Machines.2nd Symposium on Networked Systems Design and Implementation,NSDI2005.
[144]Anton Beloglazov, Rajkumar Buyya.2010. Energy Efficient Resource Management in Virtualized Cloud Data Centers. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID'10).
[145]Lagar-Cavilla H A, Whitney J A, Scannell A, et al. SnowFlock:Rapid Virtual Machine Cloning for Cloud Computing[C]. Proceeding of EuroSys 2009, Nuremberg, Germany,2009.
[146]. Vrable M, Ma J, Chen J, et al.Scalability, Fidelity, and Containment in the Potemkin Virtual Honeyfarm". Proceedings of the 20th ACM Symposium on Operating System Principles, Brighton, UK, October 2005.
[147]Jong-Geun Park, Jin-Mee Kim, Hoon Choi, Young-Choon Woo, Virtual machine migration in self-managing virtualized server environments, Advanced Communication Technology,2009. ICACT 2009.11th
[148]周文煜,陈华平,杨寿保,方君.基于虚拟机迁移的虚拟机集群资源调度[J],华中科技大学学报(自然科学版),2011,v.39；No.335S1：130-133.
[149]Akshat Verma, Puneet Ahuja, and Anindya Neogi.2008. pMapper:power and migration cost aware application placement in virtualized systems. In Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware (Middleware'08), Springer-Verlag New York, Inc., New York, NY, USA,243-264.
[150]Zhang H, Jung Uuo-Fei, Yoshihira K. Intelligent workload factoring for a hybrid cloud computing model//Proceedings of 2009 IEEE Congress on Services. Los Angeles,2009: 701-708.
[151]许伟,陈志刚,曾志文,华强胜.分布式系统中主机负载预测的一种普适性方法[J].计 算机工程与应用,2004,06：181-183+204.
[152]P Dinda, D O'Halloran. The Statistical Properties of Host Load[J]. Scientific Programming, Also available as CMU Technical Report CMU-CS-TR-98-175,1999.
[153]聚类分析,http://zh.wikipedia.org/wiki/聚类分析,2012.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |