移动网格中基于层次模型的移动节点预测
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摘要
网格计算自从第一次被用到科学和商业领域到现在已经取得了长足的进步。相信网格计算的下一个阶段是将网格服务提供给资源受限的设备,例如手持设备、PDA、智能手机、传感器等。网格和移动计算的结合被称为“移动网格”,由于无线通信和移动计算技术的快速发展,它已经成为一个新的研究领域。但是,将移动设备整合到网格中将会面临很多困难。特别是移动设备自身的局限性,比如低带宽、低处理能力、电池容量不足、频繁的断线,还有定位问题。
如今,软件密集型的系统逐渐受到分散、资源受限、嵌入式、自动化、移动计算环境的影响。在这篇论文中提出了GridLite软件架构,它基于网格平台并且适用于移动环境。GridLite是一个扩展的框架,它为在网格的基础设施上位用户提供了良好的服务。GridLite框架提出的一个目的是定义一个网格结构来管理设备,这些设备的自身缺陷在该框架下可以得到最大限度的弥补。GridLite是以服务为核心的,不同的服务可以管理不同的资源。本文在已有网格的基础上提出了一种可行性较高的GridLite原型,它融合了OODT和Prism-MW体系结构中间件的优点,使之能更适合于移动环境。总的说来,GridLite的最终目的是将现有的网格技术扩展到人们的“口袋”中,前人所做的研究和经验也表明这个目标是可以达到,而且是值得进一步往下做的。
从一个更小的方面来看,移动设备能随时改变其位置。网格代理必须知道移动设备的位置以便能有效的使用移动设备。因此,网格代理和移动设备间需要进行频繁的通信。当移动节点频繁的改变位置时,网格代理和移动设备之间的通信更加频繁。在低带宽的网络环境下,这当然也就为移动网格系统增加了不少负担。但是,如果减少移动节点和网格代理之间的通信,那么移动节点位置的精度就会降低,如何在这两者之间寻求一种平衡也是需要具体的环境来定。本文提出了用自适应距离过滤来有效减少移动节点和网格代理之间的通信机制。这个过滤器首先依据网格节点的移动方向和速度来对移动节点分簇,然后再对每个簇依据不同的值过滤节点位置信息。然而,在网格代理端移动位置更新的减少必然会产生位置误差,即代理端记录的位置与物体的实际位置有差别。为了解决这个问题,可以在移动端模拟实际物体的运动轨迹。为了对整个算法的性能评估,本文用NS2仿真工具做了模拟。
本文得到国家自然科学基金项目(批准号：60773211,60970064)、国家软件开发环境重点实验室开放基金课题(批准号:SKLSDE-2009KF-2-02)、新世纪优秀人才支持计划(批准号:NCET-08-0806)、霍英东高校青年教师基金基础性研究课题(批准号：121067)和湖北省杰出青年人才基金(批准号：2008CDB335)的资助、武汉市科技攻关项目(批准号：201010621207)。
Grid computing has made rapid strides during the last few years from their first use in the scientific computing domain to enterprise Grids deploying commercial applications. We believe the next phase of Grid computing will deal with making grid services available to resource constrained appliances such as handheld devices, PDAs, smartphones, and sensors to name a few. Integration between the grid and mobile computing known as the "Mobile Grid," is becoming the new research issue due to the rapid development of radio communications and mobile computing technology. However, to integrate mobile devices with the Grid, mobile devices face some constrained conditions. Specifically, mobile devices experiences low bandwidth, low processing power, low battery capacity, frequent disconnectivity, and relocation issues.
The software-intensive systems of today are increasingly shaped by their decentralized, resource-constrained, embedded, autonomic, and mobile (DREAM) computing environments. In this thesis we present GridLite, a software architecture based grid platform suitable for deployment in DREAM environments. GridLite is an extensible framework that provides services to users on ubiquitous, resource-limited devices within a Grid infrastructure. It uses a server infrastructure for provisioning of persistent services, and smart helper services running on the "lite" devices which tap into this infrastructure. One of the goals of GridLite research is to define a Grid architecture which manages these devices such that their resource constraints are minimized by the intelligent Grid infrastructure. This is done by defining new services for managing various resources. Our prototype implementation of GridLite represents an effective and highly effcient marriage of our OODT data grid and Prism-MW architectural middleware solutions. The ultimate goal of GridLite is to extend the reach of the grid all the way to people's "pockets". Our initial experience suggests that this goal is achievable and worthy of furtheractive pursuit.
Mobile devices can change their location at any time. The grid broker must know the location of mobile devices in order to use mobile devices as a part of grid resources. Thus, frequent communication between the grid broker and mobile devices is required. As frequent location changes of the MN (Mobile Node) occur, frequent communication between the grid broker and MN is needed. This operation increases the system load of the mobile grid in a limited bandwidth environment. Therefore, a tradeoff between a reduction of in communication traffic and the precise location accuracy of the MN is needed in the mobile grid due to the limited operating environment of the MN. Therefore, this thesis proposes an adaptive distance filter that can effectively reduce communication traffic between the mobile grid node and grid broker. This filter constructs clusters based on the mobility and velocity of the grid node and filters the location updates. However, the reduction of location updates generates location errors,whick occur when the grid broker cannot acquire the exact location of mobile nodes. To solve this problem,if the location updates are filtered,the grid broker can estimate method. For the performance evaluation of the adaptive distance filter,we modeled the mobility of the grid nodes by NS2.
This thesis is supported by National Natural Science Foundation of China (No: 60773211,60970064), Open Fund of the State Key Laboratory of Software Development Environment (No:SKLSDE-2009KF-2-02),New Century Excellent Talents in university (No:NCET-08-0806)Fok Ying-Tong Education Foundation for Young Teachers in Higher Education Institutions of China (No:121067) and the National Science Foundation of HuBei Province under Grant No.2008CDB335,NSF of Wuhan Municipality (No:201010621207).
如今,软件密集型的系统逐渐受到分散、资源受限、嵌入式、自动化、移动计算环境的影响。在这篇论文中提出了GridLite软件架构,它基于网格平台并且适用于移动环境。GridLite是一个扩展的框架,它为在网格的基础设施上位用户提供了良好的服务。GridLite框架提出的一个目的是定义一个网格结构来管理设备,这些设备的自身缺陷在该框架下可以得到最大限度的弥补。GridLite是以服务为核心的,不同的服务可以管理不同的资源。本文在已有网格的基础上提出了一种可行性较高的GridLite原型,它融合了OODT和Prism-MW体系结构中间件的优点,使之能更适合于移动环境。总的说来,GridLite的最终目的是将现有的网格技术扩展到人们的“口袋”中,前人所做的研究和经验也表明这个目标是可以达到,而且是值得进一步往下做的。
从一个更小的方面来看,移动设备能随时改变其位置。网格代理必须知道移动设备的位置以便能有效的使用移动设备。因此,网格代理和移动设备间需要进行频繁的通信。当移动节点频繁的改变位置时,网格代理和移动设备之间的通信更加频繁。在低带宽的网络环境下,这当然也就为移动网格系统增加了不少负担。但是,如果减少移动节点和网格代理之间的通信,那么移动节点位置的精度就会降低,如何在这两者之间寻求一种平衡也是需要具体的环境来定。本文提出了用自适应距离过滤来有效减少移动节点和网格代理之间的通信机制。这个过滤器首先依据网格节点的移动方向和速度来对移动节点分簇,然后再对每个簇依据不同的值过滤节点位置信息。然而,在网格代理端移动位置更新的减少必然会产生位置误差,即代理端记录的位置与物体的实际位置有差别。为了解决这个问题,可以在移动端模拟实际物体的运动轨迹。为了对整个算法的性能评估,本文用NS2仿真工具做了模拟。
本文得到国家自然科学基金项目(批准号：60773211,60970064)、国家软件开发环境重点实验室开放基金课题(批准号:SKLSDE-2009KF-2-02)、新世纪优秀人才支持计划(批准号:NCET-08-0806)、霍英东高校青年教师基金基础性研究课题(批准号：121067)和湖北省杰出青年人才基金(批准号：2008CDB335)的资助、武汉市科技攻关项目(批准号：201010621207)。
Grid computing has made rapid strides during the last few years from their first use in the scientific computing domain to enterprise Grids deploying commercial applications. We believe the next phase of Grid computing will deal with making grid services available to resource constrained appliances such as handheld devices, PDAs, smartphones, and sensors to name a few. Integration between the grid and mobile computing known as the "Mobile Grid," is becoming the new research issue due to the rapid development of radio communications and mobile computing technology. However, to integrate mobile devices with the Grid, mobile devices face some constrained conditions. Specifically, mobile devices experiences low bandwidth, low processing power, low battery capacity, frequent disconnectivity, and relocation issues.
The software-intensive systems of today are increasingly shaped by their decentralized, resource-constrained, embedded, autonomic, and mobile (DREAM) computing environments. In this thesis we present GridLite, a software architecture based grid platform suitable for deployment in DREAM environments. GridLite is an extensible framework that provides services to users on ubiquitous, resource-limited devices within a Grid infrastructure. It uses a server infrastructure for provisioning of persistent services, and smart helper services running on the "lite" devices which tap into this infrastructure. One of the goals of GridLite research is to define a Grid architecture which manages these devices such that their resource constraints are minimized by the intelligent Grid infrastructure. This is done by defining new services for managing various resources. Our prototype implementation of GridLite represents an effective and highly effcient marriage of our OODT data grid and Prism-MW architectural middleware solutions. The ultimate goal of GridLite is to extend the reach of the grid all the way to people's "pockets". Our initial experience suggests that this goal is achievable and worthy of furtheractive pursuit.
Mobile devices can change their location at any time. The grid broker must know the location of mobile devices in order to use mobile devices as a part of grid resources. Thus, frequent communication between the grid broker and mobile devices is required. As frequent location changes of the MN (Mobile Node) occur, frequent communication between the grid broker and MN is needed. This operation increases the system load of the mobile grid in a limited bandwidth environment. Therefore, a tradeoff between a reduction of in communication traffic and the precise location accuracy of the MN is needed in the mobile grid due to the limited operating environment of the MN. Therefore, this thesis proposes an adaptive distance filter that can effectively reduce communication traffic between the mobile grid node and grid broker. This filter constructs clusters based on the mobility and velocity of the grid node and filters the location updates. However, the reduction of location updates generates location errors,whick occur when the grid broker cannot acquire the exact location of mobile nodes. To solve this problem,if the location updates are filtered,the grid broker can estimate method. For the performance evaluation of the adaptive distance filter,we modeled the mobility of the grid nodes by NS2.
This thesis is supported by National Natural Science Foundation of China (No: 60773211,60970064), Open Fund of the State Key Laboratory of Software Development Environment (No:SKLSDE-2009KF-2-02),New Century Excellent Talents in university (No:NCET-08-0806)Fok Ying-Tong Education Foundation for Young Teachers in Higher Education Institutions of China (No:121067) and the National Science Foundation of HuBei Province under Grant No.2008CDB335,NSF of Wuhan Municipality (No:201010621207).
引文
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[3]C. Kesselman et al. The anatomy of the grid:Enabling scalable virtual organizations. Intl' Journal of Supercomputing Applications.2001:1-25.
[4]I. Foster et al. The physiology of the grid:An open grid services architecture for distributed systems integration[J]. Work in progress, Globus Research,2002.
[5]李洪峻,卜彦龙,薛晗等.面向无线传感器网络节点定位的移动锚节点路径规划[J].计算研究与发展,2009,46(1)：20-22.
[6]Capkun S, Hamdi M, Hubaux J P. GPS-free positioning in mobile ad-hoc networks[J]. Cluster Computing,2002,5(2):157-167.
[7]Want R, Hopper A, Falcao V, et al. The active badge location system[J]. ACM Transaction on Information Systems,1992,10(1):91—102.
[8]Harter A, Hopper A. A distributed location system for the active office[J]. IEEE Network, 1994,8(1):62-70.
[9]Bulusu N, Heidemann J, Estrin D. GPS-less low cost outdoor localization for very small device[J].IEEE Personal Communications,2000,7(5):28-34.
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[2]赵文进,石昭祥,黄曙光.移动网格计算综述[J].电子应用技术,2006,(5)：1-3.
[3]C. Kesselman et al. The anatomy of the grid:Enabling scalable virtual organizations. Intl' Journal of Supercomputing Applications.2001:1-25.
[4]I. Foster et al. The physiology of the grid:An open grid services architecture for distributed systems integration[J]. Work in progress, Globus Research,2002.
[5]李洪峻,卜彦龙,薛晗等.面向无线传感器网络节点定位的移动锚节点路径规划[J].计算研究与发展,2009,46(1)：20-22.
[6]Capkun S, Hamdi M, Hubaux J P. GPS-free positioning in mobile ad-hoc networks[J]. Cluster Computing,2002,5(2):157-167.
[7]Want R, Hopper A, Falcao V, et al. The active badge location system[J]. ACM Transaction on Information Systems,1992,10(1):91—102.
[8]Harter A, Hopper A. A distributed location system for the active office[J]. IEEE Network, 1994,8(1):62-70.
[9]Bulusu N, Heidemann J, Estrin D. GPS-less low cost outdoor localization for very small device[J].IEEE Personal Communications,2000,7(5):28-34.
[10]Doherty L, Pister K S J, Chaoui L. Convex position estimation in wireless sensor networks[C]. Proc. Of IEEE INFOCOM, Piscataway, USA:IEEE.2001,5(20):1655-1663.
[11]He T, Huang C D, Blum B M. Range-free localization schemes in large scale sensor networks[C]. Proc. Of the 9th Annual International Conference on Mobile Computing and Networking(MobiCom), San Diego, California,USA:ACM Press,2003,6(11):81-95.
[12]Priyantha N B, Balakrishnan H, Demaine E, et al. Anchor-free distributed Localization in Sensor Networks[R]. MIT Laboratory for Computer Science Tech Report,2003.
[13]GUI Xun-xue,Shan Zhi guan, Liu jia jun. Distributed locatization for anchor-free sensor networks[J]. Systems Engineering and Electronics,2008,19(3):405-418.
[14]Bulusu N. Self-Configuring Localization Systems [D]. Los Angeles:University of California,2002.
[15]Welch G, Bishop G, Vicci L,et al. The HiBall tracker:high-performance wide-area tracking for virtual and augmented environments[C]. Proc. Of the ACM Symp. On Virtual Reality Software and Technology. London:ACM Press,1999,4(30):1-11.
[16]Moon Y H, Trung T M, Youn C H,et al.Wireless Grid Middleware Architecture for Providing Reliable Services[C]. Proceedings of TENCON'05.[S]:IEEE Press,2005,8(2):1-6.
[17]Chu D C.Humphrey M.Mobile OGSI.NET:Grid Computing on Mobile Devices. (2006-06-10).http://www.cs.virginia.edu/-humphrey/papers/MobileOGSI.pdf.
[18]Ohta K, Yoshikawa T, Nakagawa T, et al. Design and Implementation of Mobile Grid Middleware for Handsets[C]. Proceedings of the 11th International Conference on Parallel and Distributed Systems. Washington D.C.USA:IEEE Computer Society,2005,6(9):679-683.
[19]曾文英,赵跃龙,朱玮,王文丰.移动网格体系结构及其资源选择方法[J].计算机工程,2008,34(20)：93-96.
[20]Cybenko G,Jiang Guofei, Bilar D.Machine Learning Applications in Grid Computing[EB]. http://agent.cs.dartmouth.edu/CoABS/papers/cybenko:grid.pdf.
[21]A. Finkelstein, C. Gryce, and J. Lewis-Bowen. Relating requirements and architectures:A study of data-grids[J]. Grid Computing,2004,2(8):207-222.
[22]C. Mattmann, N. Medvidovic, P. Ramirez, and V. Jakobac. Unlocking the grid. In CBSE, 2005,20(2):322-336.
[23]M. Shaw and D. Garlan. Software Architecture:Perspectives on an Emerging Discipline. Prentice Hall,1996.
[24]C. Mattmann, S. Malek, N. Beckman, M. Mikic-Rakic, N. Medvidovic, and D. Crichton. Glide:A grid-based light-weight infrastructure for data-intensive environments. In European Grid Conference,2005:68-77.
[25]C. Mattmann, D. Crichton, N. Medvidovic, and J. S. Hughes. A software architecture-based framework for highly distributed and data-intensive scientific applications. In ICSE,2006: 721-730,.
[26]S. Malek, M. Mikic-Rakic, and N. Medvidovic. A style-aware architectural middleware for resource-constrained, distributed systems. IEEE TSE,2005,31(3):256-272.
[27]C. Kesselman et al. The anatomy of the grid:Enabling scalable virtual organizations. Intl' Journal of Supercomputing Applications,2001:1-25.
[28]I. Foster et al. The physiology of the grid:An open grid services architecture for distributed systems integration. Work in progress, Globus Research,2002.
[29]M. Gudgin et al. Simple object access protocol version 1.2. W3c reccomendation, W3C, 2003.
[30]桂小林.网格技术导论[M].北京：北京邮电大学出版社,2005：243-244.
[31]Alchemi.net grid computing framework. web site,2004.
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