数据网格中数据复制的研究
摘要
网格计算是一个广域范围的分布式计算环境,它包括地理位置不同区域上的个体或协会这些协作者之间大规模的资源共享,这些协作者通常被称为虚拟组组织。数据网格是一种以传送和管理海量科学数据并且用于科学分析为为特定需求的网格基础设施。处理大量数据的科学应用和数据网格技术潜在受益的例子包括高能物理、天文学、生物信息学以及地球科学等。
本文中,我们首先对与复制技术有关的研究成果进行了总结。特别是集中于已经提出的用于数据网格环境的数据副本放置策略。对于每一个副本放置技术,我们考虑它的方法、目标和结果。这些策略通过有关底层的网格拓扑结构,用户的请求模式,数据集的大小以及数据的分布、节点的存储能力等方面的假设有所不同。其他特点包括副本被放置到网格节点的数据请求路径和数据请求方式。面对树结构和别的体系结构之间变化多样的特征,找到一个共同点用于比较不同的复制策略是非常困难的。因此,我们把拓扑结构分为树结构和混合/P2P体系结构,并且分析在每一种体系结构下副本替换策略的影响。一个混合的拓扑结构能够具有树结构和P2P结构的特征,并且能够被用来获取一个复制策略的较好性能
网格环境中数据复制的主要目标是通过把数据副本放置在接近用户的位置来加强数据的可用性,这样就可以最小化用户的感知响应时间。对于分等级的数据网格,副本通常以自顶向下或自底向上的方式来放置。我们提出了一种两路副本放置模式,它可以把最常用的文件副本放置到距离请求客户端近的位置,把不常用的文件副本分层放置到数据网格根节点下面。由兄弟结点和父结点来为数据请求提供服务。
另外一个有趣的、和数据网格中文件副本放置有关的问题是副本服务器之间的负载共享。目前大多数的技术都是选择候选结点用于副本放置,这些候选结点具有最大的文件访问请求。但是,在访问负载和存储负载的基础上选择候选结点有可能产生更加有效的负载平衡复制策略,于是,我们提出了一种方法,称为公平-共享复制(FSR),它在分级的数据网格中放置任何副本之前需要考虑数据请求的数量和候选结点的存储负载。
本文所提出的技术是通过使用GridNet系统来模拟实现的,GridNet系统的研制是为了用来评估数据网格中的复制策略。通过高能物理实验中有关数据网格环境的不同参数的设置来测试两路策略和公平-共享复制策略,性能结果说明了他们的有效性。数据网格环境的不同的设置是指用户访问模式,数据集大小,和服务器存储能力约束。
Grid computing is a wide-area distributed computing environment that involves large-scale resource sharing among collaborations, often referred to as Virtual Organizations, of individuals or institutes located in geographically dispersed areas. Data grids are grid infrastructure with specific needs to transfer and manage massive amounts of scientific data for analysis purposes. Examples of the scientific applications dealing with huge amounts of data and the potential beneficiaries of Data Grid technology are high energy physics, astronomy, bioinformatics, and earth sciences.
In this thesis we first present a review of current and past research on replication techniques. Specifically, we focus on data replica placement policies proposed for use in the data grid environment. For each replica placement technique, we consider its methodology, objective and results. These strategies differ by the assumptions made regarding underlying grid topology, user request patterns, dataset sizes and their distribution, and storage node capacities. Other distinguishing features include data request path and the manner in which replicas are placed on the Grid nodes. In the presence of diverse and varying characteristics of tree and other architectures it is difficult to create a common ground for juxtaposing different replication strategies. We, therefore, classify the topologies into tree and hybrid/P2P architectures and analyze the impact of replica placement policies in each one. A hybrid topology can carry features of both tree and P2P architectures and thus can be used for better performance of a replication strategy.
In multi-tier Data Grid, there is a single source of data and it is not feasible for the one server to fulfill the requests of all the users in the Data Grid. Therefore data must be replicated to the other selected nodes in order to reduce the burden on the master server. Replication also facilitates load balancing and improves reliability by creating multiple data copies. Transferring a file from a server to client consumes a huge amount of bandwidth and incurs storage cost. One possible way to reduce the access latency and bandwidth consumption is to replicate data across different sites. However, the files in Grid are big in size i.e., in the magnitude of 500MB-1GB so replication to every site is not feasible. Among many of the challenges, one and also the focus of this thesis, is to find the candidate sites where we can host the replicas. One way to tackle this problem is to place replicas at sites that satisfy the large number of requests. Another approach is to place replica at sites that optimize the transfer time. The data-intensive tasks in scientific applications usually take longer time and therefore considering storage capacity of sites and current storage load is also importance. We can manage Grid storage resources effectively if we place a replica of a file on a site that has less storage load than its neighbors and if its request for the file is above average. In the thesis, both storage status and file requests are considered before placing a replica to a site. Our approach is dynamic so it adapts to change in user behavior and system dynamics.
The main objective of replication in Grid environment is to enhance data availability by placing replicas at the proximity of users so that user perceived response time is minimized. For a hierarchical Data Grid, replicas are usually placed in either top-down or bottom-up way. We put forward Two-way replica placement scheme that places replicas of most popular files close to the requesting clients and less popular files a tier below from the Data Grid root. We facilitate data requests to be serviced by the sibling nodes as well as by the parent.
Another interesting issue related to file replica placement in Data Grid is load sharing among replica servers. Most of the current techniques select candidate nodes for replica placement that have maximum access requests for files. However, selecting candidate nodes based on access load and storage load together may result in more effective load balancing replication strategy. We proposed an approach called Fair-share Replication (FSR) that takes into account both the number of requests and the storage load on the candidate sites before placing any replica in hierarchical Data Grid.
The simulations of proposed techniques were carried out using the GridNet that is developed for evaluating the replication strategies in Data Grid. The Two-way strategy and the Fair-share replication were tested using parameters from High Energy Physics experiments and the performance results demonstrate their effectiveness for the diverse setup of Data Grid environment in terms of user access patterns, dataset sizes, and server storage capacity constraint.
本文中,我们首先对与复制技术有关的研究成果进行了总结。特别是集中于已经提出的用于数据网格环境的数据副本放置策略。对于每一个副本放置技术,我们考虑它的方法、目标和结果。这些策略通过有关底层的网格拓扑结构,用户的请求模式,数据集的大小以及数据的分布、节点的存储能力等方面的假设有所不同。其他特点包括副本被放置到网格节点的数据请求路径和数据请求方式。面对树结构和别的体系结构之间变化多样的特征,找到一个共同点用于比较不同的复制策略是非常困难的。因此,我们把拓扑结构分为树结构和混合/P2P体系结构,并且分析在每一种体系结构下副本替换策略的影响。一个混合的拓扑结构能够具有树结构和P2P结构的特征,并且能够被用来获取一个复制策略的较好性能
网格环境中数据复制的主要目标是通过把数据副本放置在接近用户的位置来加强数据的可用性,这样就可以最小化用户的感知响应时间。对于分等级的数据网格,副本通常以自顶向下或自底向上的方式来放置。我们提出了一种两路副本放置模式,它可以把最常用的文件副本放置到距离请求客户端近的位置,把不常用的文件副本分层放置到数据网格根节点下面。由兄弟结点和父结点来为数据请求提供服务。
另外一个有趣的、和数据网格中文件副本放置有关的问题是副本服务器之间的负载共享。目前大多数的技术都是选择候选结点用于副本放置,这些候选结点具有最大的文件访问请求。但是,在访问负载和存储负载的基础上选择候选结点有可能产生更加有效的负载平衡复制策略,于是,我们提出了一种方法,称为公平-共享复制(FSR),它在分级的数据网格中放置任何副本之前需要考虑数据请求的数量和候选结点的存储负载。
本文所提出的技术是通过使用GridNet系统来模拟实现的,GridNet系统的研制是为了用来评估数据网格中的复制策略。通过高能物理实验中有关数据网格环境的不同参数的设置来测试两路策略和公平-共享复制策略,性能结果说明了他们的有效性。数据网格环境的不同的设置是指用户访问模式,数据集大小,和服务器存储能力约束。
Grid computing is a wide-area distributed computing environment that involves large-scale resource sharing among collaborations, often referred to as Virtual Organizations, of individuals or institutes located in geographically dispersed areas. Data grids are grid infrastructure with specific needs to transfer and manage massive amounts of scientific data for analysis purposes. Examples of the scientific applications dealing with huge amounts of data and the potential beneficiaries of Data Grid technology are high energy physics, astronomy, bioinformatics, and earth sciences.
In this thesis we first present a review of current and past research on replication techniques. Specifically, we focus on data replica placement policies proposed for use in the data grid environment. For each replica placement technique, we consider its methodology, objective and results. These strategies differ by the assumptions made regarding underlying grid topology, user request patterns, dataset sizes and their distribution, and storage node capacities. Other distinguishing features include data request path and the manner in which replicas are placed on the Grid nodes. In the presence of diverse and varying characteristics of tree and other architectures it is difficult to create a common ground for juxtaposing different replication strategies. We, therefore, classify the topologies into tree and hybrid/P2P architectures and analyze the impact of replica placement policies in each one. A hybrid topology can carry features of both tree and P2P architectures and thus can be used for better performance of a replication strategy.
In multi-tier Data Grid, there is a single source of data and it is not feasible for the one server to fulfill the requests of all the users in the Data Grid. Therefore data must be replicated to the other selected nodes in order to reduce the burden on the master server. Replication also facilitates load balancing and improves reliability by creating multiple data copies. Transferring a file from a server to client consumes a huge amount of bandwidth and incurs storage cost. One possible way to reduce the access latency and bandwidth consumption is to replicate data across different sites. However, the files in Grid are big in size i.e., in the magnitude of 500MB-1GB so replication to every site is not feasible. Among many of the challenges, one and also the focus of this thesis, is to find the candidate sites where we can host the replicas. One way to tackle this problem is to place replicas at sites that satisfy the large number of requests. Another approach is to place replica at sites that optimize the transfer time. The data-intensive tasks in scientific applications usually take longer time and therefore considering storage capacity of sites and current storage load is also importance. We can manage Grid storage resources effectively if we place a replica of a file on a site that has less storage load than its neighbors and if its request for the file is above average. In the thesis, both storage status and file requests are considered before placing a replica to a site. Our approach is dynamic so it adapts to change in user behavior and system dynamics.
The main objective of replication in Grid environment is to enhance data availability by placing replicas at the proximity of users so that user perceived response time is minimized. For a hierarchical Data Grid, replicas are usually placed in either top-down or bottom-up way. We put forward Two-way replica placement scheme that places replicas of most popular files close to the requesting clients and less popular files a tier below from the Data Grid root. We facilitate data requests to be serviced by the sibling nodes as well as by the parent.
Another interesting issue related to file replica placement in Data Grid is load sharing among replica servers. Most of the current techniques select candidate nodes for replica placement that have maximum access requests for files. However, selecting candidate nodes based on access load and storage load together may result in more effective load balancing replication strategy. We proposed an approach called Fair-share Replication (FSR) that takes into account both the number of requests and the storage load on the candidate sites before placing any replica in hierarchical Data Grid.
The simulations of proposed techniques were carried out using the GridNet that is developed for evaluating the replication strategies in Data Grid. The Two-way strategy and the Fair-share replication were tested using parameters from High Energy Physics experiments and the performance results demonstrate their effectiveness for the diverse setup of Data Grid environment in terms of user access patterns, dataset sizes, and server storage capacity constraint.
引文
1. I. Foster. Internet Computing and the Emerging Grid. Web Matters, Nature, Macmillan Publishers, England, 2000.
2. R. Buyya, D. Abramson, and J. Giddy. Nimrod/G: An Architecture of a Resource Management and Scheduling System in a Global Computational Grid. Proceedings of HPC Asia, Beijing, China, 283-289, 2000.
3. I. Foster, C. Kesselman. The Grid: Blueprint for a New Computing Infrastructure (2nd Edition). Morgan Kaufmann, San Francisco, 2003.
4. I. Foster, C. Kesselman, S. Tuecke. The Anatomy of the Grid: Enabling Scalable Virtual Organizations. J. Supercomputer Applications, 15(3), 2001.
5. Seti@Home. http://setiathome.ssl.berkeley.edu
6. The AccessGrid. http://www.accessgrid.org
7. High Energy Physics Experiment Website, http://www.hep.net
8. B. Allcock, J. Bester, J. Bresnahan, A. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnel, S. Tuecke. Data Management and Transfer in High Performance Computational Grid Environments. Parallel Computing Journal, 28 (5):749-771, 2002.
9. K. Ranganathan and I. Foster. Design and Evaluation of Replication Strategies for a High Performance Data Grid. Proceedings of Conference on Computing in High Energy and Nuclear Physics (CHEP’01), 2001.
10. The GriPhyN Project, http://www.griphyn.org
11. H. Lamehamedi, B.K. Szymanski, Z. Shentu, E. Deelman. Simulation of Dynamic Data Replication Strategies in Data Grids. International Parallel and Distributed Processing Symposium (IPDPS’03), Homogeneous Computing Workshop, Nice, France, April 22-26, 2003.
12. L. Fan, P. Cao, J. Almeida, A.Z. Broder. Summary Cache: A scalable Wide-Area Web Cache Sharing Protocol. IEEE/ACM Transactions on Networking, Vol. 8, No.3, 2000.
13. P. Rodriguez, C. Spanner, E.W. Biersack. Analysis of Web Caching Architectures: Hierarchical and Distributed Caching. IEEE/ACM Transactions on Networking, 9( 4):404-418, 2001.
14. A. Luotonen, and K. Altis. World Wide Web Proxies. Computer Networks and ISDN Systems, Conference on WWW, April 1994.
15. R. Caceres, F. Douglis, A. Feldmann, G. Glass, and M. Rabinovich. Web Proxy Caching: The Devil is in the Details. ACM Performance Evaluation Review, 26(3):11-15, 1998.
16. B.M. Duska, D. Marwood and M.J. Feelay. The Measured Access Characteristics of World Wide Web Client Proxy Caches. Proceedings of USENIX Symposium on Internet Technologies and Systems (http://cs.ubc.ca/spider/feeley/wwwap/wwwap.html).
17. T.M. Kroeger, D. Long, and J.C. Mogul. Exploring the Bounds of Web Latency Reduction from Caching and Prefetching. Proceedings of the USENIX Symposium on Internet Technologies and Systems, Dec 1997.
18. R. Tewari, M. Dahlin, H. Vin, J. Kay. Design Consideration for Distributed Caching on the Internet. Proceedings of 19th International Conference on Distributed Computing Systems, IEEE press, 273-284, 1999.
19. A. Wolman, G. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. Levy. On the Scale and Performance of Cooperative Web Proxy Caching. Proceedings of 17th Symposium of Operating System Principles, ACM, 16-31, 1999.
20. M.R. Korupolu and M. Dahlin. Coordinated Placement and Replacement for Large-scale Distributed Caches. IEEE Workshop on Internet Applications, July 1999.
21. P. Krishnan and B. Sugla. Utility of Cooperating Web Proxy Caches. Computer Networks and ISDN Systems, 195-203, April 1998.
22. A. Chankhunthod, P.B. Danzig, C. Neerdaels, M.F. Schwartz, and K.J. Worrel. A Hierarchical Internet Object Cache. USENIX’96, January 1996.
23. L. Zhang, S. Floyd, and V. Jacobsen. Adaptive Web Caching. Proceedings of the NLANR Web Cache Workshop, 1997.
24. D. Povey, and J. Harrison. A Distributed Internet Cache. Proceedings of the 20th Australian Computer Science Conference, Australia, February 1997.
25. D. Wessels, and K. Claffy. Internet Cache Protocol (ICP), version 2, RFC 2186.
26. V. Valloppillil and K.W. Ross. Cache Array Routing Protocol v1.0. Internet Draft.
27. A. Rousskov and D. Wessels. Cache Digest. In 3rd International WWW Caching Workshop, June 1998.
28. P. Cao, K. Zhang and K. Beach. Active Cache: Caching Dynamic Contents on the Web. Proceedings of Middleware '98, Sep. 1998.
29. A. Tanenbaum and M.V. Steen. Distributed Systems: Principles and Paradigms. Prentice Hall, 2002.
30. Akamai. http://www.akamai.com
31. Digital Island. http://www.digitalisland.com
32. M.T. ?zsu and P. Valduriez. Principles of Distributed Database Systems (2nd Edition). Prentice Hall, 1999.
33. D.K. Gifford. Weighted Voting for Replicated Data. Proceeding of ACM Symposium of Operating Systems Principles, 150-162, 1979.
34. R.H. Thomas. A Majority Consensus Approach to Concurrency Control for Multiple Copy Databases. ACM Transactions on Database Systems 4(2): 180-209, 1979.
35. R. Elmasri and S.B. Navathe. Fundamentals of Database Systems (4th Edition). Addison Wesley, 2003.
36. R. Ramakrishnan and J. Gehrke. Database Management Systems (2nd Edition). McGrawHill, 2000.
37. E. Pacitti and E. Simon. Update Propagation Strategies to Improve Freshness in Lazy Master Replicated Databases. The VLDB Journal, v8, 305-318, 2000.
38. J. Gray, P. Helland, P. O’Neil and D. Shasha. The Dangers of Replication and a Solution. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Canada, 173-182, 1996.
39. M.H. Dunham and A. Helal. Mobile Computing and Databases: Anything New?. SIGMOD Record, Vol. 24, No. 4, 1995.
40. H. Kang and S. Lim. Bandwidth-Conserving Cache Validation Schemes in a Mobile Database System. MDM 2001, LNCS 1987, 121-130, 2001.
41. J. Jing, A. Elmagarmid, A. S. Helal, and R. Alonso. Bitsequences: An Adaptive Cache Invalidation Method in Mobile Client/Server Environments. Mobile Networks and Applications, 2(2):115-127, 1997.
42. D. Barbara and T. Imielinski. Sleepers and Workaholics: Caching Strategies in Mobile Environments. Proceedings of ACM SIGMOD International Conference on Management of Data, 1-12, 1994.
43. M. Cai, A. Chervenak, M. Frank. A Peer-to-Peer Replica Location Service Based on a Distributed Hash Table. Proceedings of the SC2004 Conference, Nov 2004.
44. H. Stockinger, A. Samar, B. Allcock, I. Foster, K. Holtman and B. Tierney. File and Object Replication in Data Grids. Proceedings of 10th International Symposium on High Performance Distributed Computing, IEEE press, 2001.
45. S. Vazhkudai, S. Tuecke and I. Foster. Replica Selection in the Globus Data Grid. First IEEE/ACM International Conference on Cluster Computing and the Grid (CCGRID), 2001.
46. H.V. Leong, A. Si. On Adaptive Caching in Mobile Databases. Proceedings of ACM Symposium of Applied Computing, 1997.
47. A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, S. Tuecke. The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Data Sets. Network and Computer Applications, 23(3):187-200, 2000.
48. I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. Journal of Supercomputer Applications, 11(2):115-128, 1997.
49. I. Foster and C. Kesselman. Globus: A Toolkit-Based Grid Architecture. In Foster and Kesselman, editors. The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, 259-278, 1999.
50. The Globus Project, http://www.globus.org
51. I. Foster, C. Kesselman, G. Tsudik and S. Tuecke. A Security Architecture for Computational Grids. ACM Conference on Computers and Security, 83-91, 1998.
52. S. Tuecke, D. Engert, I. Foster, M. Thompson, L. Pearlman, C. Kesselman. Internet X.509 Public Key Infrastructure Proxy Certificate Profile. IETF, Draft draft-ietf-pkix-proxy-01.txt, 2001.
53. K. Czajkowski, S. Fitzgerald, I. Foster and C. Kesselman. Grid Information Services for Distributed Resource Sharing. Proceedings of the 10th IEEE International Symposium on High-Performance Distributed Computing (HPDC-10), IEEE Press, August 2001.
54. S. Raman and S. McCanne. A Model, Analysis, and Protocol Framework for Soft State-based Communication. Computer Communication Review, 29(4), 1999.
55. K. Czajkowski, I. Foster, N. Karonis, C. Kesselman, S. Martin, W. Smith, S. Tuecke. A Resource Management Architecture for Metacomputing Systems. Proceedings of IPPS/SPDP '98 Workshop on Job Scheduling Strategies for Parallel Processing, 62-82, 1998.
56. D. Thain, T. Tannenbaum and M. Livny. Condor and the Grid. Chapter 11 of the Book: Grid Computing– Making the Global Infrastructure a Reality. Edited by F. Berman, A. Hey, G. Fox. John Wiley & Sons, 2003.
57. B. Allcock, J. Bester, J. Bresnahan, A. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnal, S. Tuecke. Secure, Efficient Data Transport and Replica Management for High Performance Data-Intensive Computing. IEEE Mass Storage Conference, 2001.
58. T.A. Howes and M. Smith. A Scalable, Deployable Directory Service Framework for the Internet. Technical Report, Center for Information Technology Integration, University of Michigan, USA.
59. K. Ranganathan and I. Foster. Identifying Dynamic Replication Strategies for a High-Performance Data Grid. Proceedings of International Grid Computing Workshop, LNCS 2242, 75-86, 2001.
60. K. Ranganathan and I. Foster. Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications. Proceedings of 11th IEEE Int’l Symposium on High Performance Distributed Computing, Scotland, 2002.
61. K. Ranganathan, A. Iamnitchi and I. Foster. Improving Data Availability through Dynamic Model Driven Replication in Large Peer-to-Peer Communities. Workshop on Global and Peer-to-Peer Computing on Large Scale Distributed Systems, Berlin, Germany, May 2002.
62. M.R. Rahman, K. Barker, R. Alhajj. Replica Placement in Data Grid: A Multi-objective Approach. GCC 2005, LNCS 3795, 645-656, 2005.
63. M.R. Rahman, K. Barker, R. Alhajj. Replica Placement Design with Static Optimality and Dynamic Maintainability. Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGrid’06), 2006.
64. H. Lamehamedi, B.K. Szymanski, Z. Shentu, E. Deelman. Data Replication Strategies in Grid Environments. Proceedings of International Conference on Antennas and Propagation (ICAP), 378-383, 2002.
65. H. Lamehamedi and B.K. Szymanski. Decentralized Data Management Framework for Data Grids. Future Generation Computer Systems (Elsevier), 23 (1):109-115, 2007.
66. LHC Computing Grid Project. http://lcg.web.cern.ch/LCG/
67. Particle Physics Data Grid (PPDG). http://www.gridpp.ac.uk
68. Enabling Grid for EsciencE (EGEE). http://www.eu-egee.org/
69. Belle Analysis Data Grid. http://epp.ph.unimelb.edu.au/epp/grid/badg/
70. Grid3. http://www.ivdgl.org/grid2003/
71. Earth System Grid. http://www.earthsystemgrid.org/
72. I. Foster and A. Iamnitchi. On Death, Taxes, and the Convergence of Peer-to-Peer and Grid Computing. Proceedings of the 2nd International Workshop on Peer-to-Peer Systems (IPTPS), Berkeley, CA, USA, LNCS 2735, Springer-Verlag, London, UK, 118-128, 2003.
73. J. Ledlie, J. Shneidman, M. Seltzer and J. Huth. Scooped Again. Proceedings of the 2nd International Workshop on Peer-to-Peer Systems (IPTPS), Berkeley, CA, USA, LNCS 2735. Springer-Verlag, London, UK, 2003.
74. G. Fox and S. Pallickara. The Narada Event Brokering System: Overview and Extensions. Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications(PDPTA '02), CSREA Press, Las Vegas, USA, 353-359, 2002.
75. M. Parshar and S. Hariri. Autonomic Grid Computing. Tutorial, International Conference on Autonomic Computing (ICAC '04), IEEE CS Press, Los Alamitos, CA, USA, 2004.
76. O. Ardaiz, P. Artigas, T. Eymann, F. Freitag, L. Navarro and M. Reinicke. Self-Organizing Resource Allocation for Autonomic Networks. Proceedings of 1st International Workshop on Autonomic Computing Systems, IEEE CS Press, Los Alamitos, CA, USA, 2003.
77. L. Pearlman, C. Kesselman, S. Gullapalli, B. Spencer, J. Futrelle, R. Kathleen, I. Foster, P. Hubbard and C. Severance. Distributed Hybrid Earthquake Engineering Experiments: Experiences with a Ground-Shaking Grid Application. Proceedings of the 13th IEEE Symposium on High Performance Distributed Computing (HPDC-13), Honolulu, HI, USA, IEEE CS Press, Los Alamitos, CA, USA, 2004.
78. M. Aderholz et al. MONARC Project Phase2 Report. Technical Report, CERN, March 2000.
79. A. Rajasekar, M. Wan, R. Moore, and W. Schroeder. Data Grid Federation. Proceedings of the 11th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2004), Las Vegas, USA, CSREA Press, 2004.
80. Biomedical Informatics Research Network (BIRN), http://www.nbirn.net.
81. R. Moore, A. Jagatheesan, A. Rajasekar, M. Wan and W. Schroeder. Data Grid Management Systems. Proceedings of the 12th NASA Goddard, 21st IEEE Conference on Mass Storage Systems and Technologies, IEEE CS Press, Los Alamitos, CA, USA, 2004.
82. C. Baru, R. Moore, A. Rajasekar and M. Wan. The SDSC Storage Resource Broker. Proceedings of CASCON'98. IBM Press, Toronto, Canada, 1998.
83. V. Vlassov, D. Li, K. Popov and S. Haridi. A Scalable Autonomous Replica Management Framework for Grids. Proceedings of IEEE John Vincent Atanasoff (JVA’06), International Symposium on Modern Computing, 33-40, 2006.
84. F. Mao, H. Jin, H. Chen, S. Wu and D. Zou. P2P Based Decentralized Dynamic Replica Placement Strategy in Grid Environment. Technical Report, Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, China, 2004.
85. F. Schintke, T. Schutt and A. Reinefeld. A Framework for Self-Optimizing Grids Using P2P Components. Proceedings of 14th International Workshop on Database and Expert Systems Appl. (DEXA’03), 689-693, 2003.
86. J. Crowcroft, T. Moreton, I. Pratt and A. Twigg. Chapter: Peer-to-Peer Technologies. In: Foster and Kesselman, editors, The Grid 2: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, 2003.
87. Q. Lv, P. Cao, E. Cohen, K. Li and S. Shenker. Search and Replication in Unstructured Peer-to-Peer Networks. Proceedings of the 16th Annual ACM International Conference on Supercomputing (ICS’02), New York, 2002.
88. R. Wolski. Forecasting Network Performance to Support Dynamic Scheduling Using the Network Weather Service. Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing, 316-325, 1997.
89. Q. Fan, Q. Wu, Y. He, J. Huang. Transportation Strategies of the Data Grid. Proceedings of the 1st International Conference on Semantics, Knowledge, and Grid (SKG), 2006.
90. A. Mondal and M. Kitsuregawa. Effective Dynamic Replication in Wide-Area Network Environments: A Perspective. International Workshop on Database and Expert Systems Applications (DEXA’05), 287-291, 2005.
91. J.H. Abawajy. Placement of File Replicas in Data Grid Environments. Proceedings of International Conference on Computational Science, LNCS 3038, 66-73, 2004.
92. Y. Yuan, Y. Wu, G. Yang, F. Yu. Dynamic Data Replication Based on Local Optimization in Data Grid. Proceedings of 6th International Conference on Grid and Cooperative Computing (GCC’07), IEEE, 815-822, 2007.
93. M. Tang, B. Lee, C. Yeo and X. Tang. Dynamic Replication Algorithms for the Multi-tier Data Grid. Future Generation Computer Systems, Elsevier, 21: 775-790, 2005.
94. T. Loukopoulos and I. Ahmad. Static and Adaptive Data Replication Algorithms for Fast Information Access in Large Distributed Systems. Proceedings of 20th IEEE International Conference on Distributed Computing Systems, 385-392, 2000.
95. A. Elghirani, A.Y. Zomaya and R. Subrata. An Intelligent Replication Framework for Data Grids. Proceedings of IEEE/ACS International Conf on Computer Systems and Applications (AICCSA’07), IEEE, 351-358, 2007.
96. A. Benoit, V. Rehn and Y. Robert. Strategies for Replica Placement in Tree Networks. Proceedings of Parallel and Distributed Processing Symposium (IPDPS’07), Heterogeneous Computing Workshop, IEEE, 1-15, 2007.
97. L. Guy, P. Kunszt, E. Laure, H. Stockinger and K. Stockinger. Replica Management in Data Grids. GGF5 Working Draft, Edinburgh, Scotland, July 2002.
98. W. Hoschek, J. Jaen-Martinez, A. Samar, H. Stockinger and K. Stockinger. Data Management in an International Data Grid Project. Proceedings of the IEEE/ACM International Workshop on Grid Computing, IEEE, 77-90, 2000.
99. Y.F. Lin, P. Liu and J.J. Wu. Optimal Placement of Replicas in Data Grid Environment with Locality Assurance. Proceedings of 12th International Conference on Parallel and Distributed Systems (ICPADS’06), IEEE, 465-472, 2006.
100. P. Liu and J. Wu. Optimal Replica Placement Strategy for Hierarchical Data Grid Systems. Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGrid’06), 2006.
101. NS-2, The Network Simulator, http://www.isi.edu/nsnam/ns/
102. U. Cibej, B. Slivnik, B. Robic. The Complexity of Static Data Replication in Data Grids. Journal of Parallel Computing, 31: 900-912, 2005.
103. M. Lei, S.V. Vrbsky and X. Hong. An On-line Replication Strategy to Increase Availability in Data Grids. Future Generation Computer Systems, Elsevier, 24:85-98, 2008.
104. V. Rehn-Sonigo. Optimal Replica Placement in Tree Networks with QoS and Bandwidth Constraints and the Closest Allocation Policy. Research Report, Inria, France, June 2007.
105. R.-S.Chang and H.-P. Chang. A Dynamic Data Replication Strategy Using Access Weights in Data Grids. Journal of Supercomputing, Springer Science +Business Media, LLC 2008.
2. R. Buyya, D. Abramson, and J. Giddy. Nimrod/G: An Architecture of a Resource Management and Scheduling System in a Global Computational Grid. Proceedings of HPC Asia, Beijing, China, 283-289, 2000.
3. I. Foster, C. Kesselman. The Grid: Blueprint for a New Computing Infrastructure (2nd Edition). Morgan Kaufmann, San Francisco, 2003.
4. I. Foster, C. Kesselman, S. Tuecke. The Anatomy of the Grid: Enabling Scalable Virtual Organizations. J. Supercomputer Applications, 15(3), 2001.
5. Seti@Home. http://setiathome.ssl.berkeley.edu
6. The AccessGrid. http://www.accessgrid.org
7. High Energy Physics Experiment Website, http://www.hep.net
8. B. Allcock, J. Bester, J. Bresnahan, A. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnel, S. Tuecke. Data Management and Transfer in High Performance Computational Grid Environments. Parallel Computing Journal, 28 (5):749-771, 2002.
9. K. Ranganathan and I. Foster. Design and Evaluation of Replication Strategies for a High Performance Data Grid. Proceedings of Conference on Computing in High Energy and Nuclear Physics (CHEP’01), 2001.
10. The GriPhyN Project, http://www.griphyn.org
11. H. Lamehamedi, B.K. Szymanski, Z. Shentu, E. Deelman. Simulation of Dynamic Data Replication Strategies in Data Grids. International Parallel and Distributed Processing Symposium (IPDPS’03), Homogeneous Computing Workshop, Nice, France, April 22-26, 2003.
12. L. Fan, P. Cao, J. Almeida, A.Z. Broder. Summary Cache: A scalable Wide-Area Web Cache Sharing Protocol. IEEE/ACM Transactions on Networking, Vol. 8, No.3, 2000.
13. P. Rodriguez, C. Spanner, E.W. Biersack. Analysis of Web Caching Architectures: Hierarchical and Distributed Caching. IEEE/ACM Transactions on Networking, 9( 4):404-418, 2001.
14. A. Luotonen, and K. Altis. World Wide Web Proxies. Computer Networks and ISDN Systems, Conference on WWW, April 1994.
15. R. Caceres, F. Douglis, A. Feldmann, G. Glass, and M. Rabinovich. Web Proxy Caching: The Devil is in the Details. ACM Performance Evaluation Review, 26(3):11-15, 1998.
16. B.M. Duska, D. Marwood and M.J. Feelay. The Measured Access Characteristics of World Wide Web Client Proxy Caches. Proceedings of USENIX Symposium on Internet Technologies and Systems (http://cs.ubc.ca/spider/feeley/wwwap/wwwap.html).
17. T.M. Kroeger, D. Long, and J.C. Mogul. Exploring the Bounds of Web Latency Reduction from Caching and Prefetching. Proceedings of the USENIX Symposium on Internet Technologies and Systems, Dec 1997.
18. R. Tewari, M. Dahlin, H. Vin, J. Kay. Design Consideration for Distributed Caching on the Internet. Proceedings of 19th International Conference on Distributed Computing Systems, IEEE press, 273-284, 1999.
19. A. Wolman, G. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. Levy. On the Scale and Performance of Cooperative Web Proxy Caching. Proceedings of 17th Symposium of Operating System Principles, ACM, 16-31, 1999.
20. M.R. Korupolu and M. Dahlin. Coordinated Placement and Replacement for Large-scale Distributed Caches. IEEE Workshop on Internet Applications, July 1999.
21. P. Krishnan and B. Sugla. Utility of Cooperating Web Proxy Caches. Computer Networks and ISDN Systems, 195-203, April 1998.
22. A. Chankhunthod, P.B. Danzig, C. Neerdaels, M.F. Schwartz, and K.J. Worrel. A Hierarchical Internet Object Cache. USENIX’96, January 1996.
23. L. Zhang, S. Floyd, and V. Jacobsen. Adaptive Web Caching. Proceedings of the NLANR Web Cache Workshop, 1997.
24. D. Povey, and J. Harrison. A Distributed Internet Cache. Proceedings of the 20th Australian Computer Science Conference, Australia, February 1997.
25. D. Wessels, and K. Claffy. Internet Cache Protocol (ICP), version 2, RFC 2186.
26. V. Valloppillil and K.W. Ross. Cache Array Routing Protocol v1.0. Internet Draft
27. A. Rousskov and D. Wessels. Cache Digest. In 3rd International WWW Caching Workshop, June 1998.
28. P. Cao, K. Zhang and K. Beach. Active Cache: Caching Dynamic Contents on the Web. Proceedings of Middleware '98, Sep. 1998.
29. A. Tanenbaum and M.V. Steen. Distributed Systems: Principles and Paradigms. Prentice Hall, 2002.
30. Akamai. http://www.akamai.com
31. Digital Island. http://www.digitalisland.com
32. M.T. ?zsu and P. Valduriez. Principles of Distributed Database Systems (2nd Edition). Prentice Hall, 1999.
33. D.K. Gifford. Weighted Voting for Replicated Data. Proceeding of ACM Symposium of Operating Systems Principles, 150-162, 1979.
34. R.H. Thomas. A Majority Consensus Approach to Concurrency Control for Multiple Copy Databases. ACM Transactions on Database Systems 4(2): 180-209, 1979.
35. R. Elmasri and S.B. Navathe. Fundamentals of Database Systems (4th Edition). Addison Wesley, 2003.
36. R. Ramakrishnan and J. Gehrke. Database Management Systems (2nd Edition). McGrawHill, 2000.
37. E. Pacitti and E. Simon. Update Propagation Strategies to Improve Freshness in Lazy Master Replicated Databases. The VLDB Journal, v8, 305-318, 2000.
38. J. Gray, P. Helland, P. O’Neil and D. Shasha. The Dangers of Replication and a Solution. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Canada, 173-182, 1996.
39. M.H. Dunham and A. Helal. Mobile Computing and Databases: Anything New?. SIGMOD Record, Vol. 24, No. 4, 1995.
40. H. Kang and S. Lim. Bandwidth-Conserving Cache Validation Schemes in a Mobile Database System. MDM 2001, LNCS 1987, 121-130, 2001.
41. J. Jing, A. Elmagarmid, A. S. Helal, and R. Alonso. Bitsequences: An Adaptive Cache Invalidation Method in Mobile Client/Server Environments. Mobile Networks and Applications, 2(2):115-127, 1997.
42. D. Barbara and T. Imielinski. Sleepers and Workaholics: Caching Strategies in Mobile Environments. Proceedings of ACM SIGMOD International Conference on Management of Data, 1-12, 1994.
43. M. Cai, A. Chervenak, M. Frank. A Peer-to-Peer Replica Location Service Based on a Distributed Hash Table. Proceedings of the SC2004 Conference, Nov 2004.
44. H. Stockinger, A. Samar, B. Allcock, I. Foster, K. Holtman and B. Tierney. File and Object Replication in Data Grids. Proceedings of 10th International Symposium on High Performance Distributed Computing, IEEE press, 2001.
45. S. Vazhkudai, S. Tuecke and I. Foster. Replica Selection in the Globus Data Grid. First IEEE/ACM International Conference on Cluster Computing and the Grid (CCGRID), 2001.
46. H.V. Leong, A. Si. On Adaptive Caching in Mobile Databases. Proceedings of ACM Symposium of Applied Computing, 1997.
47. A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, S. Tuecke. The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Data Sets. Network and Computer Applications, 23(3):187-200, 2000.
48. I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. Journal of Supercomputer Applications, 11(2):115-128, 1997.
49. I. Foster and C. Kesselman. Globus: A Toolkit-Based Grid Architecture. In Foster and Kesselman, editors. The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, 259-278, 1999.
50. The Globus Project, http://www.globus.org
51. I. Foster, C. Kesselman, G. Tsudik and S. Tuecke. A Security Architecture for Computational Grids. ACM Conference on Computers and Security, 83-91, 1998.
52. S. Tuecke, D. Engert, I. Foster, M. Thompson, L. Pearlman, C. Kesselman. Internet X.509 Public Key Infrastructure Proxy Certificate Profile. IETF, Draft draft-ietf-pkix-proxy-01.txt, 2001.
53. K. Czajkowski, S. Fitzgerald, I. Foster and C. Kesselman. Grid Information Services for Distributed Resource Sharing. Proceedings of the 10th IEEE International Symposium on High-Performance Distributed Computing (HPDC-10), IEEE Press, August 2001.
54. S. Raman and S. McCanne. A Model, Analysis, and Protocol Framework for Soft State-based Communication. Computer Communication Review, 29(4), 1999.
55. K. Czajkowski, I. Foster, N. Karonis, C. Kesselman, S. Martin, W. Smith, S. Tuecke. A Resource Management Architecture for Metacomputing Systems. Proceedings of IPPS/SPDP '98 Workshop on Job Scheduling Strategies for Parallel Processing, 62-82, 1998.
56. D. Thain, T. Tannenbaum and M. Livny. Condor and the Grid. Chapter 11 of the Book: Grid Computing– Making the Global Infrastructure a Reality. Edited by F. Berman, A. Hey, G. Fox. John Wiley & Sons, 2003.
57. B. Allcock, J. Bester, J. Bresnahan, A. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnal, S. Tuecke. Secure, Efficient Data Transport and Replica Management for High Performance Data-Intensive Computing. IEEE Mass Storage Conference, 2001.
58. T.A. Howes and M. Smith. A Scalable, Deployable Directory Service Framework for the Internet. Technical Report, Center for Information Technology Integration, University of Michigan, USA.
59. K. Ranganathan and I. Foster. Identifying Dynamic Replication Strategies for a High-Performance Data Grid. Proceedings of International Grid Computing Workshop, LNCS 2242, 75-86, 2001.
60. K. Ranganathan and I. Foster. Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications. Proceedings of 11th IEEE Int’l Symposium on High Performance Distributed Computing, Scotland, 2002.
61. K. Ranganathan, A. Iamnitchi and I. Foster. Improving Data Availability through Dynamic Model Driven Replication in Large Peer-to-Peer Communities. Workshop on Global and Peer-to-Peer Computing on Large Scale Distributed Systems, Berlin, Germany, May 2002.
62. M.R. Rahman, K. Barker, R. Alhajj. Replica Placement in Data Grid: A Multi-objective Approach. GCC 2005, LNCS 3795, 645-656, 2005.
63. M.R. Rahman, K. Barker, R. Alhajj. Replica Placement Design with Static Optimality and Dynamic Maintainability. Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGrid’06), 2006.
64. H. Lamehamedi, B.K. Szymanski, Z. Shentu, E. Deelman. Data Replication Strategies in Grid Environments. Proceedings of International Conference on Antennas and Propagation (ICAP), 378-383, 2002.
65. H. Lamehamedi and B.K. Szymanski. Decentralized Data Management Framework for Data Grids. Future Generation Computer Systems (Elsevier), 23 (1):109-115, 2007.
66. LHC Computing Grid Project. http://lcg.web.cern.ch/LCG/
67. Particle Physics Data Grid (PPDG). http://www.gridpp.ac.uk
68. Enabling Grid for EsciencE (EGEE). http://www.eu-egee.org/
69. Belle Analysis Data Grid. http://epp.ph.unimelb.edu.au/epp/grid/badg/
70. Grid3. http://www.ivdgl.org/grid2003/
71. Earth System Grid. http://www.earthsystemgrid.org/
72. I. Foster and A. Iamnitchi. On Death, Taxes, and the Convergence of Peer-to-Peer and Grid Computing. Proceedings of the 2nd International Workshop on Peer-to-Peer Systems (IPTPS), Berkeley, CA, USA, LNCS 2735, Springer-Verlag, London, UK, 118-128, 2003.
73. J. Ledlie, J. Shneidman, M. Seltzer and J. Huth. Scooped Again. Proceedings of the 2nd International Workshop on Peer-to-Peer Systems (IPTPS), Berkeley, CA, USA, LNCS 2735. Springer-Verlag, London, UK, 2003.
74. G. Fox and S. Pallickara. The Narada Event Brokering System: Overview and Extensions. Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications(PDPTA '02), CSREA Press, Las Vegas, USA, 353-359, 2002.
75. M. Parshar and S. Hariri. Autonomic Grid Computing. Tutorial, International Conference on Autonomic Computing (ICAC '04), IEEE CS Press, Los Alamitos, CA, USA, 2004.
76. O. Ardaiz, P. Artigas, T. Eymann, F. Freitag, L. Navarro and M. Reinicke. Self-Organizing Resource Allocation for Autonomic Networks. Proceedings of 1st International Workshop on Autonomic Computing Systems, IEEE CS Press, Los Alamitos, CA, USA, 2003.
77. L. Pearlman, C. Kesselman, S. Gullapalli, B. Spencer, J. Futrelle, R. Kathleen, I. Foster, P. Hubbard and C. Severance. Distributed Hybrid Earthquake Engineering Experiments: Experiences with a Ground-Shaking Grid Application. Proceedings of the 13th IEEE Symposium on High Performance Distributed Computing (HPDC-13), Honolulu, HI, USA, IEEE CS Press, Los Alamitos, CA, USA, 2004.
78. M. Aderholz et al. MONARC Project Phase2 Report. Technical Report, CERN, March 2000.
79. A. Rajasekar, M. Wan, R. Moore, and W. Schroeder. Data Grid Federation. Proceedings of the 11th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2004), Las Vegas, USA, CSREA Press, 2004.
80. Biomedical Informatics Research Network (BIRN), http://www.nbirn.net.
81. R. Moore, A. Jagatheesan, A. Rajasekar, M. Wan and W. Schroeder. Data Grid Management Systems. Proceedings of the 12th NASA Goddard, 21st IEEE Conference on Mass Storage Systems and Technologies, IEEE CS Press, Los Alamitos, CA, USA, 2004.
82. C. Baru, R. Moore, A. Rajasekar and M. Wan. The SDSC Storage Resource Broker. Proceedings of CASCON'98. IBM Press, Toronto, Canada, 1998.
83. V. Vlassov, D. Li, K. Popov and S. Haridi. A Scalable Autonomous Replica Management Framework for Grids. Proceedings of IEEE John Vincent Atanasoff (JVA’06), International Symposium on Modern Computing, 33-40, 2006.
84. F. Mao, H. Jin, H. Chen, S. Wu and D. Zou. P2P Based Decentralized Dynamic Replica Placement Strategy in Grid Environment. Technical Report, Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, China, 2004.
85. F. Schintke, T. Schutt and A. Reinefeld. A Framework for Self-Optimizing Grids Using P2P Components. Proceedings of 14th International Workshop on Database and Expert Systems Appl. (DEXA’03), 689-693, 2003.
86. J. Crowcroft, T. Moreton, I. Pratt and A. Twigg. Chapter: Peer-to-Peer Technologies. In: Foster and Kesselman, editors, The Grid 2: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, 2003.
87. Q. Lv, P. Cao, E. Cohen, K. Li and S. Shenker. Search and Replication in Unstructured Peer-to-Peer Networks. Proceedings of the 16th Annual ACM International Conference on Supercomputing (ICS’02), New York, 2002.
88. R. Wolski. Forecasting Network Performance to Support Dynamic Scheduling Using the Network Weather Service. Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing, 316-325, 1997.
89. Q. Fan, Q. Wu, Y. He, J. Huang. Transportation Strategies of the Data Grid. Proceedings of the 1st International Conference on Semantics, Knowledge, and Grid (SKG), 2006.
90. A. Mondal and M. Kitsuregawa. Effective Dynamic Replication in Wide-Area Network Environments: A Perspective. International Workshop on Database and Expert Systems Applications (DEXA’05), 287-291, 2005.
91. J.H. Abawajy. Placement of File Replicas in Data Grid Environments. Proceedings of International Conference on Computational Science, LNCS 3038, 66-73, 2004.
92. Y. Yuan, Y. Wu, G. Yang, F. Yu. Dynamic Data Replication Based on Local Optimization in Data Grid. Proceedings of 6th International Conference on Grid and Cooperative Computing (GCC’07), IEEE, 815-822, 2007.
93. M. Tang, B. Lee, C. Yeo and X. Tang. Dynamic Replication Algorithms for the Multi-tier Data Grid. Future Generation Computer Systems, Elsevier, 21: 775-790, 2005.
94. T. Loukopoulos and I. Ahmad. Static and Adaptive Data Replication Algorithms for Fast Information Access in Large Distributed Systems. Proceedings of 20th IEEE International Conference on Distributed Computing Systems, 385-392, 2000.
95. A. Elghirani, A.Y. Zomaya and R. Subrata. An Intelligent Replication Framework for Data Grids. Proceedings of IEEE/ACS International Conf on Computer Systems and Applications (AICCSA’07), IEEE, 351-358, 2007.
96. A. Benoit, V. Rehn and Y. Robert. Strategies for Replica Placement in Tree Networks. Proceedings of Parallel and Distributed Processing Symposium (IPDPS’07), Heterogeneous Computing Workshop, IEEE, 1-15, 2007.
97. L. Guy, P. Kunszt, E. Laure, H. Stockinger and K. Stockinger. Replica Management in Data Grids. GGF5 Working Draft, Edinburgh, Scotland, July 2002.
98. W. Hoschek, J. Jaen-Martinez, A. Samar, H. Stockinger and K. Stockinger. Data Management in an International Data Grid Project. Proceedings of the IEEE/ACM International Workshop on Grid Computing, IEEE, 77-90, 2000.
99. Y.F. Lin, P. Liu and J.J. Wu. Optimal Placement of Replicas in Data Grid Environment with Locality Assurance. Proceedings of 12th International Conference on Parallel and Distributed Systems (ICPADS’06), IEEE, 465-472, 2006.
100. P. Liu and J. Wu. Optimal Replica Placement Strategy for Hierarchical Data Grid Systems. Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGrid’06), 2006.
101. NS-2, The Network Simulator, http://www.isi.edu/nsnam/ns/
102. U. Cibej, B. Slivnik, B. Robic. The Complexity of Static Data Replication in Data Grids. Journal of Parallel Computing, 31: 900-912, 2005.
103. M. Lei, S.V. Vrbsky and X. Hong. An On-line Replication Strategy to Increase Availability in Data Grids. Future Generation Computer Systems, Elsevier, 24:85-98, 2008.
104. V. Rehn-Sonigo. Optimal Replica Placement in Tree Networks with QoS and Bandwidth Constraints and the Closest Allocation Policy. Research Report, Inria, France, June 2007.
105. R.-S.Chang and H.-P. Chang. A Dynamic Data Replication Strategy Using Access Weights in Data Grids. Journal of Supercomputing, Springer Science +Business Media, LLC 2008.