关于提高网格计算性能和服务质量的几点研究
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摘要
网格是借鉴电力网的概念提出来的,它利用基于IP协议的宽带数字通信网络把分散在不同地理位置的电脑组织成一个“虚拟的超级计算机”,其中每一台参与计算的计算机就是一个“节点”,而整个计算环境是由成千上万个“节点”组成的“一张网格”,最终目的是希望用户在使用网格资源时就象现在使用电力一样方便。网格计算环境中资源分布共享、自治统一管理,以及资源的异构性和强动态性使得网格的性能和服务质量很难满足用户要求。
本论文围绕提高网格环境的性能和服务质量开展了广泛的理论研究并进行了大量试验,取得了一些成果。
利用蚂蚁算法具有的正反馈性和协同性、隐含的并行性及可扩展性适合于网络结构频繁改变的网格环境的特点,对基本蚂蚁算法进行了改进,设计了基于蚂蚁算法的网格任务调度策略。针对网格环境难于保证服务质量的问题,设计了一种实用的用户、资源和任务分类调度机制,在一定程度上实现了网格的QOS机制。为了仿真复杂的网格环境,我们在研究了现有的多种网格仿真环境的基础上设计了一种可扩展的网格仿真结构,适于研究不同的网格资源管理和任务调度方法。针对网格环境易用性差、应用开发困难的问题,提出了基于组件的应用开发环境,有利于提高网格的易用性,便于网格计算的普及应用,其中的RBAC用户管理机制有利于提高系统安全性,同时通过对组件的合理管理,有助于系统负载均衡和容错性的提高。
Computational grid is based on power grid. It organizes distributed computers as a “virtual supper computer” by wide band digit network. Every computer is called a node, and all the nodes form a “Grid”. The target of grid is to make the users feel the use of grid is as convenient as using power grid. But the resources and user requirements in computational grid are more complex than power grid, and the distributed, autonomous, dynamic of resources make the performance and quality of service very hard to meet the user’s need.
This paper made many theories study and do many experiments about upgrading the performance and service quality of grid environment, and get good results.
We make use of the positive feedback, cooperativeness, parallelism and expansibility of ant algorithm which is very suitable to Grid, do some improvement on basic ant algorithm, and design the grid task scheduling method based on ant algorithm. For upgrading the service quality of grid, we use batch schedule and classify the users, tasks and resources in the grid, plan a practical QOS mechanism for Grid. For simulating the complex grid environment, we learn about the widely used grid simulate systems, design and realize an extendable grid simulate system, which is suitable to simulate different resource management and task scheduling methods. We find the application programming in grid environment is very difficult. So, we bring forward a grid application develop environment based on component technology. This environment is very easy to use, and by the proper management of components, the load balancing and fault tolerance of the system is easier.
本论文围绕提高网格环境的性能和服务质量开展了广泛的理论研究并进行了大量试验,取得了一些成果。
利用蚂蚁算法具有的正反馈性和协同性、隐含的并行性及可扩展性适合于网络结构频繁改变的网格环境的特点,对基本蚂蚁算法进行了改进,设计了基于蚂蚁算法的网格任务调度策略。针对网格环境难于保证服务质量的问题,设计了一种实用的用户、资源和任务分类调度机制,在一定程度上实现了网格的QOS机制。为了仿真复杂的网格环境,我们在研究了现有的多种网格仿真环境的基础上设计了一种可扩展的网格仿真结构,适于研究不同的网格资源管理和任务调度方法。针对网格环境易用性差、应用开发困难的问题,提出了基于组件的应用开发环境,有利于提高网格的易用性,便于网格计算的普及应用,其中的RBAC用户管理机制有利于提高系统安全性,同时通过对组件的合理管理,有助于系统负载均衡和容错性的提高。
Computational grid is based on power grid. It organizes distributed computers as a “virtual supper computer” by wide band digit network. Every computer is called a node, and all the nodes form a “Grid”. The target of grid is to make the users feel the use of grid is as convenient as using power grid. But the resources and user requirements in computational grid are more complex than power grid, and the distributed, autonomous, dynamic of resources make the performance and quality of service very hard to meet the user’s need.
This paper made many theories study and do many experiments about upgrading the performance and service quality of grid environment, and get good results.
We make use of the positive feedback, cooperativeness, parallelism and expansibility of ant algorithm which is very suitable to Grid, do some improvement on basic ant algorithm, and design the grid task scheduling method based on ant algorithm. For upgrading the service quality of grid, we use batch schedule and classify the users, tasks and resources in the grid, plan a practical QOS mechanism for Grid. For simulating the complex grid environment, we learn about the widely used grid simulate systems, design and realize an extendable grid simulate system, which is suitable to simulate different resource management and task scheduling methods. We find the application programming in grid environment is very difficult. So, we bring forward a grid application develop environment based on component technology. This environment is very easy to use, and by the proper management of components, the load balancing and fault tolerance of the system is easier.
引文
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[12] http://www.earthsystemgrid.org/
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[16] W. E. Johnston, D. Gannon, and B. Nitzberg. Information Power Grid Implementation Plan: Research, Development, and Testbeds for High Performance, Widely Distributed, Collaborative, Computing and Information Systems Supporting Science and Engineering. NASAAmes Research Center, http://www.nas.nasa.gov/IPG ,1999
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[28] Wang L. Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm_Based Approach. Journal Of Parallel and Distributed Computing. Vol.47, 1997 :18-22
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[38] Colorni A, Dorigo M and Maniezzo V. Distributed optimization by ant colonies[A]. In:Proc.of 1st European Conf. Artificial Life[C]. Pans, France: Elsevier.1991.134-142
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[2] Foster I., Kesselman C., Tuecke S. The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications, 15(3), 2001
[3] 卢锡城,肖侬,王怀民,挑战与对策,http://www.chinagrid.net/grid/,2002
[4] I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. International Journal of Supercomputer Applications. USA: Sage Publications, 1997 11(2). 115-128
[5] http://www.gridforum.org
[6] http://www.ccgrid.org
[7] http://www.gridhome.com.cn
[8] Primet, P, Harakaly, R.,Bonnassieux, F. experiments of network throughput measurement and forecasting using the network weather service. Proceedings CCGRID 2002. 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2002. 413-18
[9] 都志辉,陈渝,刘鹏,网格计算, 北京:清华大学出版社,2002年11月
[10] Staab, S.; van der Aalst, W.; Benjamins, V.R.; etc. Web services: been there, done that?. IEEE Intelligent Systems, v 18, n 1, Jan.-Feb. 2003. 72-85
[11] 肖侬,数据网格研究现状, http://tech.ccidnet.com/pub/article/c1060_a89502_p1.html, 2002
[12] http://www.earthsystemgrid.org/
[13] Grimshaw A. S., Wulf W. A., the Legion team. The Legion Vision of a Worldwide Virtual Computer. Communications of the ACM, 1997, 40(1): 39-45
[14] Su, A.; Berman, F.; Wolski, R.; Strout, M.M. Using AppLes to schedule simple SARA on the computational grid. International Journal of High Performance Computing Applications, v 13, n 3. Fall 1999. 253-62
[15] Foster, I.; Kesselman, C.; Nick, J.M.; Tuecke, S. Grid services for distributed system integration. Computer , Volume 35, Issue 6 , 2002.6. 37 –46
[16] W. E. Johnston, D. Gannon, and B. Nitzberg. Information Power Grid Implementation Plan: Research, Development, and Testbeds for High Performance, Widely Distributed, Collaborative, Computing and Information Systems Supporting Science and Engineering. NASAAmes Research Center, http://www.nas.nasa.gov/IPG ,1999
[17] 刘鹏,网格计算池模型, http://hpclab.cs.tsinghua.edu.cn/~pengliu/http://www.Globus.org/
[18] Ernemann, C., Hamscher, V.; Schwiegelshohn, U.;etc. on advantages of grid computing for parallel job scheduling. Proceedings CCGRID 2002. 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2002. 39-46
[19] Das, S.K, Harvey, D.J., Biswas, R. Latency hiding in dynamic partitioning and load balancing of grid computing applications. Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid, 2001. 347-54
[20] M.H.Willebeek-Lemair, A.P. Reeves. Strategies for Dynamic Load Balancing on Highly Parallel Computers. IEEE Transactions on Parallel and Distributed Systems, 1993.4(9).
[21] K. Czajkowski, S. Fitzgerald, I. Foster, etc. Grid Information Services for Distributed Resource Sharing. Proceedings of the Tenth IEEE International Symposium on High-Performance Distributed Computing (HPDC-10): IEEE Press. 2001.8
[22] Zheng Ran ; Li Shengli; Jin Hai. Grid resource management and scheduling model. Journal of Huazhong University of Science and Technology. v 29, n 12, Dec. 2001.87-9
[23] Klaus Krauter, Rajkumar Buyya, and Muthucumaru Maheswaran. Taxonomy and Survey of Grid Resource Management Systems for Distributed Computing. Software: Practice and Experience (SPE) Journal. USA: Wiley Press. 2001. 1-7
[24] Krauter, Klaus, Buyya, Rajkumar, Maheswaran, Muthucumaru. A taxonomy and survey of Grid resource management systems for distributed computing. Software - Practice and Experience, v 32, n 2, February, 2002. 135-164
[25] Hamscher, V.; Schwiegelshohn, U.; Streit, A.;etc. Evaluation of job-scheduling strategies for grid computing. Grid Computing - GRID 2000. First IEEE/ACM International Workshop. Proceedings (Lecture Notes in Computer Science Vol.1971), 2000. 191-202
[26] Czajkowski, K.; Foster, I.; Kesselman, C. Resource co-allocation in computational grids. High Performance Distributed Computing. 1999. The Eighth International Symposium on Cluster Computing and the Grid. 1999. 219–228
[27] Ricardo C.Crrea, Scheduling Multiprocessor Tasks with Genetic Algorithms, IEEE Transactions on Parallel and Distributed systems, Vol 10, No.8. 1999.8:825-837
[28] Wang L. Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm_Based Approach. Journal Of Parallel and Distributed Computing. Vol.47, 1997 :18-22
[29] Shen Shen Wu, David Sweeting, Heuristic algorithms for task assignment and scheduling in a processor network, Parallel Computing, Vol.20, 1994.1-14
[30] Islam, N.; Prodromidis, A.L.; Squillante, M.S. etc. Extensible resource management for cluster computing. Distributed Computing Systems. 1997. 561–568
[31] C.Papadimitriou , M.Yannakakis, Scheduling interval-ordered tasks, SIAM Journal of Computing , 1979.8:405-409
[32] Kwok Yu-Kwong, Ahmad Ishfaq. Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors Using a Parallel Genetic Algorithm. Journal Parallel and Distributed Computing, Vol.47. 1997.1:58-77
[33] 朱福喜,何炎祥,并行分布计算中的调度算法理论与设计,武汉:武汉大学出版社,2003
[34] Yun-chia liang, Alice E.Smith. An Ant System Approach to Redundancy Allocation. IEEE 1999.1478-1484
[35] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. Ant System: Optimization by a Colony of Cooperating of Agents. IEEE Transactions on Systems, Man, and Cybernetics-part B: Cybernetics Vol.26, No.1, February 1996
[36] Daniel Merkle, Martin Middendorf, Hartmut Schmeck. Ant colony optimization for resource constrained project scheduling. IEEE Transactions On Evolutionary Computation, VOL.6, NO. 4, August 2000
[37] Marco Dorigo, Luca M. Garnbardella. Ant colonied for the Travelling Salesman Problem. 1997, vol.43:73-81
[38] Colorni A, Dorigo M and Maniezzo V. Distributed optimization by ant colonies[A]. In:Proc.of 1st European Conf. Artificial Life[C]. Pans, France: Elsevier.1991.134-142
[39] Chao-Hsien Chu, JunHua Gu, and XiangDan Hou. A Heuristic Ant Algorithm for solving QOS Multicast Routing Problem. CEC 2002
[40] De Turck, F.; Volckaert, B.; Demeester, P.;etc. A generic middleware-based platform for scalable cluster computing. Future Generation Computer Systems. v 18 n 4. March 2002. 549-560
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