臺灣博碩士論文加值系統

網格運算系統不同於常見的分散式運算系統是因為它著重於大規模的資源分享和為了完成服務的開放式架構。實際上在網格系統可擴張的基礎下，全域網格技術和Globus Toolkit是朝向開放式網格服務架構(OGSA)發展，以便於各機構可以提供他們所擁有的服務和整合他們的資源。本篇論文目標是求取最佳資源配置使得網格運算可以得到最大可靠度。我們先介紹網格服務可靠度的模型和估算可靠度的方法，接下來說明網格服務的資源配置和它的最佳化模型，並提出序的最佳化方法(OO)來解決網格中資源配置問題。最後使用兩個例子來比較現存中使用基因演算法(GA)和我們的方法所得到的解和運算時間的差異。

目 錄
中文摘要...................................................i
英文摘要..................................................ii
誌謝....................................................iii
目錄......................................................iv
表目錄....................................................vi
圖目錄...................................................vii
第一章 緒論.............................................1
1.1 研究動機與目的..................................1
1.2 研究方法與論文架構..............................2
第二章 網格服務與穩定度...................................4
2.1 名詞與變數的命名................................5
2.2 網格服務的描述..................................6
2.3 穩定度模型和網格服務的分析......................7
2.4 網格運算服務可靠度的估算.......................12
第三章 網格運算服務分布的最佳化模型......................15
3.1 網格服務的資源配置.............................15
3.2 最佳化模型.....................................18
3.3 G.A. ..........................................20
3.3.1 流程圖....................................20
3.3.2 配置(編碼)、fitness evaluation............21
3.3.3 補償運算子(違背限制) .....................21
3.3.4 參數的選擇 (初始族群、交配率、突變、世代).22
第四章 以序的最佳化為基礎的演算法........................24
4.1 MRST的搜尋.....................................24
4.2 補償運算子的修正...............................24
第五章 範例..............................................26
5.1 範例1......................................... 26
5.2 範例2 .........................................28
第六章 結論..............................................31
附錄A.....................................................32
附錄B.....................................................37
參考文獻..................................................44

[1] I. Foster, C. Kesselman and S. Tuecke, The anatomy of the grid: enabling scalable virtual organizations, Int J High Perform Comput Appl 15 (2001), pp. 200–222.
[2] S.K. Das, D.J. Harvey and R. Biswas, MinEX: a latency-tolerant dynamic partitioner for grid computing applications, Future Generat Comput Syst 18 (2002), pp. 477–489.
[3] Dai, YS, Xie M, Poh KL. Reliability analysis of grid computing systems. In: IEEE Pacific Rim international symposium on dependable computing (PRDC2002). 2002. p. 97–104.
[4] I. Foster and C. Kesselman, The grid: blueprint for a new computing infrastructure, Morgan-Kaufmann, San Francisco, CA (1998).
[5] I. Foster, C. Kesselman, J.M. Nick and S. Tuecke, Grid services for distributed system integration, Computer 35 (2002), pp. 37–46.
[6] K. Krauter, R. Buyya and M. Maheswaran, A taxonomy and survey of grid resource management systems for distributed computing, Software—Practice Experience 32 (2002), pp. 135–164.
[7] V.K.P. Kumar, S. Hariri and C.S. Raghavendra, Distributed program reliability analysis, IEEE Trans Software Eng SE-12 (1986), pp. 42–50.
[8] A. Kumar, S. Rai and D.P. Agarwal, On computer communication network reliability under program execution constraints, IEEE J Select Area Commun 6 (1988), pp. 1393–1400.
[9] D.J. Chen and T.H. Huang, Reliability analysis of distributed systems based on a fast reliability algorithm, IEEE Trans Parallel Distribute Syst 3 (1992), pp. 139–154.
[10] A. Kumar and D.P. Agrawal, A generalized algorithm for evaluating distributed-program reliability, IEEE Trans Reliab 42 (1993), pp. 416–424.
[11] D.J. Chen, R.S. Chen and T.H. Huang, A heuristic approach to generating file spanning trees for reliability analysis of distributed computing systems, Comput Math Appl 34 (1997), pp. 115–131.
[12] M.S. Lin, M.S. Chang and D.J. Chen, Efficient algorithms for reliability analysis of distributed computing systems, Inform Sci 117 (1999), pp. 89–106.
[13] M.S. Lin, M.S. Chang, D.J. Chen and K.L. Ku, The distributed program reliability analysis on ring-type topologies, Comput Oper Res 28 (2001), pp. 625–635.
[14] Y.S. Dai, M. Xie, K.L. Poh and G.Q. Liu, A study of service reliability and availability for distributed systems, Reliab Eng Syst Saf 79 (2003), pp. 103–112.
[15] M. Livny and R. Raman, High-throughput resource management, The grid: blueprint for a new computing infrastructure, Morgan-Kaufmann, San Francisco, CA (1998), pp. 311–338.
[16] M. Xie, Y.S. Dai and K.L. Poh, Computing systems reliability: models and analysis, Kluwer Academic Publishers, New York (2004).
[17] B. Yang and M. Xie, A study of operational and testing reliability in software reliability analysis, Reliab Eng Syst Saf 70 (2000), pp. 323–329.
[18] C.D. Lai, M. Xie, K.L. Poh, Y.S. Dai and P. Yang, A model for availability analysis of distributed software/hardware systems, Inform Software Technol 44 (2002), pp. 343–350.
[19] Y. S. Dai and X. L. Wang, “Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm,” Reliability Engineering and System Safety, vol. 91, no. 9, pp. 1071–1082,2006.
[20] A. Kumar, R. Pathak and Y. Gupta, Genetic algorithm-based reliability optimization for computer network expansion, IEEE Trans Reliab 44 (1995), pp. 63–72.
[21] D. Coit and A. Smith, Reliability optimization of series-parallel systems using genetic algorithm, IEEE Trans Reliab 45 (1996), pp. 254–266.
[22] Z. Yangping, Z. Bingquan and W. Dongxin, Application of genetic algorithm to fault diagnosis in nuclear power plants, Reliab Eng Syst Saf 67 (2000), pp. 153–160.
[23] M. Marseguerra, E. Zio and M. Cipollone, Designing optimal degradation tests via multi-objective genetic algorithms, Reliab Eng Syst Saf 79 (2003), pp. 87–94.
[24] G. Levitin, Y.S. Dai, M. Xie and K.L. Poh, Optimizing survivability of multi-state systems with multi-level protection by multi-processor genetic algorithm, Reliab Eng Syst Saf 82 (2003), pp. 93–104.