雲端運算是一種新興的商業計算模型。它將計算任務分佈在大量計算機構成的資源池上，使各種應用系統能夠根據需要獲取計算力、存儲空間和各種軟體服務。在目前對雲端運算任務分配問題的研究中，絕大多數學者只考慮單一目標，例如使得總成本最小化、總生產時間最小化等。然而，各虛擬機負載均衡情況，系統可靠度情況，能源消耗情況等因素都對雲端運算服務質量有影響，並且，大多目標間存在衝突關係，例如：可靠度增加會導致總成本增加。由此，我們很難以單一目標去評價一個任務分配方案的優劣。因此，從多目標的角度思考雲端運算的任務分配問題才能夠適應實際應用的發展。
雲端運算由於其伺服器規模龐大、用戶群體廣泛，面臨兩大問題：對雲端運算資源進行合理分配、對大量的應用任務進行高效調度。雲端運算的任務分配問題從本質上來講是一種優化組合類型的NP-hard問題，很難在多項式時間複雜度之內求得全域最優解。近年相關研究多使用啟發式演算法（如基因演算法、粒子群演算法等）來進行問題的求解。此類方法希望能發揮啟發式演算法的特長，在可以接受的演算時間內找到高品質的解。
在本研究中，會基於多目標粒子群演算法(Multi-Objective Particle Swarm Optimization，MOPSO)中建立外部集合儲存非淩駕解的流程與思路，並藉鑒簡化群演算法(Simplified Swarm Optimization, SSO)方便、高效的更新機制，發展出一種適用於多目標問題的新演算法，多目標簡化群演算法(Multi-Objective Simplified Swarm Optimization，MOSSO)。在此基礎上，爲了增強該演算法對可行解空間的搜索能力，本篇論文中設計了動態的參數，用以平衡演算法的全域與局部搜索能力。
爲了驗證本研究所提出演算法的效能優劣，本研究使用隨機生成的雲端資源資料進行模擬分析，並與其他演算法進行比較。最終的實驗結果顯示本文所提出的演算法對於處理多目標的雲端運算任務分配問題，相較于傳統的多目標粒子群演算法(MOPSO)以及精英機制的非淩駕排序基因演算法(NSGA II)，能獲得最優質的解。

Nowadays, cloud computing and big data are changing the enterprise. Cloud computing, as a new business computing mode, distributes computing tasks across resource pools made up of a large number of computers for large-scale calculation. In the current research on the task assignment problem of cloud computing, most scholars consider single-objective programming, for example minimizing the cost or makespan. However, factors such as load balancing of virtual machines, reliability of system, and energy consumption can influence the quality of the cloud computing service. Therefore, in order to adapt to the development of practical applications, multi-objective programming should be considered in the task scheduling problem of cloud computing.
Cloud computing refers to storing and accessing applications, data or services over the internet remotely. Due to its large server size and wide user base, cloud computing is faced with two major problems: reasonable allocation of cloud computing recourses and efficient scheduling of a large number of application tasks. The task scheduling problem of cloud computing is essentially an NP-hard problem of combinatorial optimization which is hard to find the global optima within polynomial time complexity. In recent researches, heuristic algorithms (such as genetic algorithms and particle swarm optimization) are used to solve the problem. Such methods can hopefully make use of the strength of heuristic algorithms and find high-quality solutions within an acceptable running time.
This paper proposes a new algorithm (Multi-Objective Simplified Swarm Optimization，MOSSO) for multi-objective problems, based on a new, convenient and efficient heuristic algorithm called Simplified Swarm Optimization (SSO) and using the procedure and idea of establishing a repository in the Multi-Objective Particle Swarm Optimization (MOPSO). In order to increase the search ability of feasible solution space in this algorithm, this paper designs dynamitic parameters to make the mutation rate large at the early stage to enhance global search ability and the mutation rate small at late stage to enhance local search ability.
In order to verify the performance of the proposed algorithm, this paper uses randomly generated cloud computing data for simulation analysis and makes comparison with other algorithms. The final result proves that the method proposed in this paper can take both efficiency of algorithm and quality of solutions into account, and play the strength of Simplified Swarm Optimization to help users find the best quality solution and finishing multiple objectives such as minimizing total power and minimizing makespan at the same time.

摘要.........................................I
Abstract...................................III
誌謝.........................................V
目錄........................................VI
表目錄....................................VIII
圖目錄......................................IX
第一章 緒論...................................1
1.1 研究背景和動機.........................1
1.2 研究架构...............................4
1.3 論文结果...............................5
第二章 文獻回顧...............................6
2.1 雲端運算任務分配問題....................6
2.2 多目標演算法在雲端運算問題的應用.........6
2.3 簡化群演算法的發展研究..................8
第三章 問題描述...............................9
3.1 符號縮寫...............................9
3.2 問題模型...............................9
3.3 目標函數..............................10
第四章 研究方法..............................12
4.1 簡化群演算法..........................12
4.2 多目標粒子群演算法.....................13
4.3 精英機制的非淩駕排序基因演算法..........14
4.4 多目標簡化群演算法.....................15
第五章 實驗結果與分析.........................20
5.1 參數設定..............................20
5.2 績效指標..............................21
5.3 實驗結果..............................22
第六章 總結與展望.............................31
6.1 總結..................................31
6.2 展望..................................31
參考文獻.....................................33
附錄.........................................36

1. Kaur, S. and A. Verma, An efficient approach to genetic algorithm for task scheduling in cloud computing environment. International Journal of Information Technology and Computer Science (IJITCS), 2012. 4(10): p. 74.
2. Zhang, Q., L. Cheng, and R. Boutaba, Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 2010. 1(1): p. 7-18.
3. Wei, S.C. and W.C. Yeh, Resource allocation decision model for dependable and cost-effective grid applications based on Grid Bank. Future Generation Computer Systems, 2017. 77: p. 12-28.
4. Yeh, W.C. and S.C. Wei, Economic-based resource allocation for reliable Grid-computing service based on Grid Bank. Future Generation Computer Systems, 2012. 28(7): p. 989-1002.
5. Mezmaz, M., N. Melab, Y. Kessaci, Y.C. Lee, E.G. Talbi, A.Y. Zomaya, D. Tuyttens, A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal of Parallel and Distributed Computing, 2011. 71(11): p. 1497-1508.
6. Attiya, G. and Y. Hamam, Task allocation for maximizing reliability of distributed systems: A simulated annealing approach. Journal of parallel and Distributed Computing, 2006. 66(10): p. 1259-1266.
7. Zhao, C., S. Zhang, Q. Liu, J. Xie, J. Hu, Independent tasks scheduling based on genetic algorithm in cloud computing. in Wireless Communications, Networking and Mobile Computing, 2009. WiCom'09. 5th International Conference on. 2009. IEEE.
8. Tawfeek, M.A., El-Sisi, Ashraf. E.S., Keshk. A.E., Torkey. F.A., Cloud task scheduling based on ant colony optimization. in Computer Engineering & Systems (ICCES), 2013 8th International Conference on. 2013. IEEE.
9. Ramezani, F., J. Lu, and F.K. Hussain, Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization. International Journal of Parallel Programming, 2014. 42(5): p. 739-754.
10. Chen, H., H. Zhu, H. Guo, J. Zhu, X. Qin, J. Wu, Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. Journal of Systems and Software, 2015. 99: p. 20-35.
11. Malawski, M., G. Juve, E. Deelman, J. Nabrzyski, Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. Future Generation Computer Systems, 2015. 48: p. 1-18.
12. Zuo, X., G. Zhang, and W. Tan, Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Transactions on Automation Science and Engineering, 2014. 11(2): p. 564-573.
13. Abdi, S., S.A. Motamedi, and S. Sharifian. Task scheduling using Modified PSO Algorithm in cloud computing environment. in International conference on machine learning, electrical and mechanical engineering. 2014.
14. Fieldsend, J.E. and S. Singh, A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. 2002.
15. Coello, C.A.C., G.T. Pulido, and M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 2004. 8(3): p. 256-279.
16. Yeh, W.C. and M.C. Chuang, Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Systems with applications, 2011. 38(4): p. 4244-4253.
17. Zhou, A., et al., Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 2011. 1(1): p. 32-49.
18. Liu, J., X. Luo, X. Zhang, F. Zhang, B. Li, Job scheduling model for cloud computing based on multi-objective genetic algorithm. IJCSI International Journal of Computer Science Issues, 2013. 10(1): p. 134-139.
19. Jena, R., Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Computer Science, 2015. 57: p. 1219-1227.
20. Fard, H.M., R. Prodan, J.J.D. Barrionuevo, T. Fahringer, A multi-objective approach for workflow scheduling in heterogeneous environments. in Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012). 2012. IEEE Computer Society.
21. Doğan, A. and F. Özgüner, Biobjective scheduling algorithms for execution time–reliability trade-off in heterogeneous computing systems. The Computer Journal, 2005. 48(3): p. 300-314.
22. Yeh, W.C., A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. Expert Systems with Applications, 2009. 36(5): p. 9192-9200.
23. Yeh, W.C., Novel swarm optimization for mining classification rules on thyroid gland data. Information Sciences, 2012. 197: p. 65-76.
24. Huang, C.L., A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems. Reliability Engineering & System Safety, 2015. 142(Supplement C): p. 221-230.
25. Chung, Y.Y. and N. Wahid, A hybrid network intrusion detection system using simplified swarm optimization (SSO). Applied Soft Computing, 2012. 12(9): p. 3014-3022.
26. Yeh, W.C., New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series. IEEE Transactions on Neural Networks and Learning Systems, 2013. 24(4): p. 661-665.
27. Yeh, W.C., Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 2012. 42(1): p. 250-261.
28. Srinivas, N. and K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 1994. 2(3): p. 221-248.
29. Deb, K., A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 2002. 6(2): p. 182-197.
30. Yeh, W.C., Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. Knowledge-Based Systems, 2014. 64: p. 1-12.
31. Van Veldhuizen, D.A. and G.B. Lamont, Multiobjective evolutionary algorithm research: A history and analysis. 1998, Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.
32. Schott, J.R., Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. 1995, AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH.