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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 劉邦鋒(Pangfeng Liu) | |
dc.contributor.author | Yung-Ching Hsu | en |
dc.contributor.author | 許詠慶 | zh_TW |
dc.date.accessioned | 2021-06-13T01:23:16Z | - |
dc.date.available | 2016-08-05 | |
dc.date.copyright | 2011-08-05 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-02 | |
dc.identifier.citation | [1] Amazon ec2. http://aws.amazon.com/ec2/.
[2] Vijay V. Vazirani. Approximation Algorithms, chapter Bin Packing, pages 74–78. Springer Verlag, 2001. [3] Michael R. Garey and David S. Johnson. Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY, USA, 1990. [4] Shekhar Srikantaiah, Aman Kansal, and Feng Zhao. Energy aware consolidation for cloud computing. In Proceedings of the 2008 conference on Power aware computing and systems, HotPower’08, pages 10–10, Berkeley, CA, USA, 2008. USENIX Association. [5] Bo Li, Jianxin Li, Jinpeng Huai, Tianyu Wo, Qin Li, and Liang Zhong. Enacloud: An energy-saving application live placement approach for cloud computing environments. In Proceedings of the 2009 IEEE International Conference on Cloud Computing, CLOUD ’09, pages 17–24, Washington, DC, USA, 2009. IEEE Computer Society. [6] Anton Beloglazov and Rajkumar Buyya. Energy efficient resource management in virtualized cloud data centers. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID ’10, pages 826–831, Washington, DC, USA, 2010. IEEE Computer Society. [7] I. Rodero, J. Jaramillo, A. Quiroz, M. Parashar, F. Guim, and S. Poole. Energy-efficient application-aware online provisioning for virtualized clouds and data centers. In Proceedings of the International Conference on Green Computing, GREENCOMP ’10, pages 31–45, Washington, DC, USA, 2010. IEEE Computer Society. [8] Konstantinos Tsakalozos, Mema Roussopoulos, Vangelis Floros, and Alex Delis. Nefeli: Hint-based execution of workloads in clouds. In Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems, ICDCS ’10, pages 74–85, Washington, DC, USA, 2010. IEEE Computer Society. [9] Tram Truong Huu and JohanMontagnat. Virtual resources allocation for workflow-based applications distribution on a cloud infrastructure. In Proceedings of the 2010 10th IEEE/ACMInternational Conference on Cluster, Cloud and Grid Computing, CCGRID ’10, pages 612–617, Washington, DC, USA, 2010. IEEE Computer Society. [10] Jiayin Li, Meikang Qiu, Jianwei Niu, Yu Chen, and Zhong Ming. Adaptive resource allocation for preemptable jobs in cloud systems. In ISDA, pages 31–36, 2010. [11] Mark Stillwell, David Schanzenbach, Frederic Vivien, and Henri Casanova. Resource allocation using virtual clusters. In Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID ’09, pages 260–267, Washington, DC, USA, 2009. IEEE Computer Society. [12] Peter Brucker. Scheduling Algorithms. Springer Publishing Company, Incorporated, 5th edition, 2007. [13] Fangzhe Chang, Jennifer Ren, and Ramesh Viswanathan. Optimal resource allocation in clouds. In Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing, CLOUD ’10, pages 418–425, Washington, DC, USA, 2010. IEEE Computer Society. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29885 | - |
dc.description.abstract | 這篇論文指出資料中心在能源保存上的重要議題。我們探討如何將伺服器分配給工作序列並且減少總能源消耗,在此我們使用效能評比標準為“浪費能源” – 伺服器額外供應給工作的計算資源超出實際工作需求的部分。我們提出了三種工作序列資源佈署策略,分別為最大機器優先方法、最佳符合方法和混合方法。我們並證明出最大機器優先方法與混合方法擁有效能保證,最多只會造成 2/n 浪費能源比值。換句話說,額外浪費能源與實際供應能源比值不會超過 2(1 + δ)/n,n 為工作序列中工作數目,而 1 + δ 為工作序列中工作最大執行時間與最小執行時間的比值。當δ 任意大時,我們也對浪費能源推導出一個精確界限 1/2 。最後我們藉由實驗去比較這三種方法在實際應用上的效能,實驗結果顯示出這三種方法都浪費相當少能源。混合方法優於最佳符合方法,且最佳符合方法優於最大機器優先方法。 | zh_TW |
dc.description.abstract | This paper describes the important issue of energy conservation for data centers. We consider the problem of provisioning physical servers to a sequence of jobs, and reducing the total energy consumption. The performance metric is the wasted energy – the over-provisioned computing power provided by the physical servers, but exceeding the requirement of the jobs. We propose three new strategies for allocating servers to a sequence of jobs – a largest machine first heuristic, a best fit method, and a mixed method. We prove that both the largest machine first heuristic and the
mixed method will only incur at most 2/n in over-provisioned energy. That is, the ratio between the over-provisioned energy and the total provisioned energy is bounded by 2(1 + δ)/n, where n is the number of jobs, and 1+δ is the ratio between the maximum and minimum execution time of jobs. We also derive a tight bound of 1/2 on the ratio of wasted energy if the ratio δ could be arbitrarily large. We also conduct experiments to compare the three algorithms in practice. The experiment results indicate that all three algorithms waste very little energy in over-provision. The mixed method outperforms the best fit method, which outperforms the largest machine first method. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T01:23:16Z (GMT). No. of bitstreams: 1 ntu-100-R98922145-1.pdf: 391248 bytes, checksum: 9c59736acf3c66fcbb128f4f95c90e3a (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | Certification I
Acknowledgement II Chinese Abstract III Abstract IV 1 Introduction 1 2 Related Works 4 3 Model 6 3.1 Problem Definition 8 4 Algorithms 10 4.1 Largest-Machine-First 10 4.2 Best Fit Method 10 4.3 Mixed Method 11 5 Analysis 13 5.1 Performance Guarantee 14 5.2 Waste Ratio Bound 14 5.3 Waste Ratio Bound for Arbitrary Execution Time 16 6 Experiments 19 7 Conclusion 22 Bibliography 23 | |
dc.language.iso | en | |
dc.title | 雲端計算中工作序列的虛擬機器排程方法 | zh_TW |
dc.title | Virtual Machine Scheduling for Job Sequences in Cloud
Computing | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳健輝(Gen-Huey Chen),呂學一(Hsueh-I Lu),王大為(Da-Wei Wang) | |
dc.subject.keyword | 雲端計算,能源保存,工作序列排程, | zh_TW |
dc.subject.keyword | Cloud computing,Energy conservation,Job sequence scheduling, | en |
dc.relation.page | 24 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-03 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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