請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71742完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉邦鋒(Pang-Feng Liu) | |
| dc.contributor.author | Ching-Chi Lin | en |
| dc.contributor.author | 林敬棋 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:08:19Z | - |
| dc.date.available | 2020-12-26 | |
| dc.date.copyright | 2018-12-26 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-12-21 | |
| dc.identifier.citation | [1] Amazon AWS Auto-Scaling. https://aws.amazon.com/autoscaling/.
[2] Amazon EC2 Auto-Scaling. https://aws.amazon.com/ec2/autoscaling/. [3] Amazon Elastic Compute Cloud (EC2). http://aws.amazon.com/ec2/. [4] ARM DS-5 Development Studio. http://ds.arm.com/ds-5/. [5] Auto-Scaling in RightScale. https://docs.rightscale.com/faq/What_is_auto-scaling.html. [6] Auto-Scaling in Scalr. https://scalr-wiki.atlassian.net/wiki/spaces/docs/pages/1773507/Scaling. [7] Docker Swarm. https://docs.docker.com/engine/swarm/. [8] Google Cloud Platform. https://cloud.google.com/gcp/. [9] Google Cloud Platform Auto-Scaling. https://cloud.google.com/compute/docs/autoscaler/. [10] Homepage of LIKWID tool suite. http://code.google.com/p/likwid/. [11] Homepage of PARSEC Benchmark Suite. http://parsec.cs.princeton.edu/. [12] Homepage of Phoronix Test Suite. https://www.phoronix-test-suite.com/. [13] Judgegirl. https://github.com/ntuparallellab/judgegirl. [14] Juno ARM Development Platform. http://www.arm.com/products/tools/development-boards/versatile-express/juno-arm-development-platform.php. [15] Microsoft Azure. https://azure.microsoft.com/features/autoscale/. [16] Nginx. https://nginx.org. [17] SPECpower 2008 results. http://www.spec.org/power_ssj2008/results/. [18] VLC media player. http://www.videolan.org/vlc/. [19] Workload Trace: NASA-HTTP. http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html. [20] Workload Trace: WorldCup’98. http://ita.ee.lbl.gov/html/contrib/WorldCup.html. [21] S. Albers and H. Fujiwara. Energy-efficient algorithms for flow time minimization. ACM Trans. Algorithms, 3(4), Nov. 2007. [22] M. S. Aslanpour, M. Ghobaei-Arani, and A. N. Toosi. Auto-scaling web applications in clouds: A cost-aware approach. Journal of Network and Computer Applications, 95:26 – 41, 2017. [23] G. Aupy, A. Benoit, F. Dufossé, and Y. Robert. Reclaiming the energy of a schedule: models and algorithms. Concurrency and Computation: Practice and Experience, 25(11):1505–1523, 2013. [24] G. Aupy, M. Shantharam, A. Benoit, Y. Robert, and P. Raghavan. Co-scheduling algorithms for high-throughput workload execution. Journal of Scheduling, 19(6):627–640, Dec 2016. [25] N. Bansal, T. Kimbrel, and K. Pruhs. Speed scaling to manage energy and temperature. J. ACM, 54(1):3:1–3:39, Mar. 2007. [26] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xen and the art of virtualization. In ACM SIGOPS operating systems review, volume 37, pages 164–177. ACM, 2003. [27] A. Beloglazov and R. Buyya. Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst., 24(7):1366–1379, July 2013. [28] J. L. Bentley. Decomposable searching problems. Inf. Process. Lett., 8(5):244–251, 1979. [29] R. Bleuse, S. Hunold, S. Kedad-Sidhoum, F. Monna, G. Mounié, and D. Trystram. Scheduling independent moldable tasks on multi-cores with GPUs. IEEE Transactions on Parallel and Distributed Systems, 28(9):2689–2702, 2017. [30] R. D. Blumofe and D. Papadopoulos. The performance of work stealing in multiprogrammed environments. In ACM SIGMETRICS Performance Evaluation Review, pages 266–267. ACM Press, 1998. [31] P. Bogdan, S. Garg, and U. Ogras. Energy-efficient computing from systems-onchip to micro-server and data centers. In Green Computing Conference and Sustainable Computing Conference (IGSC), 2015 Sixth International, pages 1–6, Dec 2015. [32] T. D. Braun, H. J. Siegel, N. Beck, L. L. Bölöni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. Theys, B. Yao, D. Hensgen, and R. F. Freund. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput., 61(6):810–837, June 2001. [33] E. Caron, F. Desprez, and A. Muresan. Forecasting for grid and cloud computing on-demand resources based on pattern matching. In Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, pages 456–463. IEEE, 2010. [34] J.-J. Chen and T.-W. Kuo. Multiprocessor energy-efficient scheduling for real-time tasks with different power characteristics. In Parallel Processing, 2005. ICPP 2005. International Conference on, pages 13–20. IEEE, 2005. [35] Q. Chen and M. Guo. Adaptive workload-aware task scheduling for single-ISA asymmetric multicore architectures. ACM Trans. Archit. Code Optim., 11(1):8:1–8:25, Feb. 2014. [36] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. Live migration of virtual machines. In Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation-Volume 2, pages 273–286. USENIX Association, 2005. [37] W. T. Cochran, J. W. Cooley, D. L. Favin, H. D. Helms, R. A. Kaenel, W. W. Lang, G. C. Maling, D. E. Nelson, C. M. Rader, and P. D. Welch. What is the fast fourier transform? Proceedings of the IEEE, 55(10):1664–1674, 1967. [38] J. Cong and B. Yuan. Energy-efficient scheduling on heterogeneous multi-core architectures. In Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED ’12, pages 345–350, New York, NY, USA, 2012. ACM. [39] M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes. Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Transactions on Network and Service Management, 12(3):377–391, Sept 2015. [40] G. B. Dantzig. Linear programming and extensions. Princeton university press, 1998. [41] J. Du and J. Y.-T. Leung. Complexity of scheduling parallel task systems. SIAM Journal on Discrete Mathematics, 2(4):473–487, 1989. [42] A. Evangelidis, D. Parker, and R. Bahsoon. Performance modelling and verification of cloud-based auto-scaling policies. Future Generation Computer Systems, 87:629 – 638, 2018. [43] J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001. [44] A. Frumusanu. The Samsung Exynos 7420 deep dive - inside a modern 14nm SoC. http://www.anandtech.com/show/9330/exynos-7420-deep-dive, 2015. [45] A. Gandhi, Y. Chen, D. Gmach, M. F. Arlitt, and M. Marwah. Minimizing data center SLA violations and power consumption via hybrid resource provisioning. In IGCC, pages 1–8. IEEE Computer Society, 2011. [46] A. Gandhi, P. Dube, A. Karve, A. Kochut, and L. Zhang. Adaptive, model-driven autoscaling for cloud applications. In 11th International Conference on Autonomic Computing (ICAC 14), pages 57–64, Philadelphia, PA, 2014. USENIX Association. [47] M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-completeness. Books in mathematical series. W. H. Freeman, 1979. [48] D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Workload analysis and demand prediction of enterprise data center applications. In Workload Characterization, 2007. IISWC 2007. IEEE 10th International Symposium on, pages 171–180, September 2007. [49] Z. Gong, X. Gu, and J. Wilkes. PRESS: PRedictive Elastic ReSource Scaling for cloud systems. In 2010 International Conference on Network and Service Management, pages 9–16, Oct 2010. [50] T. Gonzalez and S. Sahni. Open shop scheduling to minimize finish time. Journal of the ACM (JACM), 23(4):665–679, 1976. [51] P. Greenhalgh. Big. LITTLE processing with ARM Cortex-A15 & Cortex-A7. ARM White Paper, 2011. [52] Y. Guo, A. Stolyar, and A. Walid. Online VM auto-scaling algorithms for application hosting in a cloud. IEEE Transactions on Cloud Computing, pages 1–1, 2018. [53] V. Gupta, M. Lee, and K. Schwan. Heterovisor: Exploiting resource heterogeneity to enhance the elasticity of cloud platforms. In Proceedings of the 11th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE’15, pages 79–92, New York, NY, USA, 2015. ACM. [54] L. Hu, H. Jin, X. Liao, X. Xiong, and H. Liu. Magnet: A novel scheduling policy for power reduction in cluster with virtual machines. In CLUSTER, pages 13–22. IEEE, 2008. [55] B. Jeff. big.LITTLE technology moves towards fully heterogeneous global task scheduling. ARM White Paper, 2013. [56] G. Jia, G. Han, J. Jiang, N. Sun, and K. Wang. Dynamic resource partitioning for heterogeneous multi-core-based cloud computing in smart cities. IEEE Access, 4:108–118, 2016. [57] H. Jiang, E. Haihong, and M. Song. Hierarchical prediction based task scheduling in hybrid data center. In Parallel and Distributed Systems (ICPADS), 2014 20th IEEE International Conference on, pages 17–24. IEEE, 2014. [58] J. Jiang, J. Lu, G. Zhang, and G. Long. Optimal cloud resource auto-scaling for web applications. In CCGRID, pages 58–65. IEEE Computer Society, 2013. [59] D. S. Johnson, A. Demers, J. D. Ullman, M. R. Garey, and R. L. Graham. Worstcase performance bounds for simple one-dimensional packing algorithms. SIAM Journal on Computing, 3(4):299–325, 1974. [60] M. Kim, S. Noh, S. Huh, and S. Hong. Fair-share scheduling for performanceasymmetric multicore architecture via scaled virtual runtime. In Embedded and Real-Time Computing Systems and Applications (RTCSA), 2015 IEEE 21st International Conference on, pages 60–69, Aug 2015. [61] S. I. Kim, H. T. Kim, G. S. Kang, and J.-K. Kim. Using DVFS and task scheduling algorithms for a hard real-time heterogeneous multicore processor environment. In Proceedings of the 2013 Workshop on Energy Efficient High Performance Parallel and Distributed Computing, EEHPDC ’13, pages 23–30, New York, NY, USA, 2013. ACM. [62] T. Kolpe, A. Zhai, and S. S. Sapatnekar. Enabling improved power management in multicore processors through clustered DVFS. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2011, pages 1–6. IEEE, 2011. [63] F. Kong, W. Yi, and Q. Deng. Energy-efficient scheduling of real-time tasks on cluster-based multicores. In Design, Automation Test in Europe Conference Exhibition (DATE), 2011, pages 1–6, March 2011. [64] D. Koufaty, D. Reddy, and S. Hahn. Bias scheduling in heterogeneous multi-core architectures. In Proceedings of the 5th European Conference on Computer Systems, EuroSys ’10, pages 125–138, New York, NY, USA, 2010. ACM. [65] Y. Kwon, C. Kim, S. Maeng, and J. Huh. Virtualizing performance asymmetric multi-core systems. In Proceedings of the 38th Annual International Symposium on Computer Architecture, ISCA ’11, pages 45–56, New York, NY, USA, 2011. ACM. [66] T.-W. Lam, L.-K. Lee, I. K. To, and P. W. Wong. Speed scaling functions for flow time scheduling based on active job count. In Proceedings of the 16th Annual European Symposium on Algorithms, ESA ’08, pages 647–659, Berlin, Heidelberg, 2008. Springer-Verlag. [67] K. Li. Energy efficient scheduling of parallel tasks on multiprocessor computers. The Journal of Supercomputing, 60(2):223–247, 2012. [68] C.-C. Lin, P. Liu, and J.-J. Wu. Energy-aware virtual machine dynamic provision and scheduling for cloud computing. In 2011 IEEE 4th International Conference on Cloud Computing, pages 736–737. IEEE, 2011. [69] D. Liu and C. Svensson. Power consumption estimation in CMOS VLSI chips. IEEE Journal of Solid-State Circuits, 29(6):663–670, 1994. [70] L. Liu, H. Wang, X. Liu, X. Jin, W. B. He, Q. B. Wang, and Y. Chen. Greencloud: a new architecture for green data center. In Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, ICAC-INDST ’09, pages 29–38, New York, NY, USA, 2009. ACM. [71] P. Loiseau and X. Wu. Simple algorithms for scheduling monotonic moldable tasks. CoRR, abs/1609.08588, 2016. [72] M. Mazzucco, D. Dyachuk, and R. Deters. Maximizing cloud providers’ revenues via energy aware allocation policies. Cloud Computing, IEEE International Conference on, 0:131–138, 2010. [73] M. Mazzucco, D. Dyachuk, and M. Dikaiakos. Profit-aware server allocation for green internet services. Modeling, Analysis, and Simulation of Computer Systems, International Symposium on, 0:277–284, 2010. [74] P. Mell, T. Grance, et al. The NIST definition of cloud computing. 2011. [75] N. Melot, C. Kessler, J. Keller, and P. Eitschberger. Fast crown scheduling heuristics for energy-efficient mapping and scaling of moldable streaming tasks on manycore systems. ACM Trans. Archit. Code Optim., 11(4):62:1–62:24, Jan. 2015. [76] D. Merkel. Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239):2, 2014. [77] D. Mosberger and T. Jin. httperf —a tool for measuring web server performance. ACM SIGMETRICS Performance Evaluation Review, 26(3):31–37, 1998. [78] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, and D. Zagorodnov. The eucalyptus open-source cloud-computing system. In Cluster Computing and the Grid, 2009. CCGRID’09. 9th IEEE/ACM International Symposium on, pages 124–131. IEEE, 2009. [79] C. S. Pabla. Completely fair scheduler. Linux J., 2009(184), Aug. 2009. [80] V. Pallipadi and A. Starikovskiy. The ondemand governor. In Proceedings of the Linux Symposium, volume 2, pages 215–230. sn, 2006. [81] B. Parhami. Voting algorithms. IEEE transactions on reliability, 43(4):617–629, 1994. [82] W. Reese. Nginx: the high-performance web server and reverse proxy. Linux Journal, 2008(173):2, 2008. [83] R. Rodríguez-Sánchez and E. S. Quintana-Ortí. Architecture-aware optimization of an HEVC decoder on asymmetric multicore processors. Journal of Real-Time Image Processing, 13(1):25–38, 2017. [84] E. Saad, M. Awadalla, M. Shalan, and A. Elewi. Energy-aware task partitioning on heterogeneous multiprocessor platforms. arXiv preprint arXiv:1206.0396, 2012. [85] P. Sanders and J. Speck. Energy efficient frequency scaling and scheduling for malleable tasks. In European Conference on Parallel Processing, pages 167–178. Springer, 2012. [86] W. Seo, D. Im, J. Choi, and J. Huh. Big or little: A study of mobile interactive applications on an asymmetric multi-core platform. In Workload Characterization (IISWC), 2015 IEEE International Symposium on, pages 1–11. IEEE, 2015. [87] A. Shehabi, S. Smith, D. Sartor, R. Brown, M. Herrlin, J. Koomey, E. Masanet, N. Horner, I. Azevedo, and W. Lintner. United states data center energy usage report. 2016. [88] P. Sinha and A. A. Zoltners. The multiple-choice knapsack problem. Operations Research, 27(3):503–515, 1979. [89] S. Soltesz, H. Pötzl, M. E. Fiuczynski, A. Bavier, and L. Peterson. Containerbased operating system virtualization: a scalable, high-performance alternative to hypervisors. In ACM SIGOPS Operating Systems Review, volume 41, pages 275–287. ACM, 2007. [90] S. Sotiriadis, N. Bessis, C. Amza, and R. Buyya. Vertical and horizontal elasticity for dynamic virtual machine reconfiguration. IEEE Transactions on Services Computing, PP(99):1–1, 2016. [91] S. N. Srirama and A. Ostovar. Optimal cloud resource provisioning for auto-scaling enterprise applications. International Journal of Cloud Computing, 7(2):129–162, 2018. [92] Y.-C. Syu. Energy-efficient batch task scheduling for heterogeneous multi-core platforms with per-core DVFS. Master’s thesis, National Taiwan University, 2015. [93] S. Takeda and T. Takemura. A rank-based VM consolidation method for power saving in datacenters. Information and Media Technologies, 5(3):994–1002, 2010. [94] W. Thies, M. Karczmarek, and S. Amarasinghe. Streamit: A language for streaming applications. In International Conference on Compiler Construction, pages 179–196. Springer, 2002. [95] G. Urdaneta, G. Pierre, and M. van Steen. Wikipedia workload analysis for decentralized hosting. Elsevier Computer Networks, 53(11):1830–1845, July 2009. [96] V. V. Vazirani. Approximation Algorithms, chapter Bin Packing, pages 74–78. Springer Verlag, 2001. [97] J. H. Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301):236–244, 1963. [98] C.-M. Wu, R.-S. Chang, and H.-Y. Chan. A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems, 37:141–147, 2014. [99] B. Xia and Z. Tan. Tighter bounds of the first fit algorithm for the bin-packing problem. Discrete Applied Mathematics, 158:1668–1675, August 2010. [100] T. Yamane. Statistics: An introductory analysis. 1973. [101] T. Ye, X. Guangtao, Q. Shiyou, and L. Minglu. An auto-scaling framework for containerized elastic applications. In 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), volume 00, pages 422–430, Aug. 2018. [102] Y. Zhang, L. Duan, B. Li, L. Peng, and S. Sadagopan. Cross-architecture prediction based scheduling for energy efficient execution on single-ISA heterogeneous chipmultiprocessors. Microprocess. Microsyst., 39(4):271–285, June 2015. [103] X. Zhu, C. He, K. Li, and X. Qin. Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters. Journal of parallel and distributed computing, 72(6):751–763, 2012. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71742 | - |
| dc.description.abstract | 在本篇博士論文中，我們對於資料中心內部主要的兩個議題，“如何減少能量消耗”以及“如何保持工作及服務效能”，提出了不同的運算資源分配方式以及工作排程演算法。我們的目標環境是一個提供私有雲服務的企業級資料中心。在目標環境中，服務及工作會先透過虛擬化技術被包裝成虛擬實例，再佈建到伺服器上運行。目前主要的兩種虛擬實例分別為虛擬機器以及容器。每個虛擬實例會有一個規格，限制單位時間內該實例對各項資源的最大使用量。由於工作或服務所需的資源量可能會隨著時間而變動，我們可以透過動態調整運行每個服務的虛擬實例數量及資源配置方式，來達到維持服務和工作效能，以及減少資料中心內部能量消耗的目的。
在本篇博士論文中，我們考慮三個與“如何減少能量消耗” 及“如何保持工作及服務效能” 相關的研究項目，分別是“自動調整網路服務所使用虛擬實例數量”、“虛擬實例至伺服器之節能佈建方式”，以及“多核心伺服器上的節能工作排程”。針對每個項目，我們又分別考慮不同的使用情境，並提出適合的解決方法。 關於“自動調整網路服務所使用虛擬實例數量”，我們根據每個網路服務工作量變化的特性，提出不同的調整演算法。在服務的工作量並不穩定，可能在短時間內有劇烈變化的情境下，我們提出兩種不同的調整演算法，並且套用現實應用的工作量變化軌跡，來衡量這兩種演算法的效能。而當工作量的變化是穩定甚至可預測時，我們則進一步考慮虛擬實例具有不同規格的情況，並提出演算法來最小化因調整各種規格虛擬實例數量而產生的花費。 在“虛擬實例至伺服器之節能佈建方式” 這個項目中，我們考慮兩種情境。首先，在每個虛擬實例的規格是事先給定的情況下，我們考慮如何將資料中心內的虛擬實例聚集在某些伺服器上運行，並將沒有被指派工作的伺服器切換至休眠狀態，藉此減少能量消耗。第二種情境則是每個虛擬實例的規格可以根據啟動當下系統資源使用狀況來做設定。在這種情境下，我們提出不同的演算法，產生包含“虛擬實例可使用的核心數量”、“核心運行頻率”，以及“如何配置虛擬實例至伺服器”的排程計劃。具體的來說，我們提出了在只有少量虛擬實例及伺服器時，能產生最低耗能排程計劃的動態規畫演算法，以及能在合理時間內產生低耗能排程計劃的啟發式演算法。 本篇論文的第三個研究項目是“多核心伺服器上的節能工作排程”。我們考慮兩種不同的多核心平台，分別是同質性多核心以及非對稱多核心平台。在這兩種平台上，我們分別提出不同的排程演算法，來決定如何將工作分配到核心上、工作運行的順序，以及運行工作時每個核心所使用的頻率。針對非對稱多核心平台，我們實作了一個節能排程器來驗證我們的演算法。實驗結果顯示與現有的全域任務排程器搭配不同的調整頻率方式相比，在排程相同工作時，我們的排程器最低僅消耗61.6% 使用全域任務排程器所消耗的能量。 | zh_TW |
| dc.description.abstract | This dissertation proposes computing resource allocation and scheduling algorithms for the two major issues in a data center hosting cloud, i.e to reduce the energy consumption and to guarantee service performance. We focus on an enterprise-level data center supporting a private cloud in this dissertation.
In a data center, tasks and applications are deployed to servers for execution in the form of virtual instances, i.e. virtual machines or containers. Each virtual instance has a specification, which limits the amount of resource the virtual instance can utilize per time unit. Since the resource required by a service may vary over time, we have to dynamically scale the number of virtual instances running a service in order to guarantee the service performance. We can also reduce the energy consumption by adjusting the deployment of virtual instances to servers. We study three topics that are related to service performance and energy saving. These topics are: auto-scaling for web services, energy-efficient virtual instance deployment, and energy-aware task scheduling on a multi-core server. For each topic, we consider different scenarios, and propose solutions accordingly. For auto-scaling for web services, we propose different scaling strategies based on the stability of the workload changes of a web service. For workloads that can change drastically within a short time period, we propose two virtual instance scaling strategies, and compare their performance using real-world workload traces. For workloads with predictable behaviors, we consider the scenario that there are multiple specifications of virtual instances. We propose scaling algorithms that determine the number of each specification of virtual instances for running a web service in each time period, so that the cost is minimized. For energy-efficient virtual instance deployment, we consider two scenarios, virtual instances with fixed specifications, and virtual instances with moldable specifications. For virtual instances with fixed specifications, we propose energy-efficient deployment strategies that consolidate virtual instances onto a subset of servers. The servers that are not assigned with virtual instance are put into sleep mode to save energy. For virtual instances with moldable specifications, we propose algorithms that generate energy-efficient deployment plans. A deployment plan includes: 1) the number of cores allocated to each virtual instance, 2) the operating frequency of each virtual instance, and 3) the deployment of virtual instances to servers. We propose dynamic programming algorithms which generate an optimal deployment plan with minimum energy consumption when the inputs, i.e. the number of virtual instances and servers, are small. We also propose heuristics that generate feasible deployment plans with affordable computing time. The third topic is energy-aware task scheduling on a multi-core server. We consider two types of multi-core platforms, homogeneous and asymmetric. We propose algorithms that determine the assignment of tasks to cores, the execution order of tasks, and the operating frequency of each task for the two types of multi-core platforms. An asymmetric-aware scheduler based on our proposed algorithm is implemented as a proof-of-concept. The scheduler consumes 61:6% and 76:9% energy of the existing Global Task Scheduler with performance and conservative frequency governors respectively. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:08:19Z (GMT). No. of bitstreams: 1 ntu-107-D00922019-1.pdf: 3060151 bytes, checksum: f7ff12a0d43a70c9aff77ae9fa7419db (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii Acknowledgements iii 摘要 iv Abstract vi 2 Introduction 1 3 Auto-scaling for Web Services 8 3.1 Workloads with Unstable Behaviors 9 3.1.1 Metrics Comparison 9 3.1.2 Plurality Vote and Requests-Based Scaling Algorithm 11 3.1.3 Trend Analysis 12 3.1.4 Evaluation 13 3.2 Workloads with Stable Behaviors 17 3.2.1 Problem Formulation 17 3.2.2 Proposed Methods 20 3.2.3 Evaluation 23 3.3 Summary 27 3.3.1 Workloads with Unstable Behaviors 27 3.3.2 Workloads with Stable Behaviors 28 4 Energy-efficient Virtual Instance Deployment 29 4.1 Virtual Instances with Fixed Specifications 30 4.1.1 Problem Definition 30 4.1.2 Dynamic Round-Robin 32 4.1.3 A Hybrid Approach 33 4.1.4 Evaluation 33 4.2 Virtual Instances with Moldable Specifications 39 4.2.1 System Model 40 4.2.2 Problem Formulation 42 4.2.3 Dynamic Programming 44 4.2.4 Heuristic 47 4.2.5 Evaluation 52 4.3 Summary 59 4.3.1 Virtual Instances with Fixed Specifications 59 4.3.2 Virtual Instances with Moldable Specifications 59 5 Energy-aware Task Scheduling on a Multi-core Server 61 5.1 Server with Homogeneous Cores 62 5.1.1 Models 63 5.1.2 Task Scheduling in Batch Mode 65 5.1.3 Task Scheduling in Online Mode 75 5.1.4 Evaluation 79 5.2 Server with Heterogeneous Cores 87 5.2.1 Model 89 5.2.2 Throughput-guaranteed Job Scheduling Problem 91 5.2.3 Generating Job-to-Cluster Mapping 94 5.2.4 Feasible Scheduling Plan Generation 97 5.2.5 Energy-Credit Based Scheduling Framework 106 5.2.6 Evaluation 111 5.3 Summary 119 5.3.1 Homogeneous Multi-core 120 5.3.2 Heterogeneous Multi-core 120 6 Related Works 122 6.1 Auto-scaling for Web Services 122 6.2 Energy-efficient Virtual Instance Deployment 125 6.3 Energy-aware Task Scheduling 127 7 Conclusion and Future Work 132 7.1 Conclusion 132 7.2 Future Work 134 Bibliography 136 | |
| dc.language.iso | en | |
| dc.subject | 雲端運算 | zh_TW |
| dc.subject | 資料中心 | zh_TW |
| dc.subject | 虛擬機器 | zh_TW |
| dc.subject | 容器 | zh_TW |
| dc.subject | 節能 | zh_TW |
| dc.subject | 資源分配 | zh_TW |
| dc.subject | 工作排程 | zh_TW |
| dc.subject | NP 完備性 | zh_TW |
| dc.subject | 演算法 | zh_TW |
| dc.subject | Container | en |
| dc.subject | NP-Complete | en |
| dc.subject | Cloud Computing | en |
| dc.subject | Task Scheduling | en |
| dc.subject | Resource Allocation | en |
| dc.subject | Data Center | en |
| dc.subject | Virtual Machine | en |
| dc.subject | Algorithm | en |
| dc.subject | Energy-efficiency | en |
| dc.title | 私有雲資料中心之運算資源管理 | zh_TW |
| dc.title | Computing Resource Management in a Private Cloud Data Center | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 吳真貞(Jan-Jan Wu),吳邦一(Bang-Ye Wu),趙坤茂(Kun-Mao Chao),歐陽彥正(Yen-Jen OYang) | |
| dc.subject.keyword | 雲端運算,資料中心,虛擬機器,容器,節能,資源分配,工作排程,NP 完備性,演算法, | zh_TW |
| dc.subject.keyword | Cloud Computing,Data Center,Virtual Machine,Container,Energy-efficiency,Resource Allocation,Task Scheduling,NP-Complete,Algorithm, | en |
| dc.relation.page | 146 | |
| dc.identifier.doi | 10.6342/NTU201804372 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-12-21 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 2.99 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。