Dokument

Summary:
In recent years, cloud computing has gained a wide spread use as a new computing model that offers elastic resources on demand, in a pay-as-you-go fashion. One important goal of a cloud provider is dynamic allocation of Virtual Machines (VMs) according to workload changes in order to keep application performance to Service Level Agreement (SLA) levels, while reducing resource costs. The problem is to find an adequate trade-off between the two conflicting objectives of application performance and resource costs. In this dissertation, resource allocation solutions for this trade-off are proposed by expressing application performance and resource costs in a utility function. The proposed solutions allocate VM resources at the global data center level and at the local physical machine level by optimizing the utility function. The utility function, given as the difference between performance and costs, represents the profit of the cloud provider and offers the possibility to capture in a flexible and natural way the performance-cost trade-off. For global level resource allocation, a two-tier resource management solution is developed. In the first tier, local node controllers are located that dynamically allocate resource shares to VMs, so to maximize a local node utility function. In the second tier, there is a global controller that makes VM live migration decisions in order to maximize a global utility function. Experimental results show that optimizing the global utility function by changing the number of physical nodes according to workload maintains the performance at acceptable levels while reducing costs. To allocate multiple resources at the local physical machine level, a solution based on feed-back control theory and utility function optimization is proposed. This dynamically allocates shares to multiple resources of VMs such as CPU, memory, disk and network I/O bandwidth. In addressing the complex non-linearities that exist in shared virtualized infrastructures between VM performance and resource allocations, a solution is proposed that allocates VM resources to optimize a utility function based on application performance and power modelling. An Artificial Neural Network (ANN) is used to build an on- line model of the relationships between VM resource allocations and application performance, and another one between VM resource allocations and physical machine power. To cope with large utility optimization times in the case of an increased number of VMs, a distributed resource manager is proposed. It consists of several ANNs, each responsible for modelling and resource allocation of one VM, while exchanging information with other ANNs for coordinating resource allocations. Experiments, in simulated and realistic environments, show that the distributed ANN resource manager achieves better performance-power trade-offs than a centralized version and a distributed non-coordinated resource manager. To deal with the difficulty of building an accurate online application model and long model adaptation time, a solution that offers model-free resource management based on fuzzy control is proposed. It optimizes a utility function based on a hill-climbing search heuristic implemented as fuzzy rules. To cope with long utility optimization time in the case of an increased number of VMs, a multi-agent fuzzy controller is developed where each agent, in parallel with others, optimizes its own local utility function. The fuzzy control approach eliminates the need to build a model beforehand and provides a robust solution even for noisy measurements. Experimental results show that the multi-agent fuzzy controller performs better in terms of utility value than a centralized fuzzy control version and a state-of-the-art adaptive optimal control approach, especially for an increased number of VMs. Finally, to address some of the problems of reactive VM resource allocation approaches, a proactive resource allocation solution is proposed. This approach decides on VM resource allocations based on resource demand prediction, using a machine learning technique called Support Vector Machine (SVM). To deal with interdependencies between VMs of the same multi-tier application, cross- correlation demand prediction of multiple resource usage time series of all VMs of the multi-tier application is applied. As experiments show, this results in improved prediction accuracy and application performance.

Bibliographie / References

1. [4] Amazon elastic compute cloud (amazon EC2). http://aws.amazon.com/ec2/.
2. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
3. Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin. Machine learning for on-line hardware reconfiguration. In Proc. 20th In- ternational Joint Conference on Artifical Intelligence, pages 1113–1118. Morgan Kaufmann Publishers Inc., 2007.
4. Poul-Henning Kamp and Robert N. M. Watson. Jails: Confining the omnipotent root. In Proc. 2nd International System Administration and Networking Conference, SANE 2000.
5. Luiz André Barroso and Urs Hölzle. The case for energy-proportional computing. Computer, 40(12):33–37, 2007.
6. Taliver Heath, Bruno Diniz, Enrique V. Carrera, Wagner Meira, Jr., and Ricardo Bianchini. Energy conservation in heterogeneous server clusters. In Proc. 10th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 186–195. ACM Press, 2005.
7. Younggyun Koh, Rob C. Knauerhase, Paul Brett, Mic Bowman, Zhihua Wen, and Calton Pu. An analysis of performance interference effects in virtual environments. In Proc. IEEE International Symposium on Per- formance Analysis of Systems and Software., pages 200–209. IEEE Press, 2007.
8. Qi Zhang, Ludmila Cherkasova, and Evgenia Smirni. A regression-based analytic model for dynamic resource provisioning of multi-tier applica- tions. In Proc. Fourth International Conference on Autonomic Comput- ing, ICAC '07, page 27. IEEE Press, 2007.
9. Diwaker Gupta, Ludmila Cherkasova, Rob Gardner, and Amin Vah- dat. Enforcing performance isolation across virtual machines in xen. In Proc. ACM/IFIP/USENIX International Conference on Middleware, pages 342–362. Springer-Verlag Inc., 2006.
10. Peter Bodík, Rean Griffith, Charles Sutton, Armando Fox, Michael Jor- dan, and David Patterson. Statistical machine learning makes automatic control practical for internet datacenters. In Proc. Conference on Hot Topics in Cloud Computing. USENIX Association, 2009.
11. Harold C. Lim, Shivnath Babu, Jeffrey S. Chase, and Sujay S. Parekh. Automated control in cloud computing: Challenges and opportunities. In Proc. 1st Workshop on Automated Control for Datacenters and Clouds, ACDC '09, pages 13–18. ACM Press, 2009.
12. Peter Bodik, Rean Griffith, Charles Sutton, Armando Fox, Michael I. Jordan, and David A. Patterson. Automatic exploration of datacenter performance regimes. In Proc. 1st Workshop on Automated Control for Datacenters and Clouds, ACDC '09, pages 1–6. ACM Press, 2009.
13. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The weka data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1):10–18, 2009.
14. Michael Cardosa, Madhukar R. Korupolu, and Aameek Singh. Shares and utilities based power consolidation in virtualized server environments. In Proc. 11th IFIP/IEEE International Symposium on Integrated Network Management (IM'09), pages 327–334. IEEE Press, 2009.
15. Luis M. Vaquero, Luis Rodero-Merino, Juan Caceres, and Maik Lindner. A break in the clouds: Towards a cloud definition. ACM SIGCOMM Computer Communication Review., 39:50–55, 2008.
16. Anton Beloglazov and Rajkumar Buyya. Adaptive threshold-based ap- proach for energy-efficient consolidation of virtual machines in cloud data centers. In Proc. 8th International Workshop on Middleware for Grids, Clouds and e-Science, MGC '10, pages 4:1–4:6. ACM, 2010. Bibliography
17. Y. Diao, J. L. Hellerstein, and S. Parekh. Using fuzzy control to maximize profits in service level management. IBM Syst. J., 41(3):403–420, 2002.
18. Jin Heo, Xiaoyun Zhu, Pradeep Padala, and Zhikui Wang. Memory over- booking and dynamic control of xen virtual machines in consolidated envi- ronments. In Proc. 11th IFIP/IEEE international conference on Sympo- sium on Integrated Network Management (IM'09), pages 630–637. IEEE Press, 2009.
19. Pradeep Padala, Kai-Yuan Hou, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, Sharad Singhal, and Arif Merchant. Automated control of multiple virtualized resources. In Proc. 4th ACM European Conference on Computer Systems, pages 13–26. ACM Press, 2009.
20. A. Kivity, Y. Kamay, D. Laor, U. Lublin, and A. Liguori. kvm: the Linux virtual machine monitor. In Proc. Linux Symposium, pages 225–230, 2007.
21. Sajib Kundu, Raju Rangaswami, Ajay Gulati, Ming Zhao, and Kaushik Dutta. Modeling virtualized applications using machine learning tech- niques. In Proc. 8th ACM SIGPLAN/SIGOPS Conference on Virtual Execution Environments, pages 3–14. ACM Press, 2012.
22. Harold C. Lim, Shivnath Babu, and Jeffrey S. Chase. Automated control for elastic storage. In Proc. 7th International Conference on Autonomic Computing, ICAC '10, pages 1–10. ACM Press, 2010.
23. Anton Beloglazov and Rajkumar Buyya. Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Dis- tributed Systems, 24(7):1366–1379, 2013.
24. Paul Barham, Boris Dragovic, Keir Fraser, Steven Hand, Tim Harris, Alex Ho, Rolf Neugebauer, Ian Pratt, and Andrew Warfield. Xen and the art of virtualization. In Proc. 19th ACM Symposium on Operating Systems Principles, pages 164–177. ACM Press, 2003.
25. Sadeka Islam, Jacky Keung, Kevin Lee, and Anna Liu. Empirical pre- diction models for adaptive resource provisioning in the cloud. Future Gener. Comput. Syst., 28(1):155–162, January 2012.
26. Scott E. Fahlman and Christian Lebiere. The cascade-correlation learning architecture. In Proc. Advances in Neural Information Processing Systems 2, pages 524–532. Morgan Kaufmann, 1990.
27. Xiaoqiao Meng, Canturk Isci, Jeffrey Kephart, Li Zhang, Eric Bouillet, and Dimitrios Pendarakis. Efficient resource provisioning in compute clouds via vm multiplexing. In Proc. 7th International Conference on Autonomic Computing, ICAC '10, pages 11–20. ACM Press, 2010.
28. Mohamed N. Bennani and Daniel A. Menasce. Resource allocation for autonomic data centers using analytic performance models. In Proc. 2nd International Conference on Automatic Computing, pages 229–240. IEEE Press, 2005.
29. Pradeep Padala, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, Sharad Singhal, Arif Merchant, and Kenneth Salem. Adaptive control of virtualized resources in utility computing environments. In Proc. 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, EuroSys '07, pages 289–302. ACM Press, 2007.
30. Bhuvan Urgaonkar, Prashant Shenoy, Abhishek Chandra, Pawan Goyal, and Timothy Wood. Agile dynamic provisioning of multi-tier internet ap- plications. ACM Trans. Auton. Adapt. Syst., 3(1):1:1–1:39, March 2008.
31. Zhikui Wang, Xiaoyun Zhu, Sharad Singhal, and Hewlett Packard. Uti- lization and slo-based control for dynamic sizing of resource partitions. In Proc. 16th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, pages 24–26. Springer-Verlag, 2005.
32. William E. Walsh, Gerald Tesauro, Jeffrey O. Kephart, and Rajarshi Das. Utility functions in autonomic systems. In Proc. First International Con- ference on Autonomic Computing (ICAC'04), pages 70–77. IEEE Press, 2004.
33. Rui Han, Li Guo, Moustafa M. Ghanem, and Yike Guo. Lightweight resource scaling for cloud applications. In Proc. 12th IEEE/ACM Inter- national Symposium on Cluster, Cloud and Grid Computing, CCGRID '12, pages 644–651. IEEE Press, 2012.
34. Jeffrey Dean and Sanjay Ghemawat. Mapreduce: Simplified data pro- cessing on large clusters. Commun. ACM, 51:107–113, 2008.
35. Minkyong Kim and Brian Noble. Mobile network estimation. In Proc. 7th Annual International Conference on Mobile Computing and Networking (MobiCom '01), pages 298–309. ACM, 2001.
36. Jing Xu, Ming Zhao, José Fortes, Robert Carpenter, and Mazin Yousif. Autonomic resource management in virtualized data centers using fuzzy logic-based approaches. Cluster Computing, 11(3):213–227, September 2008.
37. Tiago C. Ferreto, Marco Aurlio Stelmar Netto, Rodrigo N. Calheiros, and Csar A. F. De Rose. Server consolidation with migration control for virtualized data centers. Future Generation Comp. Syst., 27(8):1027– 1034, 2011.
38. Anton Beloglazov, Jemal H. Abawajy, and Rajkumar Buyya. Energy- aware resource allocation heuristics for efficient management of data cen- ters for cloud computing. Future Generation Comp. Syst., 28(5):755–768, 2012.
39. Jing Jiang, Jie Lu, Guangquan Zhang, and Guodong Long. Optimal cloud resource auto-scaling for web applications. In Proc. 13th IEEE/ACM Bibliography International Symposium on Cluster, Cloud and Grid Computing, CC- Grid'13, pages 58–65. IEEE Press, 2013.
40. Dorian Minarolli and Bernd Freisleben. Utility-driven allocation of mul- tiple types of resources to virtual machines in clouds. In Proc. IEEE 13th Conference on Commerce and Enterprise Computing, CEC '11, pages 137–144. IEEE Press, 2011.
41. Dorian Minarolli and Bernd Freisleben. Virtual machine resource allo- cation in cloud computing via multi-agent fuzzy control. In Proc. 2013 International Conference on Cloud and Green Computing, CGC '13, pages 188–194. IEEE Press, 2013.
42. Dara Kusic, Jeffrey O. Kephart, James E. Hanson, Nagarajan Kan- dasamy, and Guofei Jiang. Power and performance management of vir- tualized computing environments via lookahead control. In Proc. 2008 International Conference on Autonomic Computing (ICAC'08), pages 3– 12. IEEE Press, 2008.
43. Daniel A. Menasce and Mohamed N. Bennani. Autonomic virtualized environments. In Proc. International Conference on Autonomic and Au- tonomous Systems, pages 28–38. IEEE Press, 2006.
44. Rodrigo N. Calheiros, Rajiv Ranjan, and Rajkumar Buyya. Virtual ma- chine provisioning based on analytical performance and qos in cloud com- puting environments. In Proc. International Conference on Parallel Pro- cessing, ICPP '11, pages 295–304. IEEE Press, 2011.
45. Italo S. Cunha, Jussara M. Almeida, Virgilio Almeida, and Marcos San- tos. Self-adaptive capacity management for multi-tier virtualized environ- ments. In Integrated Network Management, pages 129–138. IEEE Press, 2007.
46. D. Minarolli and B. Freisleben. Utility-based resource allocation for vir- tual machines in cloud computing. In Proc. IEEE Symposium on Com- puters and Communications, ISCC '11, pages 410–417. IEEE Press, 2011. Bibliography
47. Ahmed Ali-Eldin, Johan Tordsson, and Erik Elmroth. An adaptive hy- brid elasticity controller for cloud infrastructures. In Proc. IEEE/IFIP Network Operations and Management Symposium, NOMS '12, pages 204– 212. IEEE Press, 2012.
48. Masum Z. Hasan, Edgar Magana, Alexander Clemm, Lew Tucker, and Sree Lakshmi D. Gudreddi. Integrated and autonomic cloud resource scaling. In Proc. IEEE/IFIP Network Operations and Management Sym- posium, NOMS'12, pages 1327–1334. IEEE Press, 2012.
49. Evangelia Kalyvianaki, Themistoklis Charalambous, and Steven Hand. Self-adaptive and self-configured cpu resource provisioning for virtualized servers using kalman filters. In Proceedings of the 6th International Con- ference on Autonomic Computing, ICAC '09, pages 117–126. ACM Press, 2009.
50. Dimitris Tsirogiannis, Stavros Harizopoulos, and Mehul A. Shah. Ana- lyzing the energy efficiency of a database server. In Proc. ACM SIGMOD International Conference on Management of Data, pages 231–242. ACM Press, 2010.
51. Jia Rao, Xiangping Bu, Kun Wang, and Cheng-Zhong Xu. Self-adaptive provisioning of virtualized resources in cloud computing. In Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, SIGMETRICS '11, pages 129–130. ACM, 2011.
52. Palden Lama and Xiaobo Zhou. Autonomic provisioning with self- adaptive neural fuzzy control for percentile-based delay guarantee. ACM Trans. Auton. Adapt. Syst., 8(2):9:1–9:31, July 2013.
53. Gerald J. Popek and Robert P. Goldberg. Formal requirements for vir- tualizable third generation architectures. Communications of the ACM, 17:412–421, 1974.
54. Guofu Feng, Saurabh Garg, Rajkumar Buyya, and Wenzhong Li. Revenue maximization using adaptive resource provisioning in cloud computing environments. In Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing, GRID '12, pages 192–200. IEEE Press, 2012.
55. Enda Barrett, Enda Howley, and Jim Duggan. Applying reinforcement learning towards automating resource allocation and application scalabil- ity in the cloud. Concurrency and Computation: Practice and Experience, 25(12):1656–1674, 2013.
56. Alex J. Smola and Bernhard Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14:199–222, 2004.
57. Martin Riedmiller and Heinrich Braun. A direct adaptive method for faster backpropagation learning: The rprop algorithm. In Proc. IEEE In- ternational Conference on Neural Networks, pages 586–591. IEEE Press, 1993.
58. N Bobroff, A Kochut, and K Beaty. Dynamic placement of virtual ma- chines for managing sla violations. In Proc. 10th IFIP/IEEE International Symposium on Integrated Network Management (IM'07), pages 119–128. IEEE Press, 2007.
59. Jonathan Wildstrom, Peter Stone, and Emmett Witchel. Carve: A cogni- tive agent for resource value estimation. In Proc. 5th IEEE International Conference on Autonomic Computing, pages 182–191. IEEE Press, 2008.
60. Nicholas I. Sapankevych and Ravi Sankar. Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2):24–38, 2009.
61. Gueyoung Jung, Matti A. Hiltunen, Kaustubh R. Joshi, Richard D. Schlichting, and Calton Pu. Mistral: Dynamically managing power, per- formance, and adaptation cost in cloud infrastructures. In Proc. IEEE 30th International Conference on Distributed Computing Systems, ICDCS '10, pages 62–73. IEEE Computer Society, 2010.
62. Bolei Zhang, Zhuzhong Qian, Wei Huang, Xin Li, and Sanglu Lu. Mini- mizing communication traffic in data centers with power-aware vm place- ment. In Proc. Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS '12, pages 280–285. IEEE Press, 2012.
63. Qingjia Huang, Sen Su, Siyuan Xu, Jian Li, Peng Xu, and Kai Shuang. Migration-based elastic consolidation scheduling in cloud data center. In Proc. IEEE 33rd International Conference on Distributed Computing Sys- tems Workshops, pages 93–97. IEEE Press, 2013. [55] Intel Corporation. Hardware-based intel virtualization technology. http://www.intel.com/content/www/us/en/virtualization/virtualization- technology/hardware-assist-virtualization-technology.html, 2014.
64. Dorian Minarolli and Bernd Freisleben. Distributed resource allocation to virtual machines via artificial neural networks. In Proc. 22Nd Euromi- cro International Conference on Parallel, Distributed, and Network-Based Processing, PDP '14, pages 490–499. IEEE Press, 2014.
65. Dorian Minarolli and Bernd Freisleben. Cross-correlation prediction of re- source demand for virtual machine resource allocation in clouds. In Proc. 6th International Conference on Computational Intelligence, Communi- cation Systems and Networks, CICSYN'14, pages 119–124. IEEE Press, 2014.
66. Michael Ferdman, Almutaz Adileh, Onur Kocberber, Stavros Volos, Mohammad Alisafaee, Djordje Jevdjic, Cansu Kaynak, Adrian Daniel Popescu, Anastasia Ailamaki, and Babak Falsafi. Clearing the clouds: A study of emerging scale-out workloads on modern hardware. In Proc.
67. Matthew Wall. A C++ Library of Genetic Algorithm Components. http://lancet.mit.edu/ga/.
68. Steffen Nissen. Fast Artificial Neural Network Library. http://leenissen.dk/fann/wp/.
69. Karl Johan Astrom and Bjorn Wittenmark. Adaptive Control. Addison- Wesley Longman Publishing Co., Inc., 2nd edition, 1994.
70. Mauro Andreolini, Sara Casolari, Michele Colajanni, and Michele Messori. Dynamic load management of virtual machines in cloud architectures. In CloudComp, volume 34 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pages 201–214. Springer, 2009.
71. Herbert Potzl. Linux-vserver technology. http://linux-vserver.org/Paper, 2014.
72. John H. Holland. Adaptation in Natural and Artificial Systems. MIT Press, 1992.
73. John C. Platt. Advances in kernel methods. chapter Fast Training of Support Vector Machines Using Sequential Minimal Optimization, pages 185–208. MIT Press, 1999.
74. Hiep Nguyen, Zhiming Shen, Xiaohui Gu, Sethuraman Subbiah, and John Wilkes. Agile: Elastic distributed resource scaling for infrastructure-as-a- service. In Proc. 10th International Conference on Autonomic Computing, pages 69–82. USENIX Association, 2013.
75. Karlsson Magnus, Zhu Xiaoyun, and Karamanolis Christos. An adaptive optimal controller for non-intrusive performance differentiation in com- puting services. In Proc. IEEE Conference on Control and Automation, pages 709–714. IEEE press, 2005.
76. Rudolph Emil Kalman. A new approach to linear filtering and predic- tion problems. Transactions of the ASME-Journal of Basic Engineering, 82:35–45, 1960.
77. Henry Hoffmann, Jonathan Eastep, Marco D. Santambrogio, Jason E. Miller, and Anant Agarwal. Application heartbeats: a generic interface for specifying program performance and goals in autonomous computing environments. In Proc. International Conference on Autonomic Comput- ing, pages 79–88. ACM Press, 2010.
78. G Khanna, K Beaty, G Kar, and A Kochut. Application performance management in virtualized server environments. In Proc. 10th IEEE/IFIP Network Operations and Management Symposium (NOMS 2006), pages 373–381. IEEE Press, 2006.
79. Earl Bryson Arthur and Yu-Chi Ho. Applied Optimal Control: Optimiza- tion, Estimation and Control. Blaisdell Pub. Co., 1st edition, 1969.
80. Jeff Dike. A user-mode port of the linux kernel. In Proc. 4th Annual Linux Showcase & Conference -Volume 4, ALS'00, pages 7–7. USENIX Association, 2000.
81. Werner Vogels. Beyond server consolidation. Queue, 6:20–26, 2008.
82. Jing Bi, Zhiliang Zhu, Ruixiong Tian, and Qingbo Wang. Dynamic pro- visioning modeling for virtualized multi-tier applications in cloud data center. In Proc. IEEE 3rd International Conference on Cloud Comput- ing, CLOUD '10, pages 370–377. IEEE Press, 2010.
83. Yagiz Onat Yazir, Chris Matthews, Roozbeh Farahbod, Stephen Neville, Adel Guitouni, Sudhakar Ganti, and Yvonne Coady. Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis. In Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing, CLOUD '10, pages 91–98. IEEE Press, 2010.
84. Jia Rao, Yudi Wei, Jiayu Gong, and Cheng-Zhong Xu. Dynaqos: model- free self-tuning fuzzy control of virtualized resources for qos provisioning. In Proc. 19th International Workshop on Quality of Service, pages 1–9. IEEE Press, 2011.
85. 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 Proc. IEEE International Conference on Cloud Computing (CLOUD), pages 17–24. IEEE Press, 2009.
86. Kevin M. Passino and Stephen Yurkovich. Fuzzy Control. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1997. Bibliography [92] Vinicius Petrucci, Orlando Loques, and Daniel Mossé. A dynamic op- timization model for power and performance management of virtualized clusters. In Proc. 1st International Conference on Energy-Efficient Com- puting and Networking, e-Energy '10, pages 225–233. ACM Press, 2010.
87. L.A. Zadeh. Fuzzy sets. Information Control, 8:338–353, 1965.
88. Liang Liu, Hao Wang, Xue Liu, Xing Jin, Wen Bo He, Qing Bo Wang, and Ying Chen. Greencloud: A new architecture for green data center. In Proceedings of the 6th International Conference Industry Session on Auto- nomic Computing and Communications Industry Session, ICAC-INDST '09, pages 29–38. ACM Press, 2009.
89. Stephen Hemminger. Iproute2 Homepage at Linux Foundation.
90. Xavier Dutreilh, Nicolas Rivierre, Aurlien Moreau, Jacques Malenfant, and Isis Truck. From data center resource allocation to control theory and back. In Proc. 3rd IEEE International Conference on Cloud Computing, CLOUD'10, pages 410–417. IEEE Press, 2010.
91. Rongdong Hu, Jingfei Jiang, Guangming Liu, and Lixin Wang. Kswsvr: A new load forecasting method for efficient resources provisioning in cloud. In Proc. IEEE International Conference on Services Computing, SCC '13, pages 120–127. IEEE Press, 2013.
92. Ronald P. Doyle. Model-based resource provisioning in a web service utility. In Proc. 4th USENIX Symposium on Internet Technologies and Systems, pages 5–5. USENIX Association, 2003.
93. K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward net- works are universal approximators. Neural Networks, 2(5):359–366, 1989.
94. Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, and Mohamed N. Ben- nani. On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Computing, 10(3):287–299, 2007.
95. Zhenhuan Gong and Xiaohui Gu. Pac: Pattern-driven application consol- idation for efficient cloud computing. In Proc. IEEE International Sym- posium on Modeling, Analysis and Simulation of Computer and Telecom- munication Systems, MASCOTS'10, pages 24–33. IEEE Press, 2010.
96. Akshat Verma, Puneet Ahuja, and Anindya Neogi. pmapper: Power and migration cost aware application placement in virtualized systems. In Proc. ACM/IFIP/USENIX 9th International Middleware Conference (Middleware '08), pages 243–264. Springer-Verlag, 2008.
97. Zhenhuan Gong, Xiaohui Gu, and John Wilkes. Press: Predictive elastic resource scaling for cloud systems. In Proc. International Conference on Network and Service Management (CNSM'10), pages 9–16. IEEE Press, 2010.
98. Wei Fang, ZhiHui Lu, Jie Wu, and ZhenYin Cao. Rpps: A novel resource prediction and provisioning scheme in cloud data center. In Proc. 9th IEEE International Conference on Services Computing, SCC '12, pages 609–616. IEEE Press, 2012.
99. Hien Nguyen Van, Frederic Dang Tran, and Jean-Marc Menaud. Sla- aware virtual resource management for cloud infrastructures. In Proc. Ninth IEEE International Conference on Computer and Information Technology (CIT '09), pages 357–362. IEEE Press, 2009.
100. Hadi Goudarzi, Mohammad Ghasemazar, and Massoud Pedram. Sla- based optimization of power and migration cost in cloud computing. In Proc. 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012), CCGRID '12, pages 172–179. IEEE Press, 2012.
101. Jason Sonnek, James Greensky, Robert Reutiman, and Abhishek Chan- dra. Starling: Minimizing communication overhead in virtualized com- puting platforms using decentralized affinity-aware migration. In Proc. Bibliography 39th International Conference on Parallel Processing, ICPP '10, pages 228–237. IEEE Press, 2010.
102. Jia Rao, Xiangping Bu, Cheng-Zhong Xu, Leyi Wang, and George Yin. Vconf: A reinforcement learning approach to virtual machines auto- configuration. In Proc. 6th International Conference on Autonomic Com- puting, pages 137–146. ACM Press, 2009.
103. Microsoft. Windows virtual pc. http://www.microsoft.com/windows/virtual- pc/, 2014.
104. [23] Community project, supported by Parallels, Inc. OpenVZ, Linux con- tainers. http://openvz.org/Main Page, 2014.
105. [79] Microsoft Windows Azure. https://www.windowsazure.com/en-us/, 2014.
106. Timothy Wood, Prashant Shenoy, Arun Venkataramani, and Mazin Yousif. Sandpiper: Black-box and gray-box resource management for virtual machines. Comput. Netw., 53(17):2923–2938, 2009.
107. [36] Filebench: file system and storage benchmark. http://sourceforge.net/apps/mediawiki/filebench/index.php.
108. Sourceforge, Open Source Software. Dm-ioband Project Page. http://sourceforge.net/apps/trac/ioband/, 2011.
109. Response times of three applications for the two approaches . . . 143 –155– Bibliography Bibliography [1] Advanced Micro Devices, Inc. Amd virtualization technology. http://www.amd.com/en-us/solutions/servers/virtualization, 2014.
110. George E. P. Box, Gwilym M. Jenkins, and Gregory C. Reinsel. Time Series Analysis: Forecasting and Control. Wiley, 2008.
111. Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul, Christian Limpach, Ian Pratt, and Andrew Warfield. Live migration of virtual machines. In Proc. 2nd Conference on Symposium on Networked Systems Design and Implementation, NSDI'05, pages 273–286. USENIX Association, 2005.
112. Microsoft. Microsoft virtual server. http://www.microsoft.com/virtualserver/, 2014.
113. Ripal Nathuji, Aman Kansal, and Alireza Ghaffarkhah. Q-clouds: man- aging performance interference effects for qos-aware clouds. In Proc. 5th European conference on Computer systems (EuroSys '10), pages 237–250. ACM Press, 2010. [86] National Institute of Standards and Technology (NIST). http://www.nist.gov/, 2014.
114. Waheed Iqbal, Matthew N. Dailey, David Carrera, and Paul Janecek. Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gener. Comput. Syst., 27(6):871–879, June 2011.
115. Cheng-Zhong Xu, Jia Rao, and Xiangping Bu. Url: A unified reinforce- ment learning approach for autonomic cloud management. J. Parallel Distrib. Comput., 72(2):95–105, 2012.
116. VMWare Inc. VMWare Homepage. http://www.vmware.com/, 2011. [113] VMware, Inc. Vmware esx server. http://www.vmware.com/products/esxi-and-esx/overview, 2014.
117. Xavier Dutreilh, Sergey Kirgizov, Olga Melekhova, Jacques Malenfant, Nicolas Rivierre, and Isis Truck. Using Reinforcement Learning for Au- tonomic Resource Allocation in Clouds: towards a fully automated work- flow. In Proc. Seventh International Conference on Autonomic and Au- tonomous Systems, ICAS 2011, pages 67–74. IEEE Press, May 2011. MoVe INT LIP6.
118. V.J. Rayward-Smith. Modern heuristic search methods. Wiley, 1996.

Das Dokument ist im Internet frei zugänglich - Hinweise zu den Nutzungsrechten