A survey of machine learning applications for energy-efficient resource management in cloud computing environments

Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Volumn , Issue , 2016, Pages 1185-1190

  Demirci, Mehmet ^a  

^a GAZI UNIVERSITY   (Turkey)

Author keywords

Cloud computing; Data centers; Energy efficiency; Machine learning; Resource management

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLOUD COMPUTING; INFORMATION MANAGEMENT; LEARNING SYSTEMS; MACHINE COMPONENTS; NATURAL RESOURCES MANAGEMENT; OPERATING COSTS; RESOURCE ALLOCATION; SCHEDULING; SURVEYS;

CLOUD COMPUTING ENVIRONMENTS; DATA CENTERS; ENERGY EFFICIENT; MACHINE LEARNING APPLICATIONS; OPTIMAL POLICIES; RESOURCE MANAGEMENT; VIRTUAL MACHINE PLACEMENTS; WORKLOAD CONSOLIDATION;

ENERGY EFFICIENCY;

EID: 84969731098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2015.205     Document Type: Conference Paper

References (41)

1. A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel, "The cost of a cloud: research problems in data center networks," ACM SIGCOMM computer communication review, vol. 39, no. 1, pp. 68-73, 2008.
2. T. Heath, B. Diniz, E. V. Carrera, W. Meira Jr, and R. Bianchini, "Energy conservation in heterogeneous server clusters," in Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming. ACM, 2005, pp. 186-195.
3. G. Chen,W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao, "Energyaware server provisioning and load dispatching for connection-intensive internet services." in NSDI, vol. 8, 2008, pp. 337-350.
4. R. Urgaonkar, U. C. Kozat, K. Igarashi, and M. J. Neely, "Dynamic resource allocation and power management in virtualized data centers," in Network Operations and Management Symposium (NOMS), 2010 IEEE. IEEE, 2010, pp. 479-486.
5. A. Beloglazov and R. 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. IEEE Computer Society, 2010, pp. 826-831.
6. A. Beloglazov, J. Abawajy, and R. Buyya, "Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing," Future generation computer systems, vol. 28, no. 5, pp. 755-768, 2012.
7. A. Gandhi, Y. Chen, D. Gmach, M. Arlitt, and M. Marwah, "Minimizing data center sla violations and power consumption via hybrid resource provisioning," in Green Computing Conference and Workshops (IGCC), 2011 International. IEEE, 2011, pp. 1-8.
8. H. N. Van, F. D. Tran, and J.-M. Menaud, "Performance and power management for cloud infrastructures," in Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on. IEEE, 2010, pp. 329-336.
9. Y. C. Lee and A. Y. Zomaya, "Energy efficient utilization of resources in cloud computing systems," The Journal of Supercomputing, vol. 60, no. 2, pp. 268-280, 2012.
10. J. Xu and J. A. Fortes, "Multi-objective virtual machine placement in virtualized data center environments," in Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int'l Conference on &Int'l Conference on Cyber, Physical and Social Computing (CPSCom). IEEE, 2010, pp. 179-188.
11. Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, "Cloudscale: elastic resource scaling for multi-tenant cloud systems," in Proceedings of the 2nd ACM Symposium on Cloud Computing. ACM, 2011, p. 5.
12. B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown, "Elastictree: Saving energy in data center networks." in NSDI, vol. 10, 2010, pp. 249-264.
13. A. Berl, E. Gelenbe, M. Di Girolamo, G. Giuliani, H. De Meer, M. Q. Dang, and K. Pentikousis, "Energy-efficient cloud computing," The computer journal, vol. 53, no. 7, pp. 1045-1051, 2010.
14. S.-w. Liao, T.-H. Hung, D. Nguyen, C. Chou, C. Tu, and H. Zhou, "Machine learning-based prefetch optimization for data center applications," in Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. ACM, 2009, p. 56.
15. P. Bodik, R. Griffith, C. Sutton, A. Fox, M. Jordan, and D. Patterson, "Statistical machine learning makes automatic control practical for internet datacenters," in Proceedings of the 2009 conference on Hot topics in cloud computing, 2009, pp. 12-12.
16. A. Matsunaga and J. A. Fortes, "On the use of machine learning to predict the time and resources consumed by applications," in Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE Computer Society, 2010, pp. 495-504.
17. C. Gupta, A. Mehta, and U. Dayal, "Pqr: Predicting query execution times for autonomous workload management," in Autonomic Computing, 2008. ICAC'08. International Conference on. IEEE, 2008, pp. 13-22.
18. J. Jiang, J. Lu, G. Zhang, and G. Long, "Optimal cloud resource autoscaling for web applications," in Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on. IEEE, 2013, pp. 58-65.
19. S. Islam, J. Keung, K. Lee, and A. Liu, "Empirical prediction models for adaptive resource provisioning in the cloud," Future Generation Computer Systems, vol. 28, no. 1, pp. 155-162, 2012.
20. "TPC-W," http://www.tpc.org/tpcw/, [Online; accessed 26-August-2015].
21. Z. Gong, X. Gu, and J. Wilkes, "Press: Predictive elastic resource scaling for cloud systems," in Network and Service Management (CNSM), 2010 International Conference on. IEEE, 2010, pp. 9-16.
22. A. A. Bankole and S. A. Ajila, "Predicting cloud resource provisioning using machine learning techniques," in Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on. IEEE, 2013, pp. 1-4.
23. P. Xiong, Y. Chi, S. Zhu, H. J. Moon, C. Pu, and H. Hacigümüş, "Intelligent management of virtualized resources for database systems in cloud environment," in Data Engineering (ICDE), 2011 IEEE 27th International Conference on. IEEE, 2011, pp. 87-98.
24. C.-Z. Xu, J. Rao, and X. Bu, "Url: A unified reinforcement learning approach for autonomic cloud management," Journal of Parallel and Distributed Computing, vol. 72, no. 2, pp. 95-105, 2012.
25. X. Dutreilh, S. Kirgizov, O. Melekhova, J. Malenfant, N. Rivierre, and I. Truck, "Using reinforcement learning for autonomic resource allocation in clouds: Towards a fully automated workflow," in ICAS 2011, The Seventh International Conference on Autonomic and Autonomous Systems, 2011, pp. 67-74.
26. E. Barrett, E. Howley, and J. Duggan, "Applying reinforcement learning towards automating resource allocation and application scalability in the cloud," Concurrency and Computation: Practice and Experience, vol. 25, no. 12, pp. 1656-1674, 2013.
27. J. Rao, X. Bu, C.-Z. Xu, and K. Wang, "A distributed self-learning approach for elastic provisioning of virtualized cloud resources," in Modeling, Analysis &Simulation of Computer and Telecommunication Systems (MASCOTS), 2011 IEEE 19th International Symposium on. IEEE, 2011, pp. 45-54.
28. S. Kundu, R. Rangaswami, A. Gulati, M. Zhao, and K. Dutta, "Modeling virtualized applications using machine learning techniques," in ACM SIGPLAN Notices, vol. 47, no. 7. ACM, 2012, pp. 3-14.
29. C.-J. Huang, Y.-W. Wang, C.-T. Guan, H.-M. Chen, and J.-J. Jian, "Applications of machine learning to resource management in cloud computing," International Journal of Modeling and Optimization, vol. 3, no. 2, p. 148, 2013.
30. A. Fischer, J. F. Botero, M. Till Beck, H. De Meer, and X. Hesselbach, "Virtual network embedding: A survey," Communications Surveys &Tutorials, IEEE, vol. 15, no. 4, pp. 1888-1906, 2013.
31. R. Mijumbi, J.-L. Gorricho, J. Serrat, M. Claeys, F. De Turck, and S. Latré, "Design and evaluation of learning algorithms for dynamic resource management in virtual networks," in Network Operations and Management Symposium (NOMS), 2014 IEEE. IEEE, 2014, pp. 1-9.
32. H. Chen, M. Kesavan, K. Schwan, A. Gavrilovska, P. Kumar, and Y. Joshi, "Spatially-aware optimization of energy consumption in con-solidated data center systems," in ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems. American Society of Mechanical Engineers, 2011, pp. 461-470.
33. N. Vasić, D. Novaković, S. Miučin, D. Kostić, and R. Bianchini, "Dejavu: accelerating resource allocation in virtualized environments," in ACM SIGARCH Computer Architecture News, vol. 40, no. 1. ACM, 2012, pp. 423-436.
34. J. J. Prevost, K. Nagothu, B. Kelley, and M. Jamshidi, "Prediction of cloud data center networks loads using stochastic and neural models," in System of Systems Engineering (SoSE), 2011 6th International Conference on. IEEE, 2011, pp. 276-281.
35. T. V. T. Duy, Y. Sato, and Y. Inoguchi, "Performance evaluation of a green scheduling algorithm for energy savings in cloud computing," in Parallel &Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on. IEEE, 2010, pp. 1-8.
36. M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, "Energy-efficient cloud resource management," in Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on. IEEE, 2014, pp. 386-391.
37. T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Rücksties, and J. Schmidhuber, "Pybrain," The Journal of Machine Learning Research, vol. 11, pp. 743-746, 2010.
38. J. L. Berral, Í. Goiri, R. Nou, F. Julia, J. Guitart, R. Gavalda, and J. Torres, "Towards energy-aware scheduling in data centers using machine learning," in Proceedings of the 1st International Conference on energy-Efficient Computing and Networking. ACM, 2010, pp. 215-224.
39. J. L. Berral, R. Gavalda, and J. Torres, "Adaptive scheduling on poweraware managed data-centers using machine learning," in Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. IEEE Computer Society, 2011, pp. 66-73.
40. G. Tesauro, R. Das, H. Chan, J. Kephart, D. Levine, F. Rawson, and C. Lefurgy, "Managing power consumption and performance of computing systems using reinforcement learning," in Advances in Neural Information Processing Systems, 2007, pp. 1497-1504.
41. G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani, "On the use of hybrid reinforcement learning for autonomic resource allocation," Cluster Computing, vol. 10, no. 3, pp. 287-299, 2007.