Performance modeling to divide performance interference of virtualization and virtual machine combination

IEEE International Conference on Cloud Computing, CLOUD

Volumn , Issue , 2014, Pages 256-263

  Kimura, Daichi ^a   Numata, Eriko ^a   Kawatsu, Masato ^a  

^a NEC CORPORATION   (Japan)

Author keywords

Measurement; Modeling Techniques; Performance model; Queuing model; Virtualization

Indexed keywords

CLOUD COMPUTING; MEASUREMENT; NETWORK SECURITY; QUEUEING THEORY; VIRTUALIZATION;

BLACK BOX APPROACH; MODELING TECHNIQUE; PERFORMANCE MODEL; PHYSICAL RESOURCES; QUEUING MODELS; VIRTUAL MACHINE MONITORS; VIRTUALIZED SYSTEMS; WHITE-BOX MODELS;

VIRTUAL MACHINE;

EID: 84919815874     PISSN: 21596182     EISSN: 21596190     Source Type: Conference Proceeding    
DOI: 10.1109/CLOUD.2014.43     Document Type: Conference Paper

References (25)

1. The R Project for Statistical Computing. http://www.r-project.org/
2. RUBiS: Rice University Bidding System. http://rubis.ow2.org/
3. M. Ahmad, A. Aboulanaga, S. Babu, K. Munagala. Modeling and Exploiting Query Interactions in Database Systems. In Proceedings of the 17th ACM conference on Information and knowledge management, pages 183-192, 2008.
4. D. Ardagna, M. Tanelli, M. Lovera, L. Zhang. Black-box Performance Models for Virtualized Web Service Applications. In Proceedings of the first Joint WOSP/SIPEW International Conference on Performance Engineering, pages 153-163, 2010.
5. V. Chadha, R. J. Figueiredo, R. Illikkal, R. Iyer, J. Moses, D. Newell. I/O Processing in a Virtualized Platform: A Simulation-Driven Approach. In Proceedings of the 3rd international conference on Virtual execution environments, pages 116-125, 2007.
6. I. Epifani, C. Ghezzi, R. Mirandola, G. Tamburrelli. Model Evolution by Run-Time Parameter Adaptation. In Proceedings of the 31st International Conference on Software Engineering, pages 111-121, 2009.
7. N. Huber, S. Becker, C. Rathfelder, J. Schweflinghaus, R. H. Reussner. Performance Modeling in Industry: A Case Study on Storage Virtualization. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, pages 1-10, 2010.
8. S. Izukura, K. Yanoo, T. Osaki, H. Sakaki, D. Kimura, J. Xiang. Applying a Model-Based Approach to IT Systems Development Using SysML Extension. In Proceedings of the 14th international conference on Model driven engineering languages and systems, pages 563-577, 2011.
9. Y. Koh, R. Knauerhase, P. Brett, M. Bowman, Z. Wen, C. Pu. An Analysis of Performance Interference Effects in Virtual Environments. In Proceedings of the 2007 IEEE International Symposium on Performance Analysis of Systems and Software, pages 200-209, 2007.
10. S. Kraft, G. Casale, D. Krishnamurthy, D. Greer, P. Kilpatrick. IO Performance Prediction in Consolidated Virtualized Environments. In Proceedings of the International Conference on Performance Engineering, pages 295-306, 2011.
11. S. Kraft, G. Casale, D. Krishnamurthy, D. Greer, P. Kilpatrick. Performance models of storage contention in cloud environments. Software & Systems Modeling, vol. 12, pages 681-704, 2013.
12. D. Krishnamurthy, J. Rolia, M. Xu. WAM-The Weighted Average Method for Predicting the Performance of Systems with Bursts of Customer Sessions. HP Labs. Technical Reports, HPL-2008-66, 2008.
13. S. Kundu, R. Rangaswami, A. Gulati, M. Zhao, K. Dutta. Modeling Virtualized Applications using Machine Learning Techniques. In Proceedings of the international conference on Virtual execution environments, pages 3-14, 2012.
14. L. Lu, H. Zhang, G. Jiang, H. Chen, K. Yoshihira, E. Smirni. Untangling Mixed Information to Calibrate Resource Utilization in Virtual Machines. In Proceedings of the International Conference on Autonomic Computing, pages 151-160, 2011.
15. Q. Noorshams, D. Bruhn, S. Kounev, R. Reussner. Predictive Performance Modeling of Virtualized Storage Systems using Optimized Statistical Regression Techniques. In Proceedings of the International Conference on Performance Engineering, pages 283-294, 2013.
16. P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant. Automated control of multiple virtualized resources. In Proceedings of the 4th ACM European conference on Computer systems, pages 13-26, 2009.
17. X. Pu, L. Liu, Y. Mei, S. Sivathanu, Y. Koh, C. Pu. Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments. In Proceedings of the IEEE 3rd International Conference on Cloud Computing, pages 51-58, 2010.
18. G. Tesauro, N. K. Jong, R. Das, M. N. Bennani. A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. In Proceedings of the 2006 IEEE International Conference on Autonomic Computing, pages 65-73, 2006.
19. E. Thereska, G. R. Ganger. IRONModel: Robust Performance Models in the Wild. In Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, pages 253-264, 2008.
20. G. Venkatasubramanian, R. J. Figueiredo, R. Illikkal, D. Newell. A Simulation Framework for the Analysis of TLB Behavior in Virtualized Environments. In Proceedings of the 18th Annual IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pages 211-221, 2010.
21. B. J. Watson, M. Marwah, D. Gmach, Y. Chen, M. Arlitt, Z. Wang. Probabilistic Performance Modeling of Virtualized Resource Allocation. In Proceedings of the International Conference on Autonomic Computing, pages 99-108, 2010.
22. T. Wood, L. Cherkasova, K. Ozonat, P. Shenoy. Profiling and Modeling Resource Usage of Virtualized Applications. In Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pages 366-387, 2008.
23. M. Woodside, T. Zheng, M. Litoiu. Service System Resource Management Based on a Tracked Layered Performance Model. In Proceedings of the IEEE International Conference on Autonomic Computing, pages 175-184, 2006.
24. M. Woodside, G. Franks, D. C. Petriu. The Future of Software Performance Engineering. In Proceedings of 2007 Future of Software Engineering, pages 171-187, 2007.
25. Q. Zhang, L. Cherkasova, E. Smirni. A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications. In Proceedings of Fourth International Conference on Autonomic Computing, pages 27-36, 2007.