Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Optimizing Performance and Energy Efficiency in Cloud Data Centers Through SLA-Aware Consolidation of Virtualized Resources

클라우드 데이터 센터에서 가상화된 자원의 SLA-Aware 조정을 통한 성능 및 에너지 효율의 최적화

  • Elijorde, Frank I. (Institute of ICT, West Visayas State University) ;
  • Lee, Jaewan (Dept. of Information and Communication Engineering, Kunsan National University)
  • Received : 2014.01.07
  • Accepted : 2014.04.07
  • Published : 2014.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

클라우드 컴퓨팅은 사용자의 요구에 따라 IT서비스가 생성 및 조정되는 pay-per use 모델을 도입하였다. 그러나 서비스 제공자는 아직도 물리적인 인프라로 인해 발생하는 제약조건들에 대해 관심을 갖고 있다. 필요한 QoS나 SLA를 만족시키기 위해서는 가상화된 자료들이 에너지 소비량을 최소화시키면서 시스템 성능을 최대화시키기 위해 조정되어야 한다. 본 연구는 ANN을 사용하여 클라우드 환경에서 가상화된 자원들을 조정하기 위한 예측적 SLA 어웨어 방안을 제시한다. Qos를 유지하고, 성능과 에너지 효율간의 최적화를 위해서 서버 활용 임계치는 물리적 자원의 소비에 따라 동적으로 적용한다. 또한 많은 자원을 소비하는 VM들은 능력있고 평판이 좋은 호스트에 할당함으로써 부족한 프로비전닝을 방지한다. 제안한 기법의 성능을 평가하기 위해, 이질적인 클라우드 환경에서 최적화되지 않은 전통적인 접근방법 및 기존의 기법들과 비교하였다.

Keywords

References

  1. U. S. Environmental Protection Agency, Report to congress on server and data center energy efficiency public law 109-431. Technical report, EPA ENERGY STAR Program, 2007.
  2. A. Gandhi, M. Harchol-Balter, R. Das, and C. Lefurgy, "Optimal power allocation in server farms", In SIGMETRICS, pp. 157-168, 2009.
  3. N. Bobroff, A. Kochut, and K.A. Beaty, "Dynamic placement of virtual machines for managing sla violations", In Proc. of the 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007.
  4. G. Jung, K.R. Joshi, M.A. Hiltunen, S.D. Schlichting, and C. Pu, "A cost-sensitive adaptation engine for server consolidation of multi-tier applications", Proc. of the 10th ACM/IFIP/USENIX International Conference on Middleware, pp.1-20, 2009.
  5. G. Jung, M. A. Hiltunen, K. R. Joshi, R. D. Schlichting, and C. Pu, "Mistral: Dynamically managing power, performance, and adaptation cost in Cloud infrastructures", In Proc. of the 30th Intl. Conf. on Distributed Computing Systems, pp. 62-73, 2010.
  6. X. Wang and Y. Wang, "Coordinating power control and performance management for virtualized server clusters," IEEE Transactions on Parallel and Distributed Systems (TPDS), pp.245-259, 2011.
  7. A.Beloglazov, J. H. Abawajy, R.Buyya, "Energyaware resource allocation heuristics for efficient management of data centers for Cloud computing", Future Generation Comp. Syst. (FGCS),pp. 755-768, 2012.
  8. R. Nielsen, C. Iversen, and P. Bonnet, "Private Cloud Configuration with MetaConfig", Proc. for IEEE 4th International Conference on Cloud Computing, 2011.
  9. T. Wood, P. J. Shenoy, A. Venkataramani, and M. S. Yousif, "Black-box and gray-box strategies for virtual machine migration", In Proc. of NSDI, 2007.
  10. F. Hermenier, X. Lorca, J.-M. Menaud, G. Muller, and J. Lawall, "Entropy: a consolidation manager for clusters", In Proc. of VEE, 2009.
  11. M. Chen, H. Zhang, Y.-Y. Su, X. Wang, G. Jiang, and K. Yoshihira, "Effective VM sizing in virtualized data centers", In Proc. of the IFIP/IEEE International Symposium on Integrated Network Management, 2011.
  12. M. Bichler, T. Setzer, and B. Speitkamp, "Capacity planning for virtualized servers", In Proc. of the 16th Annual Workshop on Information Technologies and Systems, 2006.
  13. J. Stoess and L. Bellosa, "Energy Management for Hypervisor-based Virtual Machines", In Proc. of IEEE Symposium on USENIX Annual Technical Conference, pp. 28-37, 2007.
  14. J. Heo, D. Henriksson, L. Xue, and T. Abdelzaher, "Integrating adaptive components: An emerging challenge in performance-adaptive systems and a server farm case-study," In Proc. of the 28th IEEE International Real-Time Systems Symposium, pp. 227-238, 2007.
  15. Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, "Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach," IEEE Trans. Parallel Distrib. Syst., pp. 1458-1472, 2008.
  16. G. Khanna, K. A. Beaty, G. Kar, and A. Kochut, "Application performance management in virtualized server environments," In Proc. of Network Operations and Management Symposium (NOMS), pp.373-381, 2006.
  17. B. Li, J. Li, J. Huai, T. Wo, Q. Li, and L. Zhong, "EnaCloud: An Energy Saving Application Live Placement Approach for Cloud Computing Environments", IEEE International Conference on Cloud Computing, pp. 17-24, 2009.
  18. G. Metri, S.Srinivasaraghavan, S.Weisong, M.Brockmeyer, "Experimental Analysis of Application Specific Energy Efficiency of Data Centers with Heterogeneous Servers," Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on , pp.786,793, 2012.
  19. M. Reidmiller, H. Braun, "A Direct Adaptive Method for Faster Back-propagation Learning: The RPRO Algorithm.", In Proc. of the IEEE International Conference on Neural Networks. 1993, p. 135-147.
  20. "CloudSim": a toolkit for modeling and simulation of Cloud computing environments and evaluation of resource provisioning algorithms", Software: Practice and Experience, pp. 23-50, 2011.
  21. Park KS, Pai VS. "CoMon: a mostly-scalable monitoring system for PlanetLab.", ACM SIGOPS Operating Systems Review 2006.
  22. "Amazon EC2 Instance Types", http://aws.amazon.com/ec2/instance-types
  23. "Standard Performance Evaluation Corporation", http://www.spec.org/power_ssj2008/results/res2011q1/power_ssj2008-20110209-00353.html
  24. "Standard Performance Evaluation Corporation", http://www.spec.org/power_ssj2008/results/res2010q2/power_ssj2008-20100315-00239.html
  25. A. Beloglazov and R. Buyya, "Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers", Concurrency and Computation: Practice and Experience (CCPE), John Wiley & Sons, Ltd, pp. 1397-1420, 2012.