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Performance and Energy Oriented Resource Provisioning in Cloud Systems Based on Dynamic Thresholds and Host Reputation

클라우드 시스템에서 동적 임계치와 호스트 평판도를 기반으로 한 성능 및 에너지 중심 자원 프로비저닝

  • Elijorde, Frank I. (Institute of ICT, West Visayas State University) ;
  • Lee, Jaewan (Dept. of Information and Communication Engineering, Kunsan National University)
  • Received : 2013.06.02
  • Accepted : 2013.09.10
  • Published : 2013.10.31

Abstract

A cloud system has to deal with highly variable workloads resulting from dynamic usage patterns in order to keep the QoS within the predefined SLA. Aside from the aspects regarding services, another emerging concern is to keep the energy consumption at a minimum. This requires the cloud providers to consider energy and performance trade-off when allocating virtualized resources in cloud data centers. In this paper, we propose a resource provisioning approach based on dynamic thresholds to detect the workload level of the host machines. The VM selection policy uses utilization data to choose a VM for migration, while the VM allocation policy designates VMs to a host based on its service reputation. We evaluated our work through simulations and results show that our work outperforms non-power aware methods that don't support migration as well as those based on static thresholds and random selection policy.

정의된 SLA의 QoS를 지키기 위해서, 클라우드 시스템은 동적인 사용 패턴에서 발생하는 변화무쌍한 작업 부하를 처리해야 한다. 서비스 관점이외에도 에너지 소비를 최소화 하는 것이 또한 새로운 관심사이다. 이는 클라우드 데이타 센터에서 가상화된 자원을 할당할 때 클라우드 제공자들은 에너지와 성능의 상관관계를 고려해야 한다. 본 논문에서는 호스트 컴퓨터의 작업부하 수준을 탐지하기 위해 동적 임계치를 기반으로 한 자원 프로비저닝 방안을 제시한다. VM선정 정책은 이주할 VM을 선택하기 위해 활용 데이터를 사용하며, VM 할당 정책은 서비스 평판도에 따라 VM들을 호스트에 지정한다. 시뮬레이션을 통해 연구결과를 평가하였으며, 시뮬레이션 결과 이주를 지원하지 않는 비 전력 방법뿐만 아니라 동적 임계치, 임의 선정 정책보다 성능이 우수함을 보였다.

Keywords

References

  1. K. Gai, S. Li, Towards Cloud Computing: A Literature Review on Cloud Computing and Its Development Trends, Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on, (2012) pp.142-146.
  2. A. Tantar, Alexandru-Adrian; A. Q. Nguyen; P. Bouvry, B. Dorronsoro, E.G. Talbi: Computational intelligence for cloud management current trends and opportunities, Evolutionary Computation (CEC), 2013 IEEE Congress on, (2013) pp.1286-1293.
  3. A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya: A taxonomy and survey of energy-efficient data centers and cloud computing systems, Univ. of Melbourne, Tech. Rep. CLOUDS-TR-2010-3 (2010).
  4. Khan, X. Yan, T. Shu, N. Anerousis: Workload characterization and prediction in the cloud: A multiple time series approach, Network Operations and Management Symposium, (2012) pp.1287-1294.
  5. A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari: Server workload analysis for power minimization using consolidation, in Proceedings of USENIX Annual Technical Conference (2009).
  6. J. Rolia, L. Cherkasova, M. Arlitt, and A. Andrzejak: A capacity management service for resource pools, In Proceedings of ACM Workshop on Software and Performance (2005).
  7. X. Kong, C. Lin, Y. Jiang, W. Yan, X. Chu: Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction, Journal of Network and Computer Applications, Vol. 34, (2011) pp. 1068-1077. https://doi.org/10.1016/j.jnca.2010.06.001
  8. Y. Zhang, W. Sun, and Y. Inoguchi: CPU Load Predictions on the Computational Grid, in Proc. of IEEE International Symposium on Cluster Computing and the Grid, (2006) pp. 321-326.
  9. E. Feller, L. Rilling, C. Morin: Energy-Aware Ant Colony Based Workload Placement in Clouds, Technical Report, INRIA (2011).
  10. M. Mastroianni, M. Meo, G. Papuzzo: Self-economy in cloud data centers: statistical assignment and migration of virtual machines, In Proc. of the 17th International Conference on Parallel Processing, Vol. 1 (2011).
  11. B. Urgaonkar, P. Shenoy, and et al.: Resource overbooking and application profiling in shared hosting platforms, In Proc. OSDI (2002).
  12. R. Nathuji and K. Schwan, Virtualpower: Coordinated power management in virtualized enterprise systems. ACM SIGOPS Operating Systems Review (2007) pp.265-278.
  13. P. Ranganathan, P. Leech, D. E. Irwin, and J. S. Chase: Ensemble-level power management for dense blade servers, in Proc. of the 33th Annual Intl. Symposium on Computer Architecture (2006).
  14. C. Lefurgy, X. Wang, and M. Ware: Server-level power control, in Proc. of the Intl. Conference on Autonomic Computing (2007).
  15. D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang: Power and performance management of virtualized computing environments via lookahead control, Cluster Computing, vol. 12, no. 1, (2009) pp. 1-15. https://doi.org/10.1007/s10586-008-0070-y
  16. R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F.D. Rose, Buyya R: CloudSim: a toolkit for modeling and simulation of Cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience (2011) pp. 23-50.
  17. "Amazon EC2 Instance Types", http://aws.amazon.com/ec2/instance-types
  18. "Standard Performance Evaluation Corporation", http://www.spec.org/power_ssj2008/results/res2011q1/ power_ssj2008-20110209-00353.html
  19. "Standard Performance Evaluation Corporation", http://www.spec.org/power_ssj2008/results/res2010q2/ power_ssj2008-20100315-00239.html