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Adaptive VM Allocation and Migration Approach using Fuzzy Classification and Dynamic Threshold

퍼지 분류 및 동적 임계 값을 사용한 적응형 VM 할당 및 마이그레이션 방식

  • Received : 2017.03.17
  • Accepted : 2017.06.20
  • Published : 2017.08.31

Abstract

With the growth of Cloud computing, it is important to consider resource management techniques to minimize the overall costs of management. In cloud environments, each host's utilization and virtual machine's request based on user preferences are dynamic in nature. To solve this problem, efficient allocation method of virtual machines to hosts where the classification of virtual machines and hosts is undetermined should be studied. In reducing the number of active hosts to reduce energy consumption, thresholds can be implemented to migrate VMs to other hosts. By using Fuzzy logic in classifying resource requests of virtual machines and resource utilization of hosts, we proposed an adaptive VM allocation and migration approach. The allocation strategy classifies the VMs according to their resource request, then assigns it to the host with the lowest resource utilization. In migrating VMs from overutilized hosts, the resource utilization of each host was used to create an upper threshold. In selecting candidate VMs for migration, virtual machines that contributed to the high resource utilization in the host were chosen to be migrated. We evaluated our work through simulations and results show that our approach was significantly better compared to other VM allocation and Migration strategies.

클라우드 컴퓨팅이 발전하면서, 전체적인 관리 비용을 최소화하기 위해 자원 관리 기술이 중요하다. 클라우드 환경에서 사용자 선호도에 기반한 호스트의 활용과 가상머신들의 요구사항은 본질적으로 자주 바뀐다. 이러한 문제를 해결하기 위해, 호스트와 가상 머신들이 분류가 되지 않은 상황에서 효율적인 자원 할당 방법을 연구할 필요가 있다. 에너지 소비를 절약하기 위해 액티브 호스트를 줄일 때, 가상머신들을 다른 호스트로 이주할때 임계값을 사용한다. 가상머신의 자원 요구량과 호스트의 자원 이용량을 분류할 때 Fuzzy Logic을 이용하여 적응성 가상머신 할당 및 이주 방법을 제안한다. 제안한 방법은 자원의 요구량에 따라 가상머신들을 분류한 뒤 가장 적은 자원활용도를 갖는 호스트에게 자원을 할당하며, 과부하된 호스트들로부터 가상머신을 이주시킬 때 상위 임계치를 설정하기 위해 각 호스트들의 자원 활용도가 사용된다. 이주하기 위한 후보 가상머신들을 선택할 때, 호스트에서 높은 자원을 가진 가상머신을 선택한다. 시뮬레이션을 통해 연구 결과를 평가하였고, 평가 결과 다른 가상머신 할당 방법들보다 효율적임을 증명하였다.

Keywords

References

  1. T. Dillon, C. Wu, E. Chang, "Cloud computing: issues and challenges", Advanced Information Networking and Applications (AINA), 24th IEEE International Conference, 2010. https://doi.org/10.1109/AINA.2010.187
  2. J. Zhang, H. Huang, X. Wang, "Resource provision algorithms in cloud computing: A survey", Journal of Network and Computer Applications, vol. 64, pp. 23-42, 2016. https://doi.org/10.1016/j.jnca.2015.12.018
  3. R. K. Gupta, R. K. Pateriya, "Energy Efficient Virtual Machine Placement Approach for Balanced Resource Utilization in Cloud Environment", International Journal of Cloud-Computing and Super-Computing, vol. 2, no. 1, pp. 9-20, 2015. http://dx.doi.org/10.21742/ijcs.2015.2.1.02
  4. M. Shelar, S. Sane, V. Kharat, R. Jadhav, "Efficient Virtual Machine Placement with Energy Saving in Cloud Data Center", International Journal of Cloud-Computing and Super-Computing, vol. 1, no. 1, pp. 15-26, 2014. http://dx.doi.org/10.21742/ijcs.2014.1.1.02
  5. A. Beloglazov, J. Abawajy, R. Buyya, "Energy-aware resource allocation heuristics for efficient management of data centers for Cloud Computing", Future Generation Computer Systems, vol. 28, Issue 5, pp. 755-768, 2012.. https://doi.org/10.1016/j.future.2011.04.017
  6. D. A. Alboaneen, B. Pranggono, H. Tianfield, "Energyaware Virtual Machine Consolidation for Cloud Data Centers", IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014. https://doi.org/10.1109/UCC.2014.166
  7. A. Beloglazov, R. Buyya, "Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers", Proceedings of the 8th International Workshop on Middleware for Grids, Cloud, and e-Science, 2010. https://doi.org/10.1145/1890799.1890803
  8. F. I. Elijorde, J. Lee, "Performance and Energy Oriented Resource Provisioning in Cloud Systems Based on Dynamic Thresholds and Host Reputation", Journal of Korean Society for Internet Information, Vol. 14, No. 5, pp. 39-48, 2013. https://doi.org/10.7472/jksii.2013.14.5.39
  9. Y. Bai, D. Wang, "Fundamentals of Fuzzy Logic Control-Fuzzy Sets, Fuzzy Rules and Defuzzification", Advanced Fuzzy Logic Technologies in Industrial Applications, 2006.
  10. A. Beloglazov, R. Buyya, "Energy Efficient Resource Management in Virtualized Cloud Data Centers", 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010. https://doi.org/10.1109/CCGRID.2010.46
  11. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. Rose, R. Buyya, "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and Experience, Wiley Press, NY, USA, 2010. https://doi.org/10.1002/spe.995
  12. P. Cingolano, J. Alcala-Fdez, "jFuzzyLogic: A Robust and Flexible Fuzzy-Logic Inference System Language Implementation" 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2012. https://doi.org/10.1109/FUZZ-IEEE.2012.6251215
  13. All Published SPECpower_ssj2008 Results, http://www.spec.org/power_ssj2008/results/power_ssj2008.html