IJMECS Vol. 7, No. 4, 8 Apr. 2015
Cover page and Table of Contents: PDF (size: 546KB)
Full Text (PDF, 546KB), PP.61-66
Views: 0 Downloads: 0
Cloud Computing, Virtual Machine, Dynamic Consolidation, Migration
Cloud computing environments have introduced a new model of computing by shifting the location of computing infrastructure to the Internet network to reduce the cost associated with the management of hardware and software resources. The Cloud model uses virtualization technology to effectively consolidate virtual machines (VMs) into physical machines (PMs) to improve the utilization of PMs. Studies however have shown that the average utilization of PMs in many Cloud data centers is still lower than expected. The Cloud model is expected to improve the existing level of utilization by employing new approaches of consolidation mechanisms. In this paper we propose a new approach for dynamic consolidation of VMs in order to maximize the utilization of PMs. This is achieved by a dynamic programing algorithm that selects the best VMs for migration from an overloaded PM, considering the migration overhead of a VM. Evaluation results demonstrate that our algorithms achieve good performance.
Esmail Asyabi, Mohsen Sharifi, "A New Approach for Dynamic Virtual Machine Consolidation in Cloud Data Centers", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.4, pp.61-66, 2015. DOI:10.5815/ijmecs.2015.04.07
[1]Ofer Biran, Antonio Corradi, Mario Fanelli, Luca Foschini, Alexander Nus, Danny Raz and Ezra Silvera, “A Stable Network-Aware VM Placement for Cloud Systems”, 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2012.
[2]Daniel Warneke and Odej Kao, "Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 985-997, June 2011.
[3]Zhen Xiao, Qi Chen and Haipeng Luo, "Automatic Scaling of Internet Applications for Cloud Computing Services," IEEE Transactions on Computers, 30 Nov. 2012.
[4]Zhen Xiao, Weijia Song and Qi Chen, "Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1107-1117, June 2013.
[5]Shekhar Srikantaiah, Aman Kansal and Feng Zhao, “Energy Aware Consolidation for Cloud Computing”, Proceedings of the 2008 conference on Power aware computing and systems, p.10-10, December 07, 2008, San Diego, California.
[6]Anton Beloglazov, Rajkumar Buyya, "Managing Overloaded PMs for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 7, pp. 1366-1379, July 2013.
[7]Wenting Wang, Haopeng Chen and Xi Chen, “An Availability-aware Approach to Resource Placement of Dynamic Scaling in Clouds”, IEEE Fifth International Conference on Cloud Computing, 2012, Honolulu, USA.
[8]Konstantinos Tsakalozos, Mema Roussopoulos, and Alex Delis, “Hint-based Execution of Workloads in Clouds with Nefeli” , ieee transactions on parallel and distributed systems, 2012.
[9]Nicolo Maria Calcavecchia, Ofer Biran, Erez Hadad and Yosef Moatti, "VM Placement Strategies for Cloud Scenarios," cloud, pp.852-859, 2012 IEEE Fifth International Conference on Cloud Computing, 2012.
[10]Michael Cardosa, Aameek Singh, Himabindu Pucha, Abhishek Chandra, "Exploiting Spatio-Temporal Tradeoffs for Energy-Aware MapReduce in the Cloud," IEEE Transactions on Computers, vol. 61, no. 12, pp. 1737-1751, Dec. 2012.
[11]Fan Chung, Ronald Graham, Ranjita Bhagwan, Stefan Savage and Geoffrey M. Voelker, “Maximizing Data Locality in Distributed Systems”, Journal of Computer and System Sciences archive, Volume 72 Issue 8, December, 2006.
[12]Anton Beloglazov, and Rajkumar Buyya, “Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers”, journal of Concurrency and Computation: Practice and Experience, Volume 24, Issue 13, pages 1397–1420, 10 September 2012.
[13]Sherif Sakr, Anna Liu, Daniel M. Batista and Mohammad Alomari, “A Survey of Large Scale Data Management Approaches in Cloud Environments”, IEEE Journal of Communications Surveys & Tutorials, Vol. 13, pp. 311 – 336, 2011.
[14]Sivadon Chaisiri, Bu-Sung Lee, Dusit Niyato, "Optimization of Resource Provisioning Cost in Cloud Computing," IEEE Transactions on Services Computing, vol. 5, no. 2, pp. 164-177, Second 2012.
[15]Cui Lin and Shiyong Lu, “Scheduling Scientific Workflows Elastically for Cloud Computing”, IEEE 4th International Conference on Cloud Computing, 2011, Washington, DC, USA.
[16]Wesam Dawoud, Ibrahim Takouna, and Christoph Meinel, “Elastic Virtual Machine for Fine-grained Cloud Resource Provisioning”, Springer 4th International Conference on Computing and Communication Systems, 2012.
[17]Ui Han, Li Guo, Moustafa M. Ghanem and Yike Guo, "Lightweight Resource Scaling for Cloud Applications," IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), 2012.
[18]Bahman Javadi, Jemal H. Abawajy and Rajkumar Buyya, “Failure-aware resource provisioning for hybrid Cloud infrastructure”, Journal of Parallel and Distributed Computing archive, Volume 72 Issue 10, October, 2012.
[19]Marco Guazzone, Cosimo Anglano, and Massimo Canonico, “Exploiting VM Migration for the Automated Powerand Performance Management of Green Cloud Computing Systems”, springer 1st International Workshop on Energy-Efficient Data Centres (E2DC 2012), Madrid, Spain, May 2012.
[20]Hong Xu and Baochun Li, “Anchor A Versatile and Efficient Framework for Resource Management in the Cloud”, IEEE Transactions on Parallel and Distributed Systems, 2012.
[21]Anton Beloglazov, Jemal Abawajy and Rajkumar Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing”, Future Generation Computer Systems, 2012.