International Journal of Education and Management Engineering(IJEME)
ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)
Published By: MECS Press
IJEME Vol.5, No.1, May. 2015
Framework for Provenance based Virtual Machine Placement in Cloud
Full Text (PDF, 290KB), PP.19-26
Due to the high availability of resources in the Cloud Computing platform, there is a tremendous increase in the underutilization of these resources. Improving the throughput and effectively utilizing these resources are two main challenges in the cloud computing scenario. This paper proposes a methodology for improving the throughput and effective utilization of resources by appropriately placing the Virtual Machine in the server that would be more productive. The proposed solution is based on VM placement algorithm and an exclusive framework is designed for this algorithm. This algorithm refers to the history of data which is available in a global provenance database. By utilizing this provenance data, the system performance is improved.
Cite This Paper
R.Narayani, W. Aisha Banu,"Framework for Provenance based Virtual Machine Placement in Cloud", IJEME, vol.5, no.1, pp.19-26, 2015.DOI: 10.5815/ijeme.2015.01.03
Daniel de Oliveira and Fernanda Baioa, A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds, J Grid Computing, 2012; 10:521–552.
R. Buyya,C. Yeo, S. Venugopal, J. Broberg and I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, FGCS, 2009; 25(6):599-616.
Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya, A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems, Advances in Computers, Marvin V. Zelkowitz (editor), ISSN: 0065-2458, Elsevier, 2011; 82: 47-111.
Chun Hui Suen and Ryan K L Ko, S2logger: end-to-end data tracking mechanism for cloud data provenance, Proceedings of the 12th IEEE International Journal, 2013; 594-604.
A. Shankar and U. Bellur, Virtual Machine Placement in Computing Clouds, Thesis, 2010.
J. Xu, J. Fortes, Multi-objective virtual machine placement in virtualized data center environments, IEEE/ACM In. Conference on Green Computing and Communications (GreenCom' 2010), 2010; 179-188.
Madhusree Kuanr, Prithviraj Mohanty and Suresh Chandra Moharana, Grouping-Based Job Scheduling in Cloud computing using Ant Colony Framework, International Journal of Engineering Research and Applications (IJERA), 2013.
K. Tsakalozos, M. Roussopoulos and A. Delis, VM Placement in non-Homogeneous IaaS-Clouds, 9th In. Conference on Service Oriented Computing (ICSOC 2011), 2011.
F. MA, F. LIU and Z. LIU, Multi-objective Optimization for Initial Virtual Machine Placement in Cloud Data Center, J. Information & Computational Science, 2012; 9(16):5029–5038.
X. Li , Z. Qian, S. Lua and J. Wub, Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center, J. Mathematical and Computer Modelling, 2013; 58:1222–1235.
Wang, X. Wang and Y. Chen, Energy-Efficient Virtual Machine Scheduling in Performance-Asymmetric Multi-Core Architectures, 8th In. Conference on Network and Service Management (CNSM 2012): Mini-Conference, 2012; 288-294.
Z. Tang, Y. Mo and K. Li, Dynamic Forecast scheduling algorithm for Virtual Machine Placement in Cloud Computing environment, J. Super computing, 2014;70:1279-1296.
X.Fu and C. Zhou, Virtual Machine selection and placement for Dynamic consolidation in cloud computing environment, Frontier of Computer Science, 2015; 9(2): 322-330.
M.Tawfeek, A, El-sisi, A. Keshk and F. Torkey, Cloud Task Scheduling Based on Ant Colony Optimization, In. Arab Journal of Information technology, 2015; 12(2):129-137.
J. A. Pascual, T. Lorido-botran, J. Miguel-Alonso and J. A. Lozano, Towards a Greener Cloud Infrastructure Management Using Placement Policies, 2014.
G. Zhao, Cost-Aware Scheduling Algorithm Based on PSO in Cloud Computing Environment, In. Journal of Grid and Distributed Computing, 2014; 7(1):33-42.