Random Articles
Reseach Article
Workload Consolidation using VM Selection and Placement Techniques in Cloud Computing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 137 - Number 4 |
| Year of Publication: 2016 |
| Authors: Monika Patel, Hiren Patel, Nimisha Patel |
10.5120/ijca2016908676
|
Monika Patel, Hiren Patel, Nimisha Patel . Workload Consolidation using VM Selection and Placement Techniques in Cloud Computing. International Journal of Computer Applications. 137, 4 ( March 2016), 8-11. DOI=10.5120/ijca2016908676
Abstract
Cloud computing provides a consumer pay-per-use computing model over the Internet using numerous data centers across the globe. Power consumption by the huge data centers in Cloud environment has attracted the attention of research community. Efficient usage of energy in Cloud can be addressed in many facets. Virtual Machine (VM) consolidation is one of the techniques to save or reduce energy in virtualized data centers. VM Migration in Cloud also provides us an opportunity for reducing energy consumption. In this research, we intend to study various VM placements & selection policies and VM migration algorithms for underloaded and overloaded hosts to reduce energy consumption and SLA violation. We propose a novel method using combination of two methods, Least Increase Power (LIP) consumption with Host Sort and Minimum Correlation Coefficient (MCC) for consolidation of VM placement, placing a migratable VM on a host based on utilization thresholds. The results show performance of each combination of algorithms varies with the changing value of the parameters brings better in terms of energy consumption, VM migration time and SLA violation. The reader may plunge the appropriate method for energy consumption.
References
- Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008, November). Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop, 2008. GCE'08 (pp. 1-10). Ieee.
- Van, H. N., Tran, F. D., & Menaud, J. M. (2010, July). Performance and power management for cloud infrastructures. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on (pp. 329-336). IEEE.
- Cao, Z., & Dong, S. (2014). An energy-aware heuristic framework for virtual machine consolidation in cloud computing. The Journal of Supercomputing,69(1), 429-451.
- Fu, X., & Zhou, C. (2015). Virtual machine selection and placement for dynamic consolidation in Cloud computing environment. Frontiers of Computer Science, 9(2), 322-330.
- Beloglazov, A., & Buyya, R. (2012). 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, 24(13), 1397-1420.
- Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
- Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., ... & Warfield, A. (2005, May). Live migration of virtual machines. In Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2 (pp. 273-286). USENIX Association.
- Huang, J., Wu, K., & Moh, M. (2014, July). Dynamic Virtual Machine migration algorithms using enhanced energy consumption model for green cloud data centers. In High Performance Computing &Simulation(HPCS),2014 International Conference on (pp. 902-910). IEEE.
- Arzuaga, E., & Kaeli, D. R. (2010, January). Quantifying load imbalance on virtualized enterprise servers. In Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering (pp.235-242). ACM.
- Kinger, S., & Goyal, K. (2013). Energy-efficient CPU utilization based virtual machine scheduling in Green clouds.
- Beloglazov, A., & Buyya, R. (2010, November). Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science (Vol. 4). ACM.
- Horri, A., Mozafari, M. S., & Dastghaibyfard, G. (2014). Novel resource allocation algorithms to performance and energy efficiency in cloud computing. The Journal of Supercomputing, 69(3), 1445-1461
- Cao, Z., & Dong, S. (2012, December). Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud Computing. In Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on (pp. 363-369). IEEE
- Fu, X., & Zhou, C. (2015). Virtual machine selection and placement for dynamic consolidation in Cloud computing environment. Frontiers of Computer Science, 9(2), 322-33
Index Terms
Keywords