A Review On Green Cloud Computing
DOI:
https://doi.org/10.24297/ijct.v15i4.6933Keywords:
Cloud Computing, Virtualization, Green Computing, DVS, VM, HostAbstract
With the increasing call for green cloud, reducing energy consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. Dynamic voltage scaling (DVS) has been a key technique in exploiting the hardware characteristics of cloud datacenters to save energy by lowering the supply voltage and operating frequency. Cloud Computing is being used widely all over the world by many IT companies as it provides various benefits to the users like cost saving and ease of use. However, with the growing demands of users for computing services, cloud providers are encouraged to deploy large datacenters which consume very high amount of energy and also contribute to the increase in carbon dioxide emission in the environment. Therefore, we require to develop techniques which will help to get more environment friendly computing i.e. Green Cloud Computing.
Downloads
Download data is not yet available.
References
[1] M. Rosenblum and T. Garfinkel, ―Virtual machine monitors: current technology and future trends,‖ Computer, vol. 38, no. 5, pp. 39–47, May 2005.
[2] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield, ―Live migration of virtual machines,‖ in Proceedings of the 2Nd Conference on Symposium on Networked Systems Design & Implementation, ser. NSDI’05, vol. 2, 2005, pp. 273–286.
[3] A. Beloglazov and R. Buyya, ―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, vol. 24, no. 13, pp. 1397– 1420, 2012.
[4] A. Murtazaev and S. Oh, ―Sercon: Server consolidation algorithm using live migration of virtual machines for green Computing,‖ IETE Technical Review, vol. 28, no. 3, pp. 212–231, 2011.
[5] F. Farahnakian, P. Liljeberg, and J. Plosila, ―LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers,‖ in Software Engineering and Advanced Applications (SEAA), 2013 39th EUROMICRO Conference on, 2013, pp. 357–364.
[6] T. Cover and P. Hart, ―Nearest neighbor pattern classification,‖ Information Theory, IEEE Transactions on, vol. 13, no. 1, pp. 21–27, 1967.
[7] N. Bobroff, A. Kochut, and K. Beaty, ―Dynamic placement of virtual machines for managing sla violations,‖ in Integrated Network Management, 2007. IM ’07. 10th IFIP/IEEE International Symposium on, May 2007, pp. 119–128.
[8] F. Farahnakian, A. Ashraf, T. Pahikkala, P. Liljeberg, J. Plosila, I. Porres, and H. Tenhunen, ―Using ant colony system to consolidate vms for green cloud computing,‖ Services Computing, IEEE Transactions on, vol. 8, no. 2, pp. 187–198, March 2015.
[9] A. Verma, P. Ahuja, and A. Neogi, ―Pmapper: Power and migration cost aware application placement in virtualized systems,‖ in 9th ACM/IFIP/USENIX International Conference on Middleware, 2008, pp.243–264.
[10] T. H. Nguyen, M. D. Francesco, and A. Yl¨a − J¨a¨aski, ―A virtual machine placement algorithm for balanced resource utilization in cloud data centers,‖ The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD), p. 474481, 2014.
[11] B. Li, J. Li, J. Huai, T. Wo, Q. Li, and L. Zhong, ―Enacloud: An energy-saving application live placement approach for cloud computing environments,‖ 2013 IEEE Sixth International Conference on Cloud Computing, vol. 0, pp. 17–24, 2009.
[12] F. Farahnakian, T. Pahikkala, P. Liljeberg, and J. Plosila, ―Energy aware consolidation algorithm based on k-nearest neighbor regression for cloud data centers,‖ in Utility and Cloud Computing (UCC), 2013 IEEE/ACM 6th International Conference on, Dec 2013, pp. 256–259.
[13] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, ―CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,‖ Software: Practice and Experience, vol. 41, no. 1, pp. 23–50, 2011.
[14] K. Park and V. Pai, ―CoMon: a mostly-scalable monitoring system for PlanetLab,‖ ACM SIGOPS Operating Systems Review, vol. 40, pp. 65– 74, 2006.
[15] D. Kusic, J. Kephart, J. Hanson, N. Kandasamy, and G. Jiang, ―Power and performance management of virtualized computing environments via lookahead control,‖ in Autonomic Computing, 2008. ICAC ’08. International Conference on, June 2008, pp. 3–12.
[2] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield, ―Live migration of virtual machines,‖ in Proceedings of the 2Nd Conference on Symposium on Networked Systems Design & Implementation, ser. NSDI’05, vol. 2, 2005, pp. 273–286.
[3] A. Beloglazov and R. Buyya, ―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, vol. 24, no. 13, pp. 1397– 1420, 2012.
[4] A. Murtazaev and S. Oh, ―Sercon: Server consolidation algorithm using live migration of virtual machines for green Computing,‖ IETE Technical Review, vol. 28, no. 3, pp. 212–231, 2011.
[5] F. Farahnakian, P. Liljeberg, and J. Plosila, ―LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers,‖ in Software Engineering and Advanced Applications (SEAA), 2013 39th EUROMICRO Conference on, 2013, pp. 357–364.
[6] T. Cover and P. Hart, ―Nearest neighbor pattern classification,‖ Information Theory, IEEE Transactions on, vol. 13, no. 1, pp. 21–27, 1967.
[7] N. Bobroff, A. Kochut, and K. Beaty, ―Dynamic placement of virtual machines for managing sla violations,‖ in Integrated Network Management, 2007. IM ’07. 10th IFIP/IEEE International Symposium on, May 2007, pp. 119–128.
[8] F. Farahnakian, A. Ashraf, T. Pahikkala, P. Liljeberg, J. Plosila, I. Porres, and H. Tenhunen, ―Using ant colony system to consolidate vms for green cloud computing,‖ Services Computing, IEEE Transactions on, vol. 8, no. 2, pp. 187–198, March 2015.
[9] A. Verma, P. Ahuja, and A. Neogi, ―Pmapper: Power and migration cost aware application placement in virtualized systems,‖ in 9th ACM/IFIP/USENIX International Conference on Middleware, 2008, pp.243–264.
[10] T. H. Nguyen, M. D. Francesco, and A. Yl¨a − J¨a¨aski, ―A virtual machine placement algorithm for balanced resource utilization in cloud data centers,‖ The 7th IEEE International Conference on Cloud Computing (IEEE CLOUD), p. 474481, 2014.
[11] B. Li, J. Li, J. Huai, T. Wo, Q. Li, and L. Zhong, ―Enacloud: An energy-saving application live placement approach for cloud computing environments,‖ 2013 IEEE Sixth International Conference on Cloud Computing, vol. 0, pp. 17–24, 2009.
[12] F. Farahnakian, T. Pahikkala, P. Liljeberg, and J. Plosila, ―Energy aware consolidation algorithm based on k-nearest neighbor regression for cloud data centers,‖ in Utility and Cloud Computing (UCC), 2013 IEEE/ACM 6th International Conference on, Dec 2013, pp. 256–259.
[13] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, ―CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,‖ Software: Practice and Experience, vol. 41, no. 1, pp. 23–50, 2011.
[14] K. Park and V. Pai, ―CoMon: a mostly-scalable monitoring system for PlanetLab,‖ ACM SIGOPS Operating Systems Review, vol. 40, pp. 65– 74, 2006.
[15] D. Kusic, J. Kephart, J. Hanson, N. Kandasamy, and G. Jiang, ―Power and performance management of virtualized computing environments via lookahead control,‖ in Autonomic Computing, 2008. ICAC ’08. International Conference on, June 2008, pp. 3–12.
Downloads
Published
2016-02-23
How to Cite
Kaur, P., & Sachdeva, M. (2016). A Review On Green Cloud Computing. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(4), 6686–6691. https://doi.org/10.24297/ijct.v15i4.6933
Issue
Section
Research Articles
