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Progress in Machines and Systems

Efficient Policies of Data Management in Cloud to Reduce Energy Consumption of Data Center

Djouhra Dad, Ghalem Belalem
Oran1 University Ahmed Ben Bella Algeria

Abstract: Cloud computing has become over the last years an important paradigm in the computing landscape. Its principle is to provide decentralized services and allows client to consume resources on a pay-as you-go model. The growing demand for this type of service brings providers service clouds to increase the size of their infrastructure to the point that the energy consumption and associated costs become very important. This cloud infrastructure comprise several compoments such as servers, cooling systems, etc. In the server of data center , CPU and RAM are the compoments which consumes an important quantity of energy. In this paper, we present three policies (FDT, DDT based on CPU utilization thresholds and DRT based on CPU utilization and RAM capacity) each one with two phases. The selection phase which is based on physical resources thresholds (one or two physical resources) and the allocation phase to place the migrated VM on hosts. We simulate the proposed approaches on CloudSim simulator and compare them with existed approaches. Our proposal allows reducing energy consumption, number of migrations, the number of SLA violations and thus minimizing the CO2 emission.

Keywords: Data Center, Cloud Computing, Energy Consumption, Virtualization, Migration, Service Level Agreement Efficient Policies of Data Management in Cloud to Reduce Energy Consumption of Data Center

DOI:https://doi.org/10.6025/pms/2020/9/1/18-28

Full_Text PDF 1.09 MB Download: 369 times

References:

[1] Beloglazov, A., Abawajy, J., Buyya, R. (2011a). Energy-Aware Resource Allocation Heuristics for Efficient Management ofData Centers for Cloud Computing. The International Journal of Grid Computing and eScience. Future Generation Computer Systems (FGCS), 28 (5), Elsevier Science, Amsterdam, The Netherlands, 755-768.
[2] Beloglazov, A., uyya, R., Lee, Y.C., Zomaya, A. (2011b). A taxonomy and survey of energyefficient data centers and cloud computing systems’, Advances in Computers, Marvin V. Zelkowitz, VSolume 82, p 47–111.
[3] Brown, R. Report to congress on server and data center energy efficiency. (2007). University of California. Public law 109- 431.
[4] Buchbinder, N., Jain, N., Menache, I. (2011). Online Job-Migration for Reducing the Electricity Bill in the Cloud, LNCS 6640, p 172–185.
[5] Dad, D., Yagoubi, D.E., Belalem, G. (2014). Energy efficient vm live migration and allocation at cloud data centers. International Journal of Cloud Applications and Computing, 4, 55–63.
[6] Dad, J., Belalem, G. (2014) .Energy Optimisation in cloud computing. International Journal of Information Technology, Communications and Convergence, 3 (1) 1-12.
[7] Glanz, J. (2012). The cloud factories: Power, pollution and the internet. New York Times.
[8] http://www.nytimes.com/2012/09/23/technology/data-centers-waste-vast-amounts-of-energybelying-industry-image.html consulted 28/01/2014.
[9] Koomey, J. G. (2008) Worldwide electricity used in data centers. Environmental Research Letters, 3(3) 034008 (8p).
[10] Lee, Y.C., Zomaya, A.Y. (2010). Energy Efficient Utilization of Resources in Cloud Computing System Center for Distributed and High Performance Computing. School of Information. Technologies. University of Sydney, Sydney, Australia, Springer.
[11] Mazzucco, M., Dyachuk, D. (2012). Balancing electricity bill and performance in server farms with setup costs, Future Generation Computer Systems, 28 . 415–426.
[12] Sasikada, P. (2013). Energy Efficiency in Cloud Computing: Way Towards Green Computing. International Journal of Cloud Computing (IJCC), 2 (4) 305 - 324.
[13] Sekhar, J., Jeba, G. (2013). Energy Efficient VM Live Migration in Cloud Data Centers, IJCSN International Journal of Computer Science and Network, 2(2), 71-75.
[14] Sinha, R., Purohit, N., Diwanji, H. (2011). Power aware live migration for data centers in cloud using dynamic threshold. International Journal of Computer Technology and Applications, 2(6) 2041-2046.
[15] Sinha, R., Purohit, N. (2011). Energy efficient dynamic integration of thresholds for migration at cloud data centers. Special Issue of International Journal of Computer Applications on Communication and Networks, (11).
[16] Uchechukwu, A., Li, K., Shen,Y. (2014). Energy Consumption in Cloud Computing Data Centers. International Journal of Cloud Computing and Services Science. 3 (3), ISSN: 2089-3337.

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