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Energy Optimization in Cloud Computing: A Review

Ashish Kumar Pandey1 , Shish Ahmad2

Section:Review Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 249-256, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.249256

Online published on Feb 28, 2019

Copyright © Ashish Kumar Pandey, Shish Ahmad . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Ashish Kumar Pandey, Shish Ahmad, “Energy Optimization in Cloud Computing: A Review,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.249-256, 2019.

MLA Style Citation: Ashish Kumar Pandey, Shish Ahmad "Energy Optimization in Cloud Computing: A Review." International Journal of Computer Sciences and Engineering 7.2 (2019): 249-256.

APA Style Citation: Ashish Kumar Pandey, Shish Ahmad, (2019). Energy Optimization in Cloud Computing: A Review. International Journal of Computer Sciences and Engineering, 7(2), 249-256.

BibTex Style Citation:
@article{Pandey_2019,
author = {Ashish Kumar Pandey, Shish Ahmad},
title = {Energy Optimization in Cloud Computing: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {249-256},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3650},
doi = {https://doi.org/10.26438/ijcse/v7i2.249256}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.249256}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3650
TI - Energy Optimization in Cloud Computing: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Ashish Kumar Pandey, Shish Ahmad
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 249-256
IS - 2
VL - 7
SN - 2347-2693
ER -

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Abstract

Nowadays, modern computing environment, having lots of challenges towards flexibility and processing capabilities. So Data Centers are required. Each data center provides physical wires by which enormous compute, network and storage resources are connected. Data centers are responsible for computation, space, network points and their effective and efficient operations. Therefore, enhancing the performance of the system likes total productivity, reliability and availability having the requirement to minimize the energy consumption of Data Centers. So, energy Consumption reductions are not only to enhance the system performance but also optimize the cost. Thus, an energy optimization is becoming a challenging task due to speedy growth in data and computing applications. In this paper we critically studied about the different energy based proposed methods and comprehensive survey along with a taxonomy of network topologies, either used in commercial data centers, or proposed by researchers

Key-Words / Index Term

Cloud Computing, Energy Consumption, Energy Saving, DCN Topology, Energy Architecture

References

[1] A. Uchechukwu, K. Li, Y. Shen, “Energy Consumption in Cloud Computing Data Centers”, International Journal of Cloud Computing and Services Science, Vol.3, No.3, pp. 31-48, 2014
[2] J. Koomey, “Estimating Total Power Consumption by Server in the U.S and the World”, Lawrence Berkeley National Laboratory, Stanford University, pp. 1-31, 2007.
[3] J. Toress, “Green Computing, The next wave in computing”, Jordi Torres, In Ed. UPC Technical University of Catalonia, Barcelona, 2010.
[4] P. Kogge, “The Tops in Flops”, IEEE Spectrum, pp. 49-54, 2011.
[5] M. Al-Fares, A. Loukissas, A. Vahdat, “A scalable, commodity data center network architecture,” ACM SIGCOMM Computer Communication Review, Vol. 38, No. 4, pp. 63–74, 2008.
[6] J. Ren, Y. Zhang, N. Zhang, D. Zhang, and X. Shen, “Dynamic channel access to improve energy efficiency in cognitive radio sensor networks,” IEEE Trans. Wireless Commun., Vol. 15, No. 5, pp. 3143–3156, 2016.
[7] A. Uchechukwu, K. Li, Y. Shen, “Improving Cloud Computing Energy Efficiency”, IEEE Asia Pacific Cloud Computing Congress, 2012.
[8] C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang, S. Lu, “Dcell a scalable and fault-tolerant network structure for data centers”, ACM Sigcomm Computer Communication Review, Vol. 38, no. 4, pp. 75– 86, 2008.
[9] C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu, “Bcube. a high performance, server-centric network architecture for modular data centers”, ACM Sigcomm Computer Communication Review, vol. 39, no. 4, pp. 63–74, 2009.
[10] H. Wu, G. Lu, D. Li, C. Guo, Y. Zhang, “Mdcube. a high performance network structure for modular data center interconnection”, In the Proceedings of the 2009 ACM 5th International Conference on Emerging Networking Experiments and Technologies, Rome, Italy ,pp. 25–36, 2009.
[11] S. K. Abda, S. A. R. Al-Haddadb, F. Hashimb, A. B. H. J. Abdullahc, and S. Yussof, ‘‘An effective approach for managing power consumption in cloud computing infrastructure’’, J. Comput. Sci., vol. 21, pp. 349–360, 2017.
[12] L. Guo , G. Hu, Y. Dong, Y. L. Luo, Y. Zhu, “A Game Based Consolidation Method of Virtual Machines in Cloud Data Centers With Energy and Load Constraints”, IEEE Access, pp. 4664-4676, 2018.
[13] J. Xu, H. Wu, L. Chen, C. Shen, “Online Geographical Load Balancing for Mobile Edge Computing with Energy Harvesting”, IEEE Transaction, pp. 1-30, 2017.
[14] S. Mazumdar, M. Pranzo, “Power efficient server consolidation for cloud data center”, Elsevier, pp. 4-16, 2017.
[15] S. K. Abd, S.A.R Al-Haddad, F. Hashim, A.B.H.J.A. azizol, S.Yusso, “An effective approach for managing power consumption in cloud computing infrastructure”, Journal of Computational Science, pp. 1-33, 2016.
[16] T. Kaur, I. Chana, “Energy Efficiency Techniques in Cloud Computing: A Survey and Taxonomy”, ACM Computing Surveys, Vol. 48, No. 2, pp. 22.1-22.46, 2015.
[17] D. Kliazovich, P. Bouvry, F. Granelli, N. L. S. da Fonseca, “Energy consumption optimization in cloud data centers” John Wiley & Sons, Luxembourg, pp. 193-215, 2015.
[18] Y. Ding, X. Qin, L. Liu, and T. Wang, ‘‘Energy efficient scheduling of virtual machines in cloud with deadline constraint’’, Future Generat. Comput. Syst., Vol. 50, pp. 62–74, 2015.
[19] A. Hammadi, L. Mhamdi , “A survey on architectures and energy efficiency in data center networks”, Elsevier, p.p. 1- 21, 2014.
[20] C. M. Wu, R. S. Chang, and H. Y. Chan, ‘‘A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters,’’ Future Generat. Comput. Syst., Vol. 37, pp. 141–147, 2014.
[21] X. Z. Member, L. T. Yang, H. Chen, J. Wang, S. Yin, X. Liu, “ Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds’’, IEEE Transaction, pp. 1-14, 2014.
[22] C. Ghribi, M. Hadji, D. Zeghlache, “Energy efficient vm scheduling for cloud data centers”, IEEE/ACM 13th International Symposium on Cluster, Cloud, and Grid Computing, p.p 671-678, 2013.
[23] W. Chawarut, L. Woraphon, ‘‘Energy-aware and real-time service management in cloud computing,’’ In the Proceedings of the 2013 IEEE 10th International Conference on Electrical Engineering or Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand, pp. 1–5, 2013.
[24] E. Volk, A. Tenschert, M. Gienger, “Improving energy efficiency in data centers and federated cloud environments. comparison of CoolEmAll and Eco2Clouds approaches and metrics”, In the Proceedings of the IEEE 3rd International Conference on Cloud and Green Computing, pp.443-450, 2013.
[25] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, A.Y. Zomaya, “Energy-Efficient Data Replication in Cloud Computing Datacenters”, IEEE Globecom workshop on Cloud Computing Systems, Networks and Applications, pp. 446-451, 2013.
[26] T. V. do, C. Rotter, “Comparision of scheduling schemes for on demand Iaas request”, Elsevier, Volume 85, Issue 6, 2012.
[27] A. Beloglazov, J. Abawajy, R. Buyya, ‘‘Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,’’ Future Generat. Comput. Syst., Vol. 28, No. 5, pp. 755–768, 2012.
[28] S. Chaisiri, B.S. Lee, D. Niyato, “Optimization of Resource Provisioning Cost in Cloud Computing”, IEEE transactions on services computing, Vol. 5, No. 2, p.p. 164-177, 2012.
[29] S. Mazumdar, M. Pranzo, ‘‘Power efficient server consolidation for cloud data center’’, Future Generat. Comput. Syst., Vol. 70, pp. 4–16, 2017.
[30] W. Zhu, Y. Zhuang, L. Zhang, ‘‘A three-dimensional virtual resource scheduling method for energy saving in cloud computing’’, Future Generat. Comput. Syst., Vol. 69, pp. 66–74, 2017.
[31] H. Duan, C. Chen, G. Min, Y. Wu, ‘‘Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems’’, Future Generat. Comput. Syst., Vol. 74, pp. 142–150, 2017.
[32] L. Zhou, ‘‘On data-driven delay estimation for media cloud’’, IEEE Trans. Multimedia, Vol. 18, No. 5, pp. 905–915, 2016.
[33] R. Li, Q. Zheng, X. Li, and J. Wu, ‘‘A novel multi-objective optimization scheme for rebalancing virtual machine placement’’, In the Proceedings of the IEEE 9th International Conference on Cloud Comput., San Francisco, CA, USA, pp. 710–717, 2016.
[34] M. Nir, A. Matrawy, “Economic and Energy Considerations for Resource Augmentation in Mobile Cloud Computing”, IEEE Transactions on Cloud Computing, p.p 1-14, 2015.
[35] J. M. H. Elmirghani, T. Klein, K. Hinton, L. Nonde, A. Q. Lawey, T. E. H. El Gorashi, M. O. I. Musa, and X. Dong, “GreenTouch GreenMeter Core Network Energy-Efficiency Improvement Measures and Optimization”, Optical Society of America, Vol. 10, p.p. A250- A269, 2018.
[36] P. Ehsan, P. Massoud, “Minimizing data center cooling and server power costs”, In the Proceedings of the ACM/IEEE 4th International Conference on on Low Power Electronic and Design (ISLPED), pp. 145-150, 2009.
[37] V. Liu, D. Halperin, A. Krishnamurthy, T. Anderson, “F10 A fault-tolerant engineered network”, In Presented as part of the USENIX 10th Symposium on Networked Systems Design and Implementation (NSDI 13), pp. 399–412, 2013.
[38] J. Moore, J. Chase, P. Ranganathan, R. Sharma, “Making Scheduling ‘Cool’ Temperature-Aware Workload Placement in Data Centers” In the Proceedings of the USENIX Annual Technical Conference, Anaheim, CA, USA, pp. 10–15, 2005.