APPLICATION OF MACHINE LEARNING TECHNIQUES IN DATA CENTRE ENERGY MANAGEMENT

Authors

  • R. Ranjana
  • N. Susila
  • T. Subha

Keywords:

data centres, cloud servers, machine learning, virtualization.

Abstract

Cloud computing is one of the leading computing paradigms that offers services like Infrastructure as a Service called IaaS, Platform as a Service called PaaS, Software as a Service called SaaS to users on a pay per use model. The massive data centers that help cloud offer all the above stated services are virtualized. Virtualization enables easy management of resources. However, the massive physical servers in the data centers tend to consume enormous energy, leading to high environmental impact. So, energy conservation with optimum usage and management is one of the prominent areas of research in cloud. The major techniques to manage energy is to identify unused physical resources and put them to low power state or sleep state. But, the usage of resources depends heavily on the user requirements in an elastic environment like cloud. Hence machine learning techniques can be used to predict the usage patterns thereby identifying the physical resources required to fulfill the user demand. This paper aims to survey the avenues wherein machine learning can be applied to help energy management in a cloud data center.

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References

. Habibullah K.M., Rondeau E., Georges JP. (2018) Reducing Energy Consumption of Network Infrastructure Using Spectral Approach. In: Dastbaz M., Arabnia H., Akhgar B. (eds) Technology for Smart Futures. Springer, Cham.

. Jun Liu, Optimizing the Energy Consumption of Servers and Networks in Cloud Data Centers. (unpublished).

. Abbasi, et al., 2007; Amis, et al., 2007; Baker, 1981; Bandyopadhyay, 2003; Basagni,

; Lin, 1997;

Mellier, 2006; Chiasserini, et al., 2002.

. Gao, “Machine Learning Applications for Data Center Optimization”, Andrew Ng. “Neural Networks: Representation (Week 4).” Machine Learning. Retrieved from https://class.coursera.org/ml2012002. 2012. Lecture.

. Z. Songa, X. Zhangb, C. Erikssona, Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Data Center Energy and Cost Saving Evaluation”. In: The 7th International Conference on Applied Energy – ICAE2015.

. J. Ni and X. Bai, “A review of air conditioning energy performance in data centers,” Renewable and sustainable energy reviews, vol. 67.

. Yuanlong Li, Yonggang Wen, Kyle Guan, and DachengTao, “Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning”.

. MiyuruDayarathna, Yonggang Wen, Senior Member, IEEE, and Rui Fan, “Data Center Energy Consumption Modeling: A Survey”.

. N. Bobroff, A. Kochut, and K. Beaty, “Dynamic placement of virtual machines for managing SLA violations”, Proceedings of the 10th IFIP/IEEE Intl.Symp. on Integrated Network Management (IM), pp.119–128, 2007.

. M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, “Energy-efficient cloud resource management,” in Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on. IEEE, 2014, pp. 386–391.

. FahimehFarahnakian, PasiLiljeberg, and JuhaPlosila, “LiRCUP: Linear Regression based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers”, In: 2013 39th Euromicro Conference Series on Software Engineering and Advanced Applications

. J. J. Prevost, K. Nagothu, B. Kelley, and M. Jamshidi, “Prediction of cloud data center networks loads using stochastic and neural models,” in System of Systems Engineering (SoSE), 2011 6th International Conference on. IEEE, 2011, pp. 276–281.

. MichałAibin, Krzysztof Walkowiak, Soroush Haeri and LjiljanaTrajkovic, “Traffic Prediction for Inter-Data CenterCross-Stratum Optimization Problems”. In: 2018 International Conference on Computing,

Networking and Communications (ICNC): Optical and Grid Computing [14]. Cazenave, “Nested Monte-Carlo search,” in: IJCAI InternationalJointConference on Artificial Intelligence2009

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Published

2021-06-30

How to Cite

Ranjana, R. ., Susila, N. ., & Subha, T. . (2021). APPLICATION OF MACHINE LEARNING TECHNIQUES IN DATA CENTRE ENERGY MANAGEMENT. The Journal of Contemporary Issues in Business and Government, 27(3), 1948–1956. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/1801

Issue

Vol. 27 No. 3 (2021): The journal of contemporary issues in business and government

Section

Articles

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