Abstract
Cloud Data Center (CDC) is growing in popularity as academic and industry hot research spot. With the rapid growth of data centers, thousands of large data centers with lots of computing nodes are established. Accordingly, the energy consumption of the CDC is very high. Also, many of the current research studies have not considered server power state transition and its effect to performance and power consumption. In this paper, we build the resource scaling scheme for energy efficiency in CDCs with considered sleep-mode. And then from evaluation result, we proves that our proposed method is able to efficiently manage the resource and reduce energy consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beloglazov, A., et al.: A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv. Comput. 82(2), 47–111 (2011)
Piraghaj, S.F., et al.: A survey and taxonomy of energy efficient resource management techniques in platform as a service cloud. In: Handbook of Research on End-to-end Cloud Computing Architecture Design, pp. 410–454 (2017)
Zhang, B., Sabhanatarajan, K., Gordon-Ross, A., George, A.: Real-time performance analysis of adaptive link rate. In: 33rd IEEE Conference on Local Computer Networks, LCN 2008, pp. 282–288. IEEE (2008)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Wu, C.-M., Chang, R.-S., Chan, Hsin-Yu.: A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Gener. Comput. Syst. 37, 141–147 (2014)
Gunaratne, C., et al.: Reducing the energy consumption of Ethernet with adaptive link rate (ALR). IEEE Trans. Comput. 57(4), 448–461 (2008)
Wu, G., et al.: Energy-efficient virtual machine placement in data centers by genetic algorithm. In: Neural Information Processing. Springer, Heidelberg (2012)
Maurya, K., Sinha, R.: Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center. Int. J. Comput. Sci. Mobile Comput. 2(3), 74–82 (2013)
Graubner, P., Schmidt, M., Freisleben, B.: Energy-efficient virtual machine consolidation. IT Prof. 15(2), 28–34 (2013)
Galloway, J.M., Smith, K.L., Vrbsky, S.S.: Power aware load balancing for cloud computing. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1 (2011)
Farooqi, A.M., Tabrez Nafis, M., Usvub, K.: Comparative analysis of green cloud computing. Int. J. 8(2), 56–60 (2017)
Acknowledgement
This work was supported by the Energy Efficiency Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20152020106310) and MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2013-0-00717) supervised by the IITP (Institute for Information & communications Technology Promotion).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Son, AY., Huh, EN. (2018). A Study on Resource Scaling Scheme for Energy Efficiency in Cloud Datacenter. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_170
Download citation
DOI: https://doi.org/10.1007/978-981-10-7605-3_170
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7604-6
Online ISBN: 978-981-10-7605-3
eBook Packages: EngineeringEngineering (R0)