Abstract
Power management has turned into a basic plan parameter as more transistors are coordinated on a solitary chip. Bringing down the supply voltage is one of the alluring ways to deal with spare intensity of the variable outstanding task at hand framework, and to accomplish long battery life. DVFS is one of the effective methods to lessen the vitality utilization. The principle thought behind DVFS plan is to powerfully scale the supply voltage of CPU, to give enough circuit speed to process the framework remaining burden so as to meet the time and execution, along these lines lessening power. The problem faced by users in cloud computing is energy consumption. Some resources in cloud stay idle for some time. This idleness of resources consume energy thereby causing more consumption of energy. Both the hardware and software reduces the energy consumptions. The results shows that proposed model reduces the consumption of energy by using DVFS.
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
Huai, W., Huang, W., Jin, S., Qian, Z.: Towards energy efficient scheduling for online tasks in cloud data centers based on DVFS. In: 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 225–232. IEEE (2015)
Ruan, X., Qin, X., Zong, Z., Bellam, K., Nijim, M.: An energy-efficient scheduling algorithm using dynamic voltage scaling for parallel applications on clusters. In: Proceedings of 16th International Conference on Computer Communications and Networks, ICCCN 2007, pp. 735–740. IEEE (2007)
Lai, Z., Lam, K.T., Wang, C.-L., Su, J., Yan, Y., Zhu, W.: Latency-aware dynamic voltage and frequency scaling on many-core architectures for data-intensive applications. In: 2013 International Conference on Cloud Computing and Big Data (CloudCom-Asia), pp. 78–83. IEEE (2013)
Huai, W., Qian, Z., Li, X., Lu, S.: Towards energy efficient data centers: a DVFS-based request scheduling perspective. In: 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 299–306. IEEE (2013)
Iyapparaja, M., et al.: Coupling and cohesion metrics in Java for adaptive reusability risk reduction. In: IET Chennai 3rd International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2012), pp. 52–57 (2012)
Li, K.: Energy efficient scheduling of parallel tasks on multiprocessor computers. J. Supercomput. 60(2), 223–247 (2012)
Nezih, Y., Datta, K., Jain, N., Willke, T.: Energy efficient scheduling of MapReduce workloads on heterogeneous clusters. In: Green Computing Middleware on Proceedings of the 2nd International Workshop, p. 1. ACM (2011)
Iyapparaja, M., Sharma, B.: Augmenting SCA project management and automation Framework. IOP Conf. Ser. Mater. Sci. Eng. 263, 1–8 (2017). https://doi.org/10.1088/1757-899x/263/4/042018. 042018
Mei, J., Li, K., Li, K.: Energy-aware task scheduling in heterogeneous computing environments. Cluster Comput. 17(2), 537–550 (2014)
Iyapparaja, M., Tiwari, M.: Security policy speculation of user uploaded images on content sharing sites. IOP Conf. Ser. Mater. Sci. Eng. 263, 1–8 (2017). https://doi.org/10.1088/1757-899x/263/4/042019. 042018
Wang, X., Wang, Y., Cui, Y.: An energy-aware bi-level optimization model for multi-job scheduling problems under cloud computing. Soft Comput. (2014)
Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)
Chen, Y., Alspaugh, S., Borthakur, D., Katz, R.: Energy efficiency for large-scale MapReduce workloads with significant interactive analysis. In: Proceedings of the 7th ACM European Conference on Computer Systems, pp. 43–56. ACM (2012)
Wen, G., Hong, J., Xu, C., Balaji, P., Feng, S., Jiang, P.: Energy-aware hierarchical scheduling of applications in large scale data centers. In: 2011 International Conference on Cloud and Service Computing (CSC), pp. 158–165. IEEE (2011)
Feng, B., Lu, J., Zhou, Y., Yang, N.: Energy efficiency for MapReduce workloads: an in-depth study. In: Proceedings of the Twenty-Third Australasian Database Conference-Volume 124, pp. 61–70. Australian Computer Society, Inc. (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Iyapparaja, M., Abirami, L., Sathish Kumar, M. (2020). Improving Energy Consumption by Using DVFS. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-38501-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38500-2
Online ISBN: 978-3-030-38501-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)