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Performance Evaluation of Energy Efficient Power Models for Digital Cloud

Affiliations

  • K L University, Green Fields, Vaddeswaram, Guntur - 522502, Andhra Pradesh, India

Abstract


Background: To improve quality of service energy-efficiency is one of the key parameters of Cloud service providers. Every year huge amounts of electrical energy consume by Cloud data center which leads to more expense in costs and emission of CO2 to the environment which is unhealthy for us. In this case the need of Green Cloud computing solutions to minimize emission of carbon footprints as well as operational costs is the utmost desire. Objectives: In our research work we have implemented four different power models such as linear model, cubic model, square model and square root model on an Infrastructure-as-a-Service (IaaS) Cloud environment to find out the best one. Methods: Here we considered the CPU utilization and power consumption by enabling virtual machine migration. Then to validate the accuracy of these power models R-squared, Mean Square Error (MSE) have been performed. Finding: We found out that the cubic polynomial model is the most efficient one and consume less power in comparison to the other three models. Application: Hence this model can be used in energy saving applications over Cloud data centers.

Keywords

Cloud Computing, CPU Utilization, Energy Efficiency, Energy-Aware Cloud Computing, Power Models.

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References


  • Natural Resources Defense Council (NRDC). Data Center Efficiency Assessment-Scaling up Energy Efficiency across the Data Center Industry: Evaluating Key Drives and Barriers.2014 August; p. 1-35.
  • Beloglazov A and Buyya R. Energy Efficient Resource Management in Virtualized Cloud Data Centers. IEEE, CLOUDS Laboratoty, The University of Melbourne, Australia, 2010; p. 1-6.
  • Beloglazov A, Abawajy J and Buyya R. Energy-aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing. Elsevier: Future Generation Computer Systems. 2012; 28:755–68.
  • Beloglazov A and Buyya R. John Wiley & Sons, Ltd.: Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers.2012; p. 1397-420.
  • Beloglazov A. Department of Computing and Information Systems, The University of Melbourne: Energy-efficient Management of Virtual Machines in Data Centers for Cloud Computing. Doctor of Philosophy (PhD Thesis).2013 February.
  • Kumar D and Sahoo B. Energy Efficient Heuristics Resource Allocation for Cloud Computing. International Journal of Artificial Intelligent Systems and Machine Learning. 2014 Jan; 6(1):32-8.
  • Rodero I, Jaramillo J, Quiroz A, Parashar M, Guim F and Poole S. Energy-efficient application-aware online provisioning for virtualized clouds and data centers. IEEE, International Green Computing Conference, 2010. 2010; p.31-45.
  • Hoon Kim K, Beloglazov A and Buyya R. Power-Aware Provisioning of Cloud Resources for Real-time Services, ACM. 2009; p. 1-6.
  • Minu Bala and Devanand. Performance Evaluation of Cloud Datacenters using Various Green Computing Tactics. New Delhi: IEEE, Proceedings of the 9th INDIACom-2015. 2015 March; p. 371-76.
  • Bo Li, Jianxin Li, Jinpeng Huai, Tianyu Wo, Qin Li, Liang Zhong. EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments.IEEE, International Conference on Cloud Computing. 2009 Sept 21-25; p. 17-24.
  • Prakash P, Kousalya G, Shriram K Vasudevan and Kawshik K Rangaraju. Distributive Power Migrtion and Management Algorithm for Cloud Environment. Journal of Computer Science. 2014; 10(3):484-91. ISSN: 1549-3636.
  • Qi Zhang, Mohamed Faten Zhani, Shuo Zhang, Quanyan Zhu, Raouf Boutaba and Joseph L Hellerstein. Dynamic Energy-Aware Capacity Provisioning for Cloud Computing Environment, ICAC’ 12, ACM. 2012 September; p. 145-54.
  • Kliazovich D, Bouvry P and Ullah Khan S. DENS: Data Center Energy-Efficient Network-Aware Scheduling.Springer: Cluster Compute. 2011 Sep; p. 1-11.
  • Kliazovich D, Bouvry P and Ullah Khan S. GreenCloud: A Packet-Level Simulator of Energy-Aware Cloud Computing Data Centers. Springer Science and Business Media.2010; p. 1-21.
  • Uchechukwu A, Keqiu Li and Yanming Shen. Sustainable Cost and Energy Consumption Analysis for Cloud Data Centers. Higher Education Press and Springer-Verlag Berlin Heidelberg, Research Article. 2014; p. 1-13.
  • Uchechukwu A, Keqiu Li and Yanming Shen. Improving Cloud Computing Energy Efficiency. Proceedings of the IEEE Asia Pacific Cloud Computing Congress. 2012; p. 538.
  • Uchechukwu A, Keqiu Li and Yanming Shen. Energy Consumption in Cloud Computing Data Centers. International Journal of Cloud Computing and Services Science (IJ-CLOSER). 2012 June; 3(3):145-62.
  • Amudhavel J, Vigneshwaran R, Janakiram A, Jarina S, Prem Kumar K, Anantharaj B, Sathian D. An Empirical Analysis on Quality of Service (QoS) in Cloud Computing. Indian Journal of Science and Technology. 2016 June; 9(22).DOI:10.17485/ijst/2016/v9i22/95181.
  • Standard Performance Evaluation Corporation. http:// www.spec.org.
  • Thaigo Teixeira Sa, Rodrigo N Calheiros and Danielo G Gomes. CloudReports: An Extensible Simulation Tool for Energy-Aware Cloud Computing Environments. Springer International Publishing: Chapter 6, Cloud ComputingChallenges, Limitations and R & D Solutions. 2014 21st Oct (Book); p. 127-42,. ISBN: 978-3-319-10529-1.

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