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Power Modeling for Energy-Efficient Resource Management in a Cloud Data Center

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Abstract

An accurate host power model is necessary for effective power management in data centers which is crucial for reducing energy consumption and cost. One should evaluate the power models for different workloads and host configurations. We have analyzed several existing power models by varying the workload type (CPU, memory, and disk-intensive) and host configurations. By analyzing the system performance and nature of the power consumption of the hosts, we have identified some performance counter parameters that determine the system power consumption. We have proposed three power models based on multi-variable Linear Regression, Support Vector Regression (SVR), and Artificial Neural Network (ANN). Experimental results show that compared to the existing models, our proposed power models, especially those based on SVR and ANN, more accurately predict the power consumption of the hosts. We have also conducted simulation experiments to show the importance of the power model in the energy-efficient resource management of the hosts in the data center. Results show that the use of our SVR-based and ANN-based power models in a resource management approach can effectively decrease the energy consumption of the data center. Moreover, we have proposed an energy-efficient virtual machine (VM) placement and consolidation algorithm that further reduces energy consumption. At first, we formulated a model using integer linear programming. Then, we designed a heuristic based on Vogel’s Approximation Method. Extensive simulation on the CloudSim platform with benchmark workload data and the Google Cloud trace logs shows that our approach outperforms the state-of-the-art algorithms under comparison in terms of energy efficiency and quality of service (QoS). The results also highlight the importance of a suitable VM placement and consolidation approach and an accurate power model in reducing energy consumption.

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Data Availability

The Google Cloud traces data analyzed during the current study has been obtained from “Borg cluster traces from Google” (https://github.com/google/cluster-data). The benchmarks used in the current study have been obtained from “Systench” (https://github.com/akopytov/sysbench), “Lookbusy” (http://www.devin.com/lookbusy/), “Ramspeed” (https://github.com/cruvolo/ramspeed-smp ), and “IOzone benchmark” (https://openbenchmarking.org/test/pts/iozone-1.9.5).

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Acknowledgements

We would like to acknowledge the UGC-NET Junior Research Fellowship (UGC-Ref. No.: 3610/(NET-NOV 2017)) provided by the University Grants Commission, Government of India, and the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India (Ref. No. MLA/MUM/GA/10(37)C) for supporting this research work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

    Anurina Tarafdar, Rajib K Das & Sunirmal Khatua

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Soumi Sarkar

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  1. Anurina Tarafdar
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Correspondence to Sunirmal Khatua.

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Tarafdar, A., Sarkar, S., Das, R.K. et al. Power Modeling for Energy-Efficient Resource Management in a Cloud Data Center. J Grid Computing 21, 10 (2023). https://doi.org/10.1007/s10723-023-09642-5

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