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VMDFS: virtual machine dynamic frequency scaling framework in cloud computing

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Abstract

Development of modern techniques, such as virtualization, underlies new solutions to the problem of reducing energy consumption in cloud computing. However, for the infrastructure as a service providers, it would be a difficult process to guarantee energy saving. Analysis of the workload of applications shows that the average utilization of virtual machines has many fluctuations; therefore, deciding about how to control such fluctuations in virtual machines plays a significant role in improving the energy consumption of datacenters. In this study, an adaptable model called virtual machine dynamic frequency system (VMDFS) has been developed whose its innovation is monitoring the average fluctuations of workloads to vary the CPU frequency of virtual machines at runtime, dynamically. In this model, enhanced exponential moving average method is used to predict workload fluctuations, and then after calculating a smoothing coefficient for the utilization fluctuations, the coefficient is used to control the CPU frequency (or computing power) of virtual machines. The proposed model was compared with several base line approaches such as DVFS using real datasets from CoMon project (PlanetLab). The results of experiments on VMDFS show that besides the reduced service-level agreement violation by up to 43.22%, the overall energy consumption is reduced by 40.16%. In addition, the overall runtime before a host shutdown increased by 17.44% in average, while the runtime before a virtual machine migration increased by 7.2%. This also shows an overall decrease in the number of migrations.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Payame Noor University, Qeshm International Center branch, Qeshm, Iran

    Kiamars Shojaei

  2. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Faramarz Safi-Esfahani

  3. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Faramarz Safi-Esfahani

  4. Department of Computer Engineering and Information Technology, Payame Noor University, Najafabad Branch, Najafabad, Iran

    Saeed Ayat

Authors
  1. Kiamars Shojaei
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  2. Faramarz Safi-Esfahani
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  3. Saeed Ayat
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Corresponding author

Correspondence to Faramarz Safi-Esfahani.

Appendix: Case study

Appendix: Case study

In order to show how the proposed framework works, a practical case study is presented for the runtime environment described in Table 11. The assumptions in this example are as follows: there exist six hosts, ten virtual machines, and a 1-h period of workload from PlanetLab. Table 12 shows CPU utilization in these ten virtual machines during 5-min intervals in a 1-h period. In time interval 1, the maximum computing power (frequency) of a virtual machine is used for calculating the energy. In the consequent intervals, VM CPU utilizations are estimated using enhanced exponential moving average (EEMA) prediction model. These predictions affect the computing power of each virtual machine. Table 13 shows the enhanced exponential moving average (EEMA) prediction values for the time intervals shown in Table 12.

Table 11 Properties of VMs and hosts in the case study
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Table 12 Utilization of 10 virtual machines in 5-min intervals in a 1-h period
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Table 13 Utilization prediction for 10 virtual machines using enhanced exponential moving average (EEMA) method
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Power and energy calculations are performed in the first 5 mins (Time 300.1) based on the maximum VM utilization. Before entering the second simulation interval, under-loaded and overloaded hosts are determined. Virtual machines are then migrated from over/under-loaded candidate hosts to appropriate target hosts using the algorithms for selecting the best VM for migration. Consumed power and energy are calculated in the second interval (time: 600.1). These calculations consider the effect of predicted power consumption, as given by enhanced exponential moving average (EEMA). Table 14 shows the calculated parameters of VMDFS evaluation metrics in the first three intervals of the 1-h simulation period in CloudSim.

Table 14 VMDFS results and evaluation metrics
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Shojaei, K., Safi-Esfahani, F. & Ayat, S. VMDFS: virtual machine dynamic frequency scaling framework in cloud computing. J Supercomput 74, 5944–5979 (2018). https://doi.org/10.1007/s11227-018-2508-1

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  • DOI: https://doi.org/10.1007/s11227-018-2508-1

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