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GMM-LSTM: a component driven resource utilization prediction model leveraging LSTM and gaussian mixture model

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Abstract

Nowadays, Cloud services are gaining importance among users due to their cost-effectiveness and highly scalable resources. To meet the user’s demands, several data centres are built across the globe, which has severe environmental as well as economical concerns. Energy consumption is one of the most significant issue faced by cloud service providers. Prediction of accurate resource usage of the physical machine helps in effective utilization of resources in a data centre, resulting in minimizing an active number of physical machines, which helps to minimize the energy consumption of a data centre. Although several models till date focus on virtual machine consolidation with a notion of reducing energy consumption, the reduction of operational physical machines has not gathered enough attention. In this paper, we propose a prediction model to predict resource utilization of physical machines, which enables to effectively utilize the entire data centre’s resources to reduce energy consumption. First, the raw time series workload is processed to enhance the value of its features for better training and prediction of mean resource utilization in the cloud data centre using the proposed Sum Average (SA) algorithm. Afterward, Gaussian Mixture Model (GMM) is employed to cluster heterogeneous machines of data centre based on its resource usage which helps to analyze the prediction for each kind of configured machine available in a data centre. In addition, the Long Short Term Memory model (LSTM) is employed to predict the mean resource usage of physical machines for every clustered machine. Furthermore, the effectiveness of our proposed model is evaluated using the Google cluster trace usage dataset. Lastly, the proposed model is compared with Linear Regression, Moving Average, and Auto Regression Integrated Moving Average model. Root Mean Square Error (RMSE) analysis states that our proposed model outperforms the other compared techniques

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Publicly available dataset is used for this research.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India

    Sheetal Garg & Rohit Ahuja

  2. School of Computing, Engineering and Physical Sciences, University of the West of Scotland, United Kingdom, Hamilton, G72 0LH, Scotland, UK

    Raman Singh

  3. Faculty of Software Engineering and Computer Systems, ITMO University, Saint Petersburg, 197101, Russia

    Ivan Perl

Authors
  1. Sheetal Garg
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  2. Rohit Ahuja
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  3. Raman Singh
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  4. Ivan Perl
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Contributions

SG: Conceptualization, Implementation, Software, Validation, Writing - Original Draft. RA: Resources, Writing - Original Draft, Supervision.RS: Data Curation, Visualization, Resources, Supervision. IP: Supervision, Resources.

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Correspondence to Sheetal Garg.

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Garg, S., Ahuja, R., Singh, R. et al. GMM-LSTM: a component driven resource utilization prediction model leveraging LSTM and gaussian mixture model. Cluster Comput 26, 3547–3563 (2023). https://doi.org/10.1007/s10586-022-03747-4

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  • DOI: https://doi.org/10.1007/s10586-022-03747-4

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