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Intelligent Computing pp 562–580Cite as

Predicting Resource Usage in Edge Computing Infrastructures with CNN and a Hybrid Bayesian Particle Swarm Hyper-parameter Optimization Model

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 284))

Abstract

As the computational needs of edge infrastructures increased, efficient resource management becomes a necessity. An accurate prediction of future resource usage provides insight into dynamic task offloading, proactive auto-scaling, virtual machine migration, and workload balancing. In this paper we propose the use of multi-output one-dimensional convolutional neural networks as resource usage predictors. Convolutional neural networks can manipulate resource usage observations as time series data with the advantage of an adaptive window size selection. In addition, we propose an innovative hybrid hyper-parameter optimization method that combines particle swarm optimization and Bayesian optimization in order to conclude to a close to optimal convolutional neural network architecture. To validate the efficiency of our approach, we conducted experiments with an edge computing infrastructure. The evaluation results show that the proposed regression model achieves higher accuracy as compared to other machine learning meta-predictors and state of the are resource usage models.

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Acknowledgments

This work is part of the ACCORDION project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871793.

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Authors and Affiliations

  1. National Technical University of Athens, 9, Heroon Polytechniou Str., 15773, Zografou, Greece

    John Violos, Tita Pagoulatou, Stylianos Tsanakas & Theodora Varvarigou

  2. Harokopio University of Athens, 9, Omirou Str., 17778, Tavros, Greece

    Konstantinos Tserpes

Authors
  1. John Violos
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  2. Tita Pagoulatou
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  3. Stylianos Tsanakas
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  4. Konstantinos Tserpes
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  5. Theodora Varvarigou
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Corresponding author

Correspondence to John Violos .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Violos, J., Pagoulatou, T., Tsanakas, S., Tserpes, K., Varvarigou, T. (2021). Predicting Resource Usage in Edge Computing Infrastructures with CNN and a Hybrid Bayesian Particle Swarm Hyper-parameter Optimization Model. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_40

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