Abstract
In this paper a polynomial radial basis function neural network is trained to model and predict the temperature profile-energy proxy of a highly complex data center located at the University of the Aegean, Greece. A number of input variables are identified that directly quantify the rack’s air temperature. The corresponding data set is generated through an experimental monitoring system used over a two-week period. The network’s structure encompasses three distinct levels. The first level involves a number of hidden nodes with Gaussian activation functions, while the second level generates first order polynomial functions of the input variables. Finally, the third level aggregates the outputs of the above two levels and generates the network’s output. The network’s training process is based on using the particle swarm optimization algorithm. For comparative reasons, a typical radial basis function and a feed-forward network were developed. The results indicate that the proposed network is very effective in predicting the server rack’s air temperature, outperforming the other two networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ham, S.-W., Kim, M.-H., Choi, B.-N., Jeong, J.-W.: Simplified server model to simulate data center cooling energy consumption. Energy Build. 86, 328–339 (2015)
Ibrahim, H., Aburukba, R.O., El-Faki, K.: An integer linear programming model and adaptive genetic algorithm approach to minimize energy consumption of cloud computing data centers. Comput. Electr. Eng. (2018). doi:https://doi.org/10.1016/j.compeleceng.2018.02.028
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut. Gener. Comput. Syst. 28(5), 755–768 (2012)
Prevost, J.J., Nagothu, K., Kelley, B., Jamshidi, M.: Prediction of cloud data center networks loads using stochastic and neural models. In: the Proceedings of the 6th International Conference on System of Systems Engineering, pp. 276–281 (2011)
Derakhshan, F., Roessler, H., Schefczik, P., Randriamasy, S.: On prediction of resource consumption of service requests in cloud environments. In: The Proceedings of the 20th International Conference on Innovations in Clouds, Internet and Networks (ICIN), pp. 169–176 (2017)
Matsunaga, A., Fortes, J.A.B.: On the use of machine learning to predict the time and resources consumed by applications. In: The Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 495–504 (2010)
Xu, J., Fortes, J.A.B.: Multi-objective virtual machine placement in virtualized data center environments. In: The Proceedings of the IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, pp. 179–188 (2010)
Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybernet. 4, 364–378 (1971)
Oh, S.-K., Kim, W.-D., Pedrycz, W., Park, B.-J.: Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization. Fuzzy Sets Syst. 163(1), 54–77 (2011)
Tsekouras, G.E.: A simple and effective algorithm for implementing particle swarm optimization in RBF network’s design using input-output fuzzy clustering. Neurocomputing 108, 36–44 (2013)
Lee, T.-T., Jeng, J.-T.: The Chebyshev-polynomials-based unified model neural networks for function approximation. IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 28(6), 925–935 (1998)
Rigos, A., Tsekouras, G.E., Vousdoukas, M.I., Chatzipavlis, A., Velegrakis, A.F.: A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery. Integr. Comput.-Aided Eng. 23, 141–160 (2016)
GeSI: SMART 2020: Enabling the low carbon economy in the information age (2008)
Kaplan, J., Forrest, W., Kindler, N.: Revolutionizing Data Center Energy Efficiency, Technical Report. McKinsey & Company (2008)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, Berlin (2001)
Tikhonov, A.N., Goncharsky, A.V., Stepanov, V.V., Yagola, A.G.: Numerical methods for the solution of ill-posed problems. Kluwer Academic Publishers, Dordrecht (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Troumbis, I.A., Tsekouras, G.E., Kalloniatis, C., Papachiou, P., Haralambopoulos, D. (2018). Modeling Data Center Temperature Profile in Terms of a First Order Polynomial RBF Network Trained by Particle Swarm Optimization. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-01421-6_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01420-9
Online ISBN: 978-3-030-01421-6
eBook Packages: Computer ScienceComputer Science (R0)