Modeling server workloads for campus email traffic using recurrent neural networks

Boukoros, S., Nugaliyadde, A., Marnerides, A. , Vassilakis, C., Koutsakis, P. and Wong, K. W. (2017) Modeling server workloads for campus email traffic using recurrent neural networks. In: Liu, D., Xie, S., Li, Y., Zhao, D. and El-Alfy, E.-S. M. (eds.) Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14–18, 2017, Proceedings, Part V. Series: Lecture notes in computer science (10638). Springer: Cham, pp. 57-66. ISBN 9783319701387 (doi: 10.1007/978-3-319-70139-4_6)

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As email workloads keep rising, email servers need to handle this explosive growth while offering good quality of service to users. In this work, we focus on modeling the workload of the email servers of four universities (2 from Greece, 1 from the UK, 1 from Australia). We model all types of email traffic, including user and system emails, as well as spam. We initially tested some of the most popular distributions for workload characterization and used statistical tests to evaluate our findings. The significant differences in the prediction accuracy results for the four datasets led us to investigate the use of a Recurrent Neural Network (RNN) as time series modeling to model the server workload, which is a first for such a problem. Our results show that the use of RNN modeling leads in most cases to high modeling accuracy for all four campus email traffic datasets.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Marnerides, Dr Angelos
Authors: Boukoros, S., Nugaliyadde, A., Marnerides, A., Vassilakis, C., Koutsakis, P., and Wong, K. W.
College/School:College of Science and Engineering > School of Computing Science
Published Online:29 October 2017

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