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  • RNN to Model Server Workloads for Campus Email Traffic_Submitted

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-70139-4_6 © Springer International Publishing AG 2017

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks

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  • Spyros Boukoros
  • Anupiya Nugaliyadde
  • Angelos Marnerides
  • Costas Vassilakis
  • Polychronis Koutsakis
  • Kok Wai Wong
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Abstract

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.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-70139-4_6 © Springer International Publishing AG 2017