@conference {boukoros2017,
	title = {Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks},
	booktitle = {Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017)},
	year = {2017},
	pages = {57{\textendash}66},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	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 us-er and system emails, as well as spam. We initially tested some of the most popu-lar 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.},
	keywords = {Email traffic, Model server workload, Recurrent neural network, Time series modeling},
	doi = {10.1007/978-3-319-70139-4_6},
	author = {Spyros Boukoros and Anupiya Nugaliyadde and Angelos Marnerides and Costas Vassilakis and Polychronis Koutsakis and Kok Wai Wong}
}
