Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks

Boukoros, Spyros and Nugaliyadde, Anupiya and Marnerides, Angelos and Vassilakis, Costas and Koutsakis, Polychronis and Wai Wong, Kok (2017) Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks. In: Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science . Springer, Cham, pp. 57-66. ISBN 9783319701387

PDF (RNN to Model Server Workloads for Campus Email Traffic_Submitted)
RNN_to_Model_Server_Workloads_for_Campus_Email_Traffic_Submitted.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (185kB)


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:
Contribution in Book/Report/Proceedings
Additional Information:
The final publication is available at Springer via © Springer International Publishing AG 2017
ID Code:
Deposited By:
Deposited On:
12 Sep 2017 08:54
Last Modified:
11 May 2022 01:35