Modelling email traffic workloads with RNN and LSTM models

Om, K., Boukoros, S., Nugaliyadde, A., McGill, T., Dixon, M., Koutsakis, P.ORCID: 0000-0002-4168-0888 and Wong, K.W. (2020) Modelling email traffic workloads with RNN and LSTM models. Human-centric Computing and Information Sciences, 10 (1). Art. 39.

Abstract

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

Item Type:	Journal Article
Murdoch Affiliation(s):	Information Technology, Mathematics and Statistics
Publisher:	SpringerOpen
Copyright:	© 2020 BioMed Central Ltd unless otherwise stated.
URI:	http://researchrepository.murdoch.edu.au/id/eprint/57570

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