Modelling email traffic workloads with RNN and LSTM models View Full Text


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Article Info

DATE

2020-09-02

AUTHORS

Khandu Om, Spyros Boukoros, Anupiya Nugaliyadde, Tanya McGill, Michael Dixon, Polychronis Koutsakis, Kok Wai Wong

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. More... »

PAGES

39

References to SciGraph publications

  • 2018-10-12. Classifying 3D objects in LiDAR point clouds with a back-propagation neural network in HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
  • 2019-12-02. Emotion classification based on brain wave: a survey in HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
  • 2012. Efficient BackProp in NEURAL NETWORKS: TRICKS OF THE TRADE
  • 2011. Dickey-Fuller Tests in INTERNATIONAL ENCYCLOPEDIA OF STATISTICAL SCIENCE
  • 2017-10-29. Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks in NEURAL INFORMATION PROCESSING
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1186/s13673-020-00242-w

    DOI

    http://dx.doi.org/10.1186/s13673-020-00242-w

    DIMENSIONS

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