Journal article
Modelling email traffic workloads with RNN and LSTM models
Human-centric Computing and Information Sciences, Vol.10(1), Art. 39
2020
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.
Details
- Title
- Modelling email traffic workloads with RNN and LSTM models
- Authors/Creators
- K. Om (Author/Creator) - Murdoch UniversityS. Boukoros (Author/Creator) - TU DarmstadtA. Nugaliyadde (Author/Creator) - Murdoch UniversityT. McGill (Author/Creator) - Murdoch UniversityM. Dixon (Author/Creator) - Murdoch UniversityP. Koutsakis (Author/Creator) - Murdoch UniversityK.W. Wong (Author/Creator) - Murdoch University
- Publication Details
- Human-centric Computing and Information Sciences, Vol.10(1), Art. 39
- Publisher
- SpringerOpen
- Identifiers
- 991005540949007891
- Copyright
- © 2020 BioMed Central Ltd unless otherwise stated.
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
72 File views/ downloads
133 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.18 Power Systems & Electric Vehicles
- 4.18.296 Energy Forecasting
- Web Of Science research areas
- Computer Science, Information Systems
- ESI research areas
- Computer Science