A new highly accurate workload model for campus email traffic

Boukoros, Spyros; Kalampogia, Athina; Koutsakis, P.

doi:10.1109/iccnc.2016.7440639

Cited by 2 publications

(8 citation statements)

References 9 publications

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“…In order to overcome the limitations of probabilistic models, email traffic was modelled using RNNs in [5], treating email traffic workload as a time series problem. The prediction accuracy was found to be substantially higher than that of the probabilistic modelling approach in [6].…”

Section: Introductionmentioning

confidence: 75%

“…Returning to the topic of email traffic prediction, Boukoros et al [6] divided email traffic into five categories: system incoming/outgoing, users incoming/outgoing and spam traffic. The datasets were collected from the Technical University of Crete (TUC) in Greece for nine non-consecutive weeks between February and October 2014.…”

Section: Related Workmentioning

confidence: 99%

“…In order to address the limitations of probabilistic models, email traffic was evaluated as a time series problem using Recurrent Neural Networks in [5] and the prediction accuracy was found to be substantially higher than the probabilistic modelling approach in [6]. In this work we provide further investigation with the use of tuned hyper-parameters.…”

Section: Related Workmentioning

confidence: 99%

“…As in [6] The Recurrent Neural Network model has achieved significantly higher accuracy compared to the probability distribution models Table 1 provides a snapshot of the most relevant work on email traffic modelling.…”

Section: Related Workmentioning

confidence: 99%

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Modelling email traffic workloads with RNN and LSTM models

Boukoros

Nugaliyadde

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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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.

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Section: Introductionmentioning

confidence: 75%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Modelling email traffic workloads with RNN and LSTM models

Boukoros

Nugaliyadde

et al. 2020

Hum. Cent. Comput. Inf. Sci.

Self Cite

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“…In our previous work in [13] we modeled the email traffic data collected over nine weeks from the Technical University of Crete (TUC). We evaluated various well-known distributions from the relevant literature on workload characterization, in terms of their fitting accuracy to our data.…”

Section: Introductionmentioning

confidence: 99%

Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks

Boukoros

Nugaliyadde

Marnerides

et al. 2017

Neural Information Processing

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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.

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A new highly accurate workload model for campus email traffic

Cited by 2 publications

References 9 publications

Modelling email traffic workloads with RNN and LSTM models

Modelling email traffic workloads with RNN and LSTM models

Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks

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