DeepFlow: Towards Network-Wide Ingress Traffic Prediction Using Machine Learning At Large Scale

Fischer, Sebastian; Katsarou, Katerina; Holschke, Oliver

doi:10.1109/isncc49221.2020.9297301

Cited by 5 publications

(2 citation statements)

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“…This complexity may make it challenging to implement the approach in real-world scenarios, particularly in cases where there are limited computational resources. S. Fischer et al [ 13 ] introduced DEEPFLOW, a traffic prediction system that uses several machine learning approaches to process ingress traffic data and anticipates all traffic flows. The prediction model is divided into two categories: statistical and deep learning models based on neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Methodology Data Preprocessing Forecast Horizon Dataset Outlier Detection Evaluation Metric [ 7 ] Gaussian processes Based stations are grouped based on geographical location Single-Step and multi-step Univariate, temporal N/A Mean Absolute Percentage Error (MAPE) [ 9 ] linear regression, Passive Aggressive Regressor, k neighbors regressor, and multi-layer perceptron regressor N/A Single-Step Univariate, temporal N/A TLPQ (Traffic Level Prediction Quality), Root Mean Square Percentage Error (RMSPE) [ 11 ] RNN and CNN N/A Single-Step Multi-variate, temporal, and spatial N/A MAE (Mean Absolute Error), RMSE (Root Mean Square Error), MAPE, and MA (Mean Accuracy) [ 12 ] ARIMA and RNN Discrete wavelet transform DWT) decomposes a given discrete signal into orthogonal wavelet functions. Single-Step Univariate, temporal N/A NRMSE (Normalized Root Mean Square Error) [ 13 ] LSTM, Seq2SeqLSTM, ConvLSTM ...…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Saha,

Haque,

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2024

Sensors

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The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons—six, nine, and twelve steps—demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model’s performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.

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Section: Literature Reviewmentioning

confidence: 99%