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Table 3 Summary of TSF and non-TSF-based traffic prediction

From: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Ref. ML Technique Application Dataset Features Output Evaluation
   (approach) (availability)   (training) Settings Resultsab
NBP [141] Supervised: · MLP-NN (offline) End-to-end path bandwidth availability prediction (TSF) NSF TeraGrid dataset (N/A) Max, Min, Avg load observed in past 10 s  30 s Available bandwidth on a end-to-end path in future epoch Number of features =3 MLP-NN: ·(N/A) MSE =8%
Cortez et al. [104] Supervised: · NNE trained with Rp (offline) Link load and traffic volume prediction in ISP networks (TSF) SNMP traffic data from 2 ISP nets, · traffic on a transatlantic link · aggregated traffic in the ISP backbone (N/A) Traffic volume observed in past few minutes several days Expected traffic volume Number of features =69 5 NNs NNE: · all SLPs for dataset1 · 1 hidden layer MLPs with 68 neurons for dataset2 1h lookahead: · MAPE =1.43%5.23% 1h  24h lookahead: · MAPE =6.34%23.48%
Bermolen et al. [52] Supervised: · SVR (offline) Link load prediction in ISP networks (TSF) Internet traffic collected at the POP of an ISP network (N/A) Link load observed at τ time scale Expected link load Number of features =d samples with d=1..30 Number of support vectors: · varies with d (e.g.  320 for d=10) RMSE < 2 for τ=1ms and d=5·≈ AR ·10% less than MA
Chabaa et al. [86] Supervised: MLP-NN with different training algorithms (GD, CG, SS, LM, Rp) (offline) Network traffic prediction (TSF) 1000 points dataset (N/A) Past measurements Expected traffic volume Number of features (N/A) MLP-NN: · 1 hidden layer LM: · RMSE =0.0019 RPE =0.0230% Rp: · RMSE =0.0031 RPE =0.0371%
Zhu et al. [500] Supervised: MLP-NN with PSO-ABC (offline) Network traffic prediction (TSF) 2-week hourly traffic measurements (N/A) N past days hourly traffic volume Expected next-day hourly traffic volume Number of features =5 MLP-NN (5, 11, 1) PSO-ABC: ·30 particles of length=66 MSE =0.006 on normalized data 50% less than BP
Li et al. [274] Supervised: MLP-NN (offline) Traffic volume prediction on an inter-DC link (Regression) 6-week inter-DC traffic dataset from Baidu · SNMP counters data collected every 30 s · Top-5 applications traffic data collected every 5 min (N/A) Level-N wavelet transform used to extract time and frequency features from total and elephant traffic volumes time series k×30-s ahead expected traffic volume Number of wavelets: ·N=10 Number of features =k×120 for N=10 1 hidden layer MLP-NN RRMSE =4%10% for k=140
Chen et al. [94] Supervised: · KBR · LSTM-RNN (offline) Inferring future traffic volume based on flow statistics (regression) Network traffic volume and flow count collected every 5 min over a 24-week period (public) Flow count Expected traffic volume Number of features: · 1 feature (past sample) LSTM-RNN: ·(N/A) RNN · MSE > 0.3 on normalized data · 0.05 higher than KBR · twice as much as RNN fed with traffic volume time series
Poupart et al. [365] Supervised: · GPR · oBMM · MLP-NN (offline) Early flow-size prediction and elephant flow detection (classification) 3 university and academic networks datasets with over three million flows each (public) · source IP · destination IP · source port · destination port · protocol · server vs. client · size of 3 first packets Flow size class; elephant vs. non-elephant Number of features: · 7 features MLP-NN: · (106,60,40,1) GPR: · TPR > 80% · TNR > 80% oBMM: · TPR and TNR ≈100% on one dataset · TPR < 50% on other datasets MLP-NN: · TPR > 80% · lowest TNR < 80%
  1. aAverage values. Results vary according to experimental settings
  2. bAccuracy metrics: mean square error (MSE), relative prediction error (RPE), mean absolute prediction error (MAPE), average root mean square error (RMSE)