Table 3 Summary of TSF and non-TSF-based traffic prediction
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 =6∼9 5 NNs NNE: · all SLPs for dataset1 · 1 hidden layer MLPs with 6∼8 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=1∼40 |
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% |