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Table 13 Summary of congestion inference from the estimation of different network parameters

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

Ref. ML Technique Network Dataset Features Output Evaluation
   (location)     Settings Resultsab
El Khayat et al. [238] Supervised: · MLP-NN · MART · Bagging DT · Extra-trees(offline) Wired(end-system) Synthetic data: · ns-2 simulation ·> 1k random topologies Data distribution: · Training = 18k · Testing = 7.6k · Packet size · RTT: avg, min, max, stdev · Sesion loss rate · Initial timeout · Packets ACK at once · Session duration · TLR Prediction: · Throughput Ensemble DT: · 25 trees NN: N/A MSE (10−3)c: · 0.245 · 0.423 · 0.501 · 0.525
Mirza et al. [316] Supervised: · SVR(offline) Multi-path wired(end-system) Synthetic data: · Laboratory testbed - Dumbbell multi- path topology · RON testbed · Queuing delay · Packet loss · Throughput Prediction: · Throughput · 2 input features · RBF kernel Rate of predictions with RPE ≤ 10%: · Lab: 51% · RON: 87%
Quer et al. [371] Supervised: · BN(offline) WLAN (access point) Synthetic data: · ns-3 simulation · Star topology Data distribution: · Training =40k · Testing =10k · MAC-TX · MAC-RTX · MAC contention window · CWND · CWND status · RTT · Trhoughput Prediction: · Throughput DAG: · 7 vertices · 6 edges Using MAC-TX: · NRMSE =0.37 Using all features: · NRMSE =0.27
Mezzavilla et al. [309] Supervised: · BN(offline) WANET(end-system) Synthetic data: · ns-3 simulation · Topology: - (not mentioned) · MAC-TX · MAC-RTX · Slots before TX · Queue TX packets · Missing entries in IP table Classification: · Static · Mobile DAG: · 6 vertices · 5 edges Using MAC-TX and MAC-RTX: · Precision =0.88 · Recall =0.91
Fixed-Share Experts [22] Supervised: · WMA (online) · WANET · Wired · Hybrid wired and wireless (end-system) Synthetic data: · QualNet simulation · Topology: - Random WANET - Dumbbell wired Real data: · File transfer · Wired and WLAN · RTT Prediction: · RTT · 1 input feature · 100 experts · Simple experts MAE (ticks): · Synthetic data (ticks of 500ms): =0.53 · Real data (ticks of 4ms): =2.95
SENSE [128] Supervised: · WMA(online) Hybrid wired and wireless (end-system) Real data: · Dataset from [22] · RTT Prediction: · RTT · 1 input feature · 100 experts · EWMA experts MAE (ticks of 4ms): =1.55
ACCPndn [230] Supervised: · TLFN - PSO - GA(online) NDN(controller node) Synthetic data: · ns-2 simulation · Topology: - DFN - SWITCH Data distribution: · Training =70% · Validation =15% · Testing =15% · PIT entries rate Prediction: · PIT entries rate ·R input neurons · 2 hidden layers with R neurons ·R output neurons R: number of contributing routers MSE: · PSO-GA =2.23 · GA-PSO =3.25 · PSO =4.05 · GA =5.65 · BP =7.27
Smart-DTN-CC [412] Reinforcement: · Q-learning - Boltzmann - WoLF(online) DTN (node) Synthetic data: · ONE simulation: · Random topology States: · Input rate · Output rate · Buffer space Reward: · State transition Decision-making: · Action to control the congestion(finite action-set: 12 actions) · 3 input features · 4 states · 12 actions Improvement to CCC: · Delivery ratio =53% · Delay =95%
  1. aAverage values. Results vary according to the configured network parameters (e.g. topology, mobility, traffic)
  2. bError metrics: MAE, MSE, NRMSE, and Relative Prediction Error (RPE)
  3. cRespectively to the list of elements in the column ML technique