Table 13 Summary of congestion inference from the estimation of different network parameters
Ref. | ML Technique | Network | Dataset | Features | Output | Evaluation | |
|---|---|---|---|---|---|---|---|
| Â | Â | (location) | Â | Â | Â | 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% |