Table 15 Summary of ML-based Resource Allocation
Ref. | ML Technique | Network | Dataset | Features | Output | Evaluation | |
|---|---|---|---|---|---|---|---|
Settings | Results | ||||||
Baldo et al. [35] | Supervised: · MLP-NN | Wireless networks | Simulation data generated using ns-Miracle simulator | · Signal to noise ratio · Received frames · Erroneous frames · Idle time | · Throughput · Delay · Reliability | 2 layers with 6 neurons in the hidden layer | Very good accuracy |
Bojovic [65] | Supervised: · MLP-NN | Wireless LAN | Synthetic data generated using testbed | · Signal to noise ratio · Probability of failure · Business ratio · Average beacon delay · Number of detected stations | · Throughput of an access point | 2 layers with varying number of nodes in the hidden layer, maximum number of epochs, and learning rate | NRMSE = 8% |
Adeel et al. [6] | RNN with GD, AIWPSO, and DE | Cellular network | Synthetically generated using a SEAMCAT LTE simulator | · Signal to interference noise ratio · Inter-cell-interference · Modulation/coding schemes · Transmit power | Throughput | 5-8-1a | Mean square error · AIWPSO: 8.5 ×10−4· GD: 1.03 ×10−3 · DE: 9.3 ×10−4 |
Testolin et al. [443] | Supervised: · Linear classifier Unsupervised: · RNN | Wireless networks | 38 video clips taken from CIF | · Video frame size | · Quality level of each video in terms of the average SSIM index | 32 visible units with a varying number of hidden units | RMSE < 3% |
Mijumbi et al. [312] | RL · Q-learning (ε-greedy and softmax) | VNs | Simulation on ns-3 and real Internet traffic traces | States · Percentages of allocated and unused resources in substrate nodes and links | Actions · Increase or decrease the percentages of allocated resource | 29 states, 9 actions | Improved the acceptance ratio |
Mijumbi et al. [313] | Supervised: · FNN | VNF chains | VoIP traffic traces | · Dependency of resource requirements of each VNFC on its neighbor VNFCs · Historical local VNFC resource utilization | · Resource requirements of each VNFC | 2 NNs for each VNFC | Accuracy ∼ 90% |
Shi et al. [410] | Supervised: · MDP · BN | VNF chains | Simulation data generated using WorkflowSim | · Historical resource usage | · Future resource reliability | Running time for MDP: O(tv+1), where t and v stand for the number of NFV component tasks and the number of VMs, respectively | Better than other greedy methods in terms of cost |