Table 16 Summary of ML-based fault prediction
Ref. | ML Technique | Network | Dataset | Features | Output | Evaluation | |
|---|---|---|---|---|---|---|---|
| Â | Â | (location) | Â | Â | (training) | Settings | Results |
Hood et al. [193] | Supervised: · BN | Campus network | Data collected from router | Management information base (MIB) variables for following network functions · Interface group · IP group · UDP group | Predict network health | 500 samples for each of 14 MIB variables of the 3 network functions | Predict approximately 8 min before fault occurrence |
Kogeda et al. [248] | Supervised: · BN | Cellular network | Simulation with fault injection | ·Power ·Multiplexer ·Cell ·Transmission | Faulty or not | 4 nodes each with 3 states | Confidence level of 99.8% |
Snow et al. [414] | Supervised: · NN (MLP) | Wireless network | Generated using discrete time event simulation | ·Mean time to failure ·Mean time to restore ·Time Profile ·Run Time | Dependability of a network ·Survivability ·Availability ·Failed components ·Reportable outages | 14 inputs, 10 and 5 nodes in the first and second hidden layer, respectively | Closely approximates reportable outages |
Wang et al. [466] | Supervised: · DT (J4.8) · Rule learners (JRip) · SVM · BN · Ensemble | Wireless sensor network | Generated using sensor network testbed | ·Received signal strength indication ·Send and forward buffer sizes ·Channel load assessment ·Forward and backward | Link quality estimation | 10-fold cross validation was used with 5000 samples | Accuracy · 82% for J4.8 ·80% for JRip |
Lu et al. [285] | Manifold learning: ·SHLLE | Distributed systems | Generated from a testbed of a distributed environment with a file transfer application | System performance ·interface group ·IP group ·TCP group ·UDP group | Prediction of network, CPU, and memory failures | Not provided | · Precision: 0.452 ·Recall: 0.456 · False positive rate: 0.152 |
Pellegrini et al. [355] | Different ML methods: ·Linear Regression · M5P · REP-Tree · LASSO · SVM · Least-Square SVM | Multi-tier e-commerce web application | Generated from a testbed of a virtual architecture | Different system performance | Remaining Time to Failure (RTTF) | Not provided | Soft mean absolute error · Linear regression: 137.600 · M5P: 79.182 · REP-Tree: 69.832 · LASSO as a Predictor: 405.187 · SVM: 132.668 · Least-Square SVM: 132.675 |
Wang et al. [469] | Supervised: · Double-exponential smoothing (DES) and SVM | Optical network | Real data collected from an optical network of a telecommunications operator | Indicators In Board Data: ·Input Optical Power · Laser Bias Current · Laser Temperature Offset · Output Optical Power · Environmental Temperature ·Unusable Time | Predicting equipment failure | 10-fold cross-validation was used to test model accuracy | DES with SVM · Prediction accuracy: 95% |
Kumar et al. [255] | Unsupervised: · DNN with Autoencoders | Cellular Network | Fault data from one of the national mobile operators of USA for a month | Historical data of fault occurrence and their inter-arrival times | Prediction of inter-arrival time of faults | 10 neurons in the hidden layer | DNN with autoencoders · NRMSE: 0.122092 ·RMSE: 0.504425 |