Skip to main content

Table 16 Summary of ML-based fault prediction

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

Ref. ML Technique Network Dataset Features Output Evaluation
   (location)    (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