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Table 18 Summary of ML-based fault localization

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
Chen et al. [91] DT (C4.5) Network systems Snapshots of logs from eBay A complete request trace ·Request type ·Request name ·Pool ·Host ·Version ·Status of each request Different faulty elements 10 one hour snapshots with 14 faults in total ·Precision: 92% ·Recall: 93%
Ruiz et al. [393] BN Optical network Synthetically generated time series Quality of Transmission (QoT) parameters ·Received power ·Pre-forward error correction bit error rate (pre-FEC BER) Detect one of the two fault scenarios ·Tight filtering ·Inter-channel interference 5,000 and 500 time series for training and testing, respectively Accuracy: 99.2%
Khanafer et al. [237] BN and EMD Cellular network Synthetically generated from a simulated and a real UMTS network ·Causes of faults ·Symptoms, i.e., alarms and KPIs Identify the cuase of the fault 77 and 42 faulty cells for training and testing, respectively Accuracy: 88.1%
Kiciman and Fox [241] Supervised: · DT (ID3) Three-tier enterprise applications Generated using small testbed platform Paths classified as normal or anomalous Hardware and software components that are correlated with the failures Three different DTs were evaluated Correctly detect 89% to 96% of major failures
Johnsson et al. [225] Unsupervised: discrete state-space particle filtering IP network Discrete event simulator ·Active network measurements ·Probabilistic inference ·Change detection Probability mass function indicating the location of the faulty components Operations per filter: O(|G|), where |G| is the number of edges in a graph G Found the location of faults and performance degradations in real time
Barreto et al. [40] Unsupervised: · Winner-Take-All (WTA) · Frequency-Sensitive Competitive Learning (FSCL) · SOM ·Neural-Gas algorithm (NGA) Cellular network Simulation study State vectors representing the normal functioning of a network State vector causing the abnormally 400 vectors were used for training and 100 vectors were used for testing False alarm: · WTA: 12.43 · FSCL: 10.20 · SOM: 8.75 ·NGA: 9.50