Recent Advances in Fault Localization in Computer Networks

Dusia, Ayush; Sethi, Adarshpal S.

doi:10.1109/comst.2016.2570599

Cited by 63 publications

(42 citation statements)

References 73 publications

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“…In [13], [14], the authors presented a consolidated taxonomy on different techniques that have been developed for localizing link faults. These techniques are broadly categorized as rule-based techniques [15], [16], case-based techniques [17], [18], probability-based techniques [19]- [22] and model-based techniques [23], [24].…”

Section: A Network Fault Localizationmentioning

confidence: 99%

Machine Learning-Based Link Fault Identification and Localization in Complex Networks

Srinivasan

Truong-Huu

Gurusamy

2019

IEEE Internet Things J.

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With the proliferation of network devices and rapid development in information technology, networks such as Internet of Things are increasing in size and becoming more complex with heterogeneous wired and wireless links. In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e.g., a wireless node changes its access point. Identifying whether a link disconnection or a link reconnection has occurred and localizing the failed link become a challenging problem. An active probing approach requires a long time to probe the network by sending signaling messages on different paths, thus incurring significant communication delay and overhead. In this paper, we adopt a passive approach and develop a three-stage machine learning-based technique, namely ML-LFIL that identifies and localizes link faults by analyzing the measurements captured from the normal traffic flows, including aggregate flow rate, end-to-end delay and packet loss. ML-LFIL learns the traffic behavior in normal working conditions and different link fault scenarios. We train the learning model using support vector machine, multi-layer perceptron and random forest. We implement ML-LFIL and carry out extensive experiments using Mininet platform. Performance studies show that ML-LFIL achieves high accuracy while requiring much lower fault localization time compared to the active probing approach.

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Section: A Network Fault Localizationmentioning

confidence: 99%

Machine Learning-Based Link Fault Identification and Localization in Complex Networks

Srinivasan

Truong-Huu

Gurusamy

2019

IEEE Internet Things J.

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“…A survey [1] of recent fault localization techniques expands on the taxonomy presented in [2]. The taxonomy presented consists of three categories of fault localization: Artificial Intelligence, Model Traversing, and Graph-theoretic.…”

Section: A Related Workmentioning

confidence: 99%

Detecting Network Soft-failures with the Network Link Outlier Factor (NLOF)

Mendoza

Dasari

McGarry

2019

2019 IEEE International Conference on Big Data (Big Data)

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In this paper, we describe and experimentally evaluate the performance of our Network Link Outlier Factor (NLOF) for detecting soft-failures in communication networks. The NLOF is computed using the throughput values derived from NetFlow records. The flow throughput values are clustered in two stages, outlier values are determined within each cluster, and the flow outliers are used to compute the outlier factor or score for each network link. When sampling NetFlow records across the full span of a network, NLOF enables the detection of soft-failures across the span of the network; large NLOF scores correlate well with links experiencing failure.

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“…Fault management in the NVE includes two core aspects: fault diagnosis and fault recovery [5,6]. Fault diagnosis uses the relevant technology and observed symptom set to locate the exact network faults.…”

Section: Related Workmentioning

confidence: 99%

Symptom-aware hybrid fault diagnosis algorithm in the network virtualization environment

Su¹,

Meng²,

Han³

et al. 2019

Turk J Elec Eng & Comp Sci

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Finally, a heuristic greedy algorithm is proposed to select appropriate actions based on minimum weight set covering method with minimum cost. Simulation results show that, compared with other algorithms, the SAHFD algorithm has a higher accuracy rate, lower false positive rate, and better environmental adaptability in the NVE.(NVE), the network infrastructure and network services are decoupled and multiple heterogeneous virtual networks (VNs) are allowed to share the substrate network (SN) resources through abstraction, distribution, and isolation [4]. Each virtual network (VN) consists of virtual nodes and virtual links, which can be dynamically constructed and reasonably configured according to the actual needs of users. An example of the NVE is shown in Figure 1. Different VNs are isolated from each other, which can use different network technologies and protocols to provide customized traditional network architecture and more flexible services, and greatly improve the efficiency of network resource utilization. Network virtualization technology effectively reduces the cost of network operation and improves the quality of network service, which has become one of the key technologies for next-generation Internet.Faults are network events that are the root cause of problems in the network. Faults in SNs are not * Correspondence: glgiuip@163.com This work is licensed under a Creative Commons Attribution 4.0 International License.

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Recent Advances in Fault Localization in Computer Networks

Cited by 63 publications

References 73 publications

Machine Learning-Based Link Fault Identification and Localization in Complex Networks

Machine Learning-Based Link Fault Identification and Localization in Complex Networks

Detecting Network Soft-failures with the Network Link Outlier Factor (NLOF)

Symptom-aware hybrid fault diagnosis algorithm in the network virtualization environment

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