Machine Learning-Based Network Status Detection and Fault Localization

Mohammed, Ayse Rumeysa; Mohammed, Shady A.; Côté, D.; Shirmohammadi, Shervin

doi:10.1109/tim.2021.3094223

Cited by 11 publications

(3 citation statements)

References 33 publications

(30 reference statements)

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“…It achieves that by utilizing an artificial neural network trained with a dataset of actual video streaming traces. The work in [11] also uses ML to detect the network status as normal, congestion, and network fault, as well as localize the fault with accuracies of up to 99%. But, as interesting as these methods are, they only detect the fault and do not take autonomous actions to fix it.…”

Section: Related Workmentioning

confidence: 99%

Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Altamimi

Côté

et al. 2023

IEEE Access

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Today's Network Operation Centres (NOC) consist of teams of network professionals responsible for monitoring and taking actions for their network's health. Most of these NOC actions are relatively complex and executed manually; only the simplest tasks can be automated with rules-based software. But today's networks are getting larger and more complex. Therefore, deciding what action to take in the face of non-trivial problems has essentially become an art that depends on collective human intelligence of NOC technicians, specialized support teams organized by technology domains, and vendors' technical support. This model is getting increasingly expensive and inefficient, and the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this article, we investigate whether an autonomous NOC can achieve superintelligence; i.e., it can recommend or take actions that lead to better results than those achieved by the collective human intelligence, in this case rules designed by human experts. Our investigation is inspired by the superintelligence achieved in computer games recently. Specifically, we build an Action Recommendation Engine based on Reinforcement Learning, a type of Artificial Intelligence method, and we train it with expert rules and lets it explore actions by itself. We then show that it can learn new and more efficient strategies that outperform expert rules designed by humans. This can be used in face of network problems to either quickly recommend actions to NOC technicians or autonomously take actions for fast recovery.

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Section: Related Workmentioning

confidence: 99%

Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Altamimi

Côté

et al. 2023

IEEE Access

Self Cite

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“…In (Reddy et al, 2021), fault detection and classification was proposed based on weighted K-nearest neighbor and decision tree (DT) in low-voltage-DC-microgrid. Mohammed et al (2021) proposed a method using machine learning that detects network status and locates IJICC 17,2 faults. They used a decision tree, gradient boosting (GB) and extreme GB ML algorithms to classify network states into three classes: normal, congestion and network fault.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields

Rahi,

Ebrahimnejad,

Motameni

2023

IJICC

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PurposeTaking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.Design/methodology/approachThe proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.FindingsThe proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.Research limitations/implicationsBy expanding the dimensions of the problem, the model verification space grows exponentially using automata.Originality/valueUnlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

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“…Machine Learning approaches have been employed in many instrumentation applications, such as quality monitoring of laser cladding [12], vortex flow-meter design [13], measurement of residual Oxygen concentration [14], vision-based measurement systems [15], state-of-charge prediction [16], localization of faults and network status detection [17], Parkinson's disease diagnostics [18], machine health monitoring [19], real-time aging prediction of integrated circuits [20], vision systems calibration in welding robots [21], and sleep apnea analysis [22]. Other machine learning forms have adopted Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) networks to solve various optimization problems [23].…”

Section: Introductionmentioning

confidence: 99%

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

Abouheaf

Boase

Gueaieb

et al. 2023

Engineering Applications of Artificial Intelligence

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Machine Learning-Based Network Status Detection and Fault Localization

Cited by 11 publications

References 33 publications

Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

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