A Framework for Supporting Intelligent Fault and Performance Management for Communication Networks

Li, Hongjun; Baras, John S.

doi:10.1007/3-540-45508-6_20

Cited by 6 publications

(4 citation statements)

References 22 publications

(20 reference statements)

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“…The solution is based on a distributed cooperative multi-agent system, with probabilistic networks as the framework for knowledge representation and evidence inferencing. A solution for supporting intelligent fault management, and performance operations for communications networks is described in [ 30 ]. Fault management automation via intelligent mobile agents is analyzed in the paperwork of [ 31 , 32 , 33 ], and deep Q-learning for self-organizing networks’ fault management and radio performance improvement is considered in [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions

Minea

Dumitrescu

Minea³

2021

Sensors

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The article presents a research in the field of complex sensing, detection, and recovery of communications networks applications and hardware, in case of failures, maloperations, or unauthorized intrusions. A case study, based on Davis AI engine operation versus human maintenance operation is performed on the efficiency of artificial intelligence agents in detecting faulty operation, in the context of growing complexity of communications networks, and the perspective of future development of internet of things, big data, smart cities, and connected vehicles. (*). In the second part of the article, a new solution is proposed for the detection of applications faults or unauthorized intrusions in traffic of communications networks. The first objective of the proposed method is to propose an approach for predicting time series. This approach is based on a multi-resolution decomposition of the signals employing the undecimate wavelet transform (UWT). The second approach for assessing traffic flow is based on the analysis of long-range dependence (LRD) (for this case, a long-term dependence). Estimating the degree of long-range dependence is performed by estimating the Hurst parameter of the analyzed time series. This is a relatively new statistical concept in communications traffic analysis and can be implemented using UWT. This property has important implications for network performance, design, and sizing. The presence of long-range dependency in network traffic is assumed to have a significant impact on network performance, and the occurrence of LRD can be the result of faults that occur during certain periods. The strategy chosen for this purpose is based on long-term dependence on traffic, and for the prediction of faults occurrence, a predictive control model (MPC) is proposed, combined with a neural network with radial function (RBF). It is demonstrated via simulations that, in the case of communications traffic, time location is the most important feature of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions

Minea

Dumitrescu

Minea³

2021

Sensors

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“…Mobile agents as described before may help in solving the problem of visiting each meter and executed on the embedded web server checking the status and reading the power consumption value, encapsulating this data and move to visit another power meter. We assume that the embedded micro web-server in each meter is capable of hosting this mobile agent and has the required and enough functionality of handling the mobile agent's issues including security and fault tolerance [3,4]. In this section we will present how to model the problem of Mobile Agent Automatic Meter Reader and or Monitor (MAMR/M or simply MAMR).…”

Section: Fig 1 Power Metering Systemsmentioning

confidence: 99%

Novel Approach for Remote Energy Meter Reading Using Mobile Agents

Tahboub

Lazarescu

2006

Third International Conference on Information Technology: New Generations (ITNG'06)

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Power or Energy meter systems can be incorporated with embedded controllers such as Micro web-servers with Ethernet port to transmit the reader data over the Internet. Such data can be then fed and integrated into existing Energy Management Systems located at power companies and organizations. The problem of efficiently collecting data from a large number of distributed embedded web-servers in the energy meters is still a challenging problem. Mobile Agents are executing programs that migrate during execution and present methods for maintaining and using distributed systems. In this paper, we develop a new abstract approach using Mobile Agents to solve the problem of efficiently collecting meters readings.

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“…This problem has several differences from traditional network fault management problems. Typical network fault management deals with localized failure [5] [7]. For instance, when there is something wrong with a switch, what propagates is not the failure but the consequences of the failure on the data plane (e.g., congestion builds up at upstream nodes).…”

Section: The Problem Featuresmentioning

confidence: 99%

Using Neural Networks to Identify Control and Management Plane Poison Messages

Shayman

Skoog

2003

Integrated Network Management VIII

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Poison message failure propagation is a mechanism that has been responsible for large scale failures in both telecommunications and IP networks: Some or all of the network elements have a software or protocol 'bug' that is activated on receipt of a certain network control/management message (the poison message). This activated 'bug' will cause the node to fail with some probability. If the network control or management is such that this message is persistently passed among the network nodes, and if the node failure probability is sufficiently high, large-scale instability can result. Identifying the responsible message type can permit filters to be configured to block poison message propagation, thereby preventing instability. Since message types have distinctive modes of propagation, the node failure pattern can provide valuable information to help identify the CUlprit message type. Through extensive simulations, we show that artificial neural networks are effective in isolating the responsible message type.

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A Framework for Supporting Intelligent Fault and Performance Management for Communication Networks

Cited by 6 publications

References 22 publications

Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions

Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions

Novel Approach for Remote Energy Meter Reading Using Mobile Agents

Using Neural Networks to Identify Control and Management Plane Poison Messages

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