A practical scheme for MPLS fault monitoring and alarm correlation in backbone networks

Yu, Ming; Li, Wenjui; Chung, Li-jin W.

doi:10.1016/j.comnet.2005.11.006

Cited by 7 publications

(4 citation statements)

References 22 publications

(23 reference statements)

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“…Others concentrate on prediction problems for sequential rule mining on several different application domains. Some of these approaches are based on rule correlation, fuzzy logic, coding correlation, Bayesian networks, and artificial neural networks …”

Section: Related Workmentioning

confidence: 99%

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ALACA: A platform for dynamic alarm collection and alert notification in network management systems

et al. 2017

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SummaryMobile network operators run Operations Support Systems that produce vast amounts of alarm events. These events can have different significance levels and domains and also can trigger other ones. Network operators face the challenge to identify the significance and root causes of these system problems in real time and to keep the number of remedial actions at an optimal level, so that customer satisfaction rates can be guaranteed at a reasonable cost. In this paper, we propose a scalable streaming alarm management system, referred to as Alarm Collector and Analyzer, that includes complex event processing and root cause analysis. We describe a rule mining and root cause analysis solution for alarm event correlation and analyses.The solution includes a dynamic index for matching active alarms, an algorithm for generating candidate alarm rules, a sliding window-based approach to save system resources, and a graph-based solution to identify root causes. Alarm Collector and Analyzer is used in the network operation center of a major mobile telecom provider. It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using the results of root cause analysis. We present experimental results that provide insights on performance of real-time alarm data analytics systems.

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Section: Related Workmentioning

confidence: 99%

“…Some of these approaches are based on rule correlation, fuzzy logic, coding correlation, Bayesian networks, and artificial neural networks. 8,[16][17][18][19] Each of the aforementioned approaches, ranging from generating an alarm correlation engine to alarm modeling and validation, have their own advantages and disadvantages.…”

Section: Related Workmentioning

confidence: 99%

ALACA: A platform for dynamic alarm collection and alert notification in network management systems

et al. 2017

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“…However, it can be very difficult to develop good model for complex networks. Other approaches probabilistic-based [8,9,10,11], and codebookbased [12,13,14] have also been investigated. These algorithms have common advantages and shortcomings over each other.…”

Section: Introductionmentioning

confidence: 99%

A new probing scheme for fault detection and identification

Mohamed

Basir

2009

2009 IEEE International Conference on Electro/Information Technology

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Probing technology has been used as a fault detection and identification method in computer networks and successful applications have been reported. One of the most appealing features of probing-based schemes is that it is an active approach. A set of probes can be sent on a periodic basis. If a network failure is detected, the outcomes of these probes are further analyzed to determine the root cause of the problem. However, the availability of a large set of such probes may in fact place a huge burden on management systems in terms of extra management traffic and storage space. Hence, the need of minimizing such a probing set has become highly desirable. In this work, we propose a preplanned probe selection scheme, in which a small set of probes are chosen such that it maintains the diagnostic power of the original set. The new approach is based on the constraint satisfaction problem paradigm and its powerful search techniques are exploited. The efficiency of the new algorithm has been demonstrated by the results reported.

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“…Then, several methods have been proposed in the literature in order to design more powerful fault diagnosis solutions. These proposals are based on graph theory and artificial intelligence tools, such as: Marckov Chains [11], Causal Graphs [7], Bayesian Networks [13], [8], Codebook [14] and Rule-Based Reasoning [10]. They are based on the construction, a priori, of a dependency model between all the metrics characterizing the equipments of an operator network [7], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Scalable and fast root cause analysis using inter cluster inference

Bennacer¹,

Ciavaglia²,

Ghamri-Doudane³

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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The capability to diagnose the root cause of an observed problem precisely and quickly is a desirable feature for large communication networks. However, the design of a technique that is at the same time fast, scalable and accurate is a challenging task. In this paper, we propose a novel method based on inter-cluster inference to overcome the usual limits of fault diagnosis techniques. The approach is based on two important concepts: a cluster decomposition of the dependency graph in order to ensure scalability, and the introduction of duplicated nodes aiming at preserving the end-to-end network view. The evaluation of the proposed approach has demonstrated a significant reduction in the complexity and the computation time of the root cause analysis, since it is based on a set of small-scale dependency graphs.

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A practical scheme for MPLS fault monitoring and alarm correlation in backbone networks

Cited by 7 publications

References 22 publications

ALACA: A platform for dynamic alarm collection and alert notification in network management systems

ALACA: A platform for dynamic alarm collection and alert notification in network management systems

A new probing scheme for fault detection and identification

Scalable and fast root cause analysis using inter cluster inference

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