Beacon: A Hierarchical Network Topology Monitoring System Based on IP Multicast

Novaes, Marcos

doi:10.1007/3-540-44460-2_15

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the discussion of techniques that could be used to build a fault propagation model is beyond the scope of this paper. However, there are a number of publications that focus on this issue while investigating its various aspects [4], [26], [35], [39]. In Section V-A, we cite some of the techniques that can be used to build a model for end-to-end service failure diagnosis, which is the subject of our experimental study.…”

Section: Belief Network As Fault Propagation Modelmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Fault Localization in Communication Systems Using Belief Networks

Steinder

Sethi

2004

IEEE/ACM Trans. Networking

152

100

View full text Add to dashboard Cite

Abstract-We apply Bayesian reasoning techniques to perform fault localization in complex communication systems while using dynamic, ambiguous, uncertain, or incorrect information about the system structure and state. We introduce adaptations of two Bayesian reasoning techniques for polytrees, iterative belief updating, and iterative most probable explanation. We show that these approximate schemes can be applied to belief networks of arbitrary shape and overcome the inherent exponential complexity associated with exact Bayesian reasoning. We show through simulation that our approximate schemes are almost optimally accurate, can identify multiple simultaneous faults in an event driven manner, and incorporate both positive and negative information into the reasoning process. We show that fault localization through iterative belief updating is resilient to noise in the observed symptoms and prove that Bayesian reasoning can now be used in practice to provide effective fault localization.Index Terms-Fault localization, probabilistic inference, root cause diagnosis.

show abstract

Section: Belief Network As Fault Propagation Modelmentioning

confidence: 99%

“…In the network layer of the Internet, current routes may be calculated from ipRoutingTable of TCP/IP MIB-II [33]. Besides, a plethora of other, more advanced techniques has been proposed by the research community for different types of networks, e.g., [4], [18], [32], [35], and [43].…”

Section: A End-to-end Service Modelmentioning

confidence: 99%

Probabilistic Fault Localization in Communication Systems Using Belief Networks

Steinder

Sethi

2004

IEEE/ACM Trans. Networking

152

100

View full text Add to dashboard Cite

show abstract

A 3-layered self-reconfigurable generic model for self-diagnosis of telecommunication networks

Tembo¹,

Courant²,

Vaton³

2015

2015 SAI Intelligent Systems Conference (IntelliSys)

View full text Add to dashboard Cite

International audienceThe dynamic and distributed nature of telecommunication networks makes complex the design of model-based approaches for network fault diagnosis. Most model-based approaches assume the prior existence of the model which is reduced to a static image of the network. Such models become rapidly obsolete when the network changes. We propose in this paper a 3-layered self-reconfigurable generic model of fault diagnosis in telecommunication networks. The Layer 1 of the model is an undirected graph which models the network topology. Network behavior, also called fault propagation, is modeled in Layer 2 using a set of directed acyclic graphs interconnected via the Layer 1. We handle uncertainties of fault propagation by quantifying strengths of dependencies between Layer 2 nodes with conditional probability distributions estimated from network generated data. Layer 3 is the junction tree representation of the loopy obtained Layer 2 Bayesian networks. The junction tree is the diagnosis computational layer since exact inference algorithms fail on loopy bayesian networks. This generic model embeds intelligent self-reconfiguration capabilities in order to track some changes in network topology and network behavior. These self-reconfiguration capabilities are highlighted through some example scenarios that we describe. We apply this 3-layered generic model to carry out active self-diagnosis of the GPON-FTTH access network.We present and analyze some experimental diagnosis results carried out by running a Python implementation of the generic model

show abstract

Multidomain Diagnosis of End-to-End Service Failures in Hierarchically Routed Networks

Steinder

Sethi

2007

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Abstract. This paper investigates an approach to improving the scalability and feasibility of probabilistic fault localization in communication systems by exploiting the domain semantics of computer networks. The proposed technique divides the computational effort and system knowledge among multiple, hierarchically organized managers. Each manager performs fault localization in the domain it manages and requires only the knowledge of its own domain. Since failures propagate among domains, domain managers cooperate with each other to find a consensus explanation of the observed disorder. We show through simulation that the proposed approach increases the effectiveness of probabilistic diagnosis and makes it feasible in networks of considerable size 1 .

show abstract

Beacon: A Hierarchical Network Topology Monitoring System Based on IP Multicast

Cited by 3 publications

References 12 publications

Probabilistic Fault Localization in Communication Systems Using Belief Networks

Probabilistic Fault Localization in Communication Systems Using Belief Networks

A 3-layered self-reconfigurable generic model for self-diagnosis of telecommunication networks

Multidomain Diagnosis of End-to-End Service Failures in Hierarchically Routed Networks

Contact Info

Product

Resources

About