Distributed fault identification in telecommunication networks

Management Technologies for E-Commerce and E-Business Applications

2002

Probabilistic inference was shown effective in non-deterministic diagnosis of end-to-end service failures. To overcome the exponential complexity of the exact inference algorithms in fault propagation models represented by graphs with undirected loops, Pearl's iterative algorithms for polytrees were used as an approximation schema. The approximation made it possible to diagnose end-to-end service failures in network topologies composed of tens of nodes. This paper proposes a distributed algorithm that increases the admissible network size by an order of magnitude. The algorithm divides the computational effort and system knowledge among multiple, hierarchically organized managers. The cooperation among managers is illustrated with examples, and the results of a preliminary performance study are presented. 1

“…Many researchers have recognized the importance of distributed fault localization [1,9,23]. However, few distributed fault localization techniques have actually been proposed.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed Fault Localization in Hierarchically Routed Networks

Management Technologies for E-Commerce and E-Business Applications

2002

“…• Fault localization [4,5,53] (also referred to as fault isolation, alarm/event correlation, and root cause analysis)-a set of observed fault indications is analyzed to find an explanation of the alarms. • Testing [5,53]-a process that, given a number of possible hypotheses, determines the actual faults.…”

Section: Introductionmentioning

confidence: 99%

“…• Testing [5,53]-a process that, given a number of possible hypotheses, determines the actual faults.…”

Section: Introductionmentioning

confidence: 99%

A survey of fault localization techniques in computer networks

Science of Computer Programming

2004

289

213

Fault localization, a central aspect of network fault management, is a process of deducing the exact source of a failure from a set of observed failure indications. It has been a focus of research activity since the advent of modern communication systems, which produced numerous fault localization techniques. However, as communication systems evolved becoming more complex and offering new capabilities, the requirements imposed on fault localization techniques have changed as well. It is fair to say that despite this research effort, fault localization in complex communication systems remains an open research problem. This paper discusses the challenges of fault localization in complex communication systems and presents an overview of solutions proposed in the course of the last ten years, while discussing their advantages and shortcomings. The survey is followed by the presentation of potential directions for future research in this area.

“…The algorithms were shown effective in the diagnosis of end-to-end service failures in networks composed of tens of nodes. This paper introduces a distributed fault-localization technique [4,7], which increases the admissible network size by an order of magnitude by taking advantage of the domain semantics of communication systems. The technique divides the computational effort and system knowledge among hierarchically organized managers.…”

Section: Introductionmentioning

confidence: 99%

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

IEEE Trans. Parallel Distrib. Syst.

2007

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 .