Allan J. Finkel

Alarm correlation and fault identification in communication networks

Presents an approach for modeling and solving the problem of fault identification and alarm correlation in large communication networks. A single fault in a large network may result in a large number of alarms, and it is often very difficult to isolate the true cause of the fault. This appears to be one of the most important difficulties in managing faults in todays networks. The problem may become worse in the case of multiple faults. The authors present a general methodology for solving the alarm correlation and fault identification problem. They propose a new alarm structure, propose a general model for representing the network, and give two algorithms which can solve the alarm correlation and fault identification problem in the presence of multiple faults. These algorithms differ in the degree of accuracy achieved in identifying the fault, and in the degree of complexity required for implementation. >

Towards a practical alarm correlation system

A single fault in a telecommunication network frequently results in a number of alarms being reported to the network operator. This multitude of alarms can easily obscure the real cause of the fault. In addition, when multiple faults occur at approximately the same time, it can be difficult to determine how many faults have occurred, thus creating the possibility that some may be missed. A variety of solution approaches have been proposed in the literature, however, practically deployable, commercial solutions remain elusive. The experiences of the Network Fault and Alarm Correlator and Tester (NetFACT) project, carried out at IBM Research and described in this paper, provide some insight as to why this is the case, and what must be done to overcome the barriers encountered. Our observations are based on experimental use of the NetFACT system to process a live, continuous alarm stream from a portion of the Advantis physical backbone network, one of the largest private telecommunications networks in the world.

Distributed fault identification in telecommunication networks

Telecommunications networks are often managed by a large number of management centers, each responsible for a logically autonomous part of the network. This could be a small subnetwork such as an Ethernet, a Token Ring or an FDDI ring, or a large subnetwork comprising many smaller networks. In response to a single fault in a telecommunications network, many network elements may raise alarms, which are typically reported only to the subarea management center that contains the network element raising the alarm. As a result, a particular management center has a partial view of the status of the network. Management Centers must therefore cooperate in order to correctly infer the real cause of the failure. The algorithms proposed in this paper outline the way these management centers could collaborate in correlating alarms and identifying faults.

An explicit solution of the inverse periodic problem for Hill's equation

Let the periodic spectrum of the Hill’s operator

RODM: a control information base

{{ - d^2 } / {dx^2 + p(x)}}

An Alarm Correlation System for Heterogeneous Networks

have n nonzero gaps. We give explicit formulas for the isospectral manifold of operators

Adding rule-based techniques to procedural languages

{{ - d^2 } / {dx^2 + q(x)}}

Model management and the automation of computer system operations

having the same spectrum. This allows us to realize the isospectral manifold explicitly as a torus. What makes this possible is an explicit solution of the flow \[ \left. {\frac{d}{{dt}}q = \frac{d}{{dx}}\frac{\partial }{{\partial q(x)}}\Delta (\lambda ,q)} \right|_{\lambda = \mu _n (q)} \] introduced by McKean and Trubowitz, where

Methods and systems for alarm correlation and fault localization in communication networks

\Delta

Process control for real time systems

is the discriminant and