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 today's 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.>

The problem of minimum cost identification of a finite state machine (FSM) using a trace of its event history is addressed. The motivation is fault identification in communication systems, although other applications are possible as well. The event history used for the identification is partially observed, i.e., it is known to be a member of a regular language. Any string which belongs in this regular language is a possible trace of the FSM's event history. Furthermore, the event history is assumed to be corrupted with deletions, additions, and changes of symbols. The FSM to be estimated is related to a known FSM by performing an unknown number of additions and changes of arcs. An identification algorithm based on a fast algorithm that can correct corrupted data strings generated by a known finite state machine is developed. Examples of the method are provided, including one based on the IEEE 802.2 logical link control protocol.>

A new channel model and channel inversion algorithm are presented for correcting symbol sequences that have been corrupted by an unknown combination of known fault mechanisms. The algorithm is similar to the Viterbi algorithm in that it is suitable for situations in which the uncorrupted data string is generated by a known finite-state process, but it is more versatile in that it can correct a much broader class of errors. Of particular importance is the fact that the algorithm corrects common context-sensitive errors, such as symbol changes, transpositions, mergers, splits, insertions, and deletions, which may be assigned different probabilities depending on the context of preceeding and subsequent symbols. As many communication channels can be modeled in this way, this algorithm is a significant extension over the Viterbi algorithm and previous decoding techniques. The notion of channel rules is introduced to provide a framework for the user to specify the channel operation. The algorithm is given in both an off-line form and a recursive form suitable for sequentially presented data streams. In most applications, the recursive form has computational complexity only a constant times that of the Viterbi algorithm.>

This work provides a framework for approaching the problem of fault detection in communication processes. Communication processes are modeled as finite state machines (FSMs), and the authors' model consists of two FSMs, one observing part of the behavior of the other. The authors prove that specific classes of faults are detectable, and give a procedure for constructing detectors, but the design of the minimum alphabet detector is NP-complete. An example applicable to the 802.2 protocol is provided.>

The problem of minimum-cost correction of a corrupted set of data that has been generated by a known finite state machine (FSM) is examined. The Viterbi algorithm is modified to correct insertions and deletions as well as errors, still using a trellis diagram that has the same number of states as the FSM that generates the uncorrupted data. Two problems are examined. In the first problem the data is given in the traditional form of a string so the novel aspect is that insertions and deletions are now corrected. In the second problem, a unique string need not be given, but a regular language is given, and any string belonging to the regular language is a possible data string. Again, deletion addition, and changes of symbols are corrected. A direct generalization of the Viterbi decoding algorithm is thus proved to be an efficient technique for solving a much wider class of problems.>