Model-Based Probabilistic Reasoning for Self-Diagnosis of Telecommunication Networks: Application to a GPON-FTTH Access Network

Abstract

Carrying out self-diagnosis of telecommunication networks requires an understanding of the phenomenon of fault propagation on these networks. This understanding makes it possible to acquire relevant knowledge in order to automatically solve the problem of reverse fault propagation. Two main types of methods can be used to understand fault propagation in order to guess or approximate as much as possible the root causes of observed alarms. Expert systems formulate laws or rules that best describe the phenomenon. Artificial intelligence methods consider that a phenomenon is understood if it can be reproduced by modeling. We propose in this paper, a generic probabilistic modeling method which facilitates fault propagation modeling on large-scale telecommunication networks. A Bayesian network (BN) model of fault propagation on GPON-FTTH (Gigabit-capable Passive Optical Network-Fiber To The Home) access network is designed according to the generic model. GPON-FTTH network skills are used to build structure and approximatively determine parameters of the BN model so-called expert BN model of the GPON-FTTH network. This BN model is confronted with reality by carrying out self-diagnosis of real malfunctions encountered on a commercial GPON-FTTH network. Obtained self-diagnosis results are very satisfying and we show how and why these results of the probabilistic model are more consistent with 1 the behaviour of the GPON-FTTH network, and more reasonable on a representative sample of diagnosis cases, than a rule-based expert system. With the main goal to improve diagnostic performances of the BN model, we study and apply EM (Expectation Maximization) algorithm in order to automatically fine-tune parameters of the BN model from real data generated by a commercial GPON-FTTH network. We show that the new BN model with optimized parameters reasonably improves self-diagnosis previously carried out by the expert Bayesian network model of the GPON-FTTH access network.

Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks

“…with a Bayesian network formalism [9], it gets the ability to deal with uncertainty resulting from non-deterministic fault propagation. It also becomes robust to missing data [7]. This is particularly interesting in network management situations, for which collected monitoring data is often incomplete and depends on the specific network conditions which have led to alarms.…”

“…Table I. gives a confusion matrix crossing diagnosis conclusions obtained with both tools on the 10611 cases [7]. The rows of the table give the numbers of occurrences of diagnosed root causes obtained from the rule-based expert system, whereas the columns give results from the first PANDA implementation.…”

“…In those cases, in spite of one missing optical power measurement, PANDA is capable to derive a diagnosis decision which is fully compliant with the conclusion that would be drawn by a human expert. This capability is allowed by a global analysis of the PON which is performed by the Bayesian inference engine [7].…”

“…This second PANDA model, tuned by machine learning, has been assessed on another dataset (test dataset) of 5490 diagnosis cases and compared to the initial PANDA model without machine learning. Table II. gives the confusion matrix crossing diagnosis conclusions obtained with both PANDA versions on the 5490 cases of the test dataset [7]. The tuning of the PANDA model through machine learning changes the diagnosis decisions in 3% of the cases, leading to more consistency in some cases, and more clear-cut decisions in other cases.…”

“…This paper describes principles and outcomes of a modular approach to fault diagnosis in optical access networks, benefiting from both probabilistic modeling and machine learning. All implementation details of the probabilistic model and of the machine learning algorithm used are given in [5][6] [7]. This paper provides a synthesis of main principles, outcomes and insights of this work.…”