Model-based Expert systems

Second generation expert systems attempted to solve the limitations of rule-based sys-tems by using stronger methods, such as model-based reasoning. Model-based Syssys-tems (MBS) are knowledge-based systems which reason about a system from an explicit representation of its structure and functional behaviour. For the telecommunication networks management, the structural representation involves the description of net-work elements (NEs) and of the netnet-work topology (see Figure 4.3 for an example).

The representation of functional behavior describes the processes of event propagation and event correlation (Jakobson and Weissman, 1995). As the real plants tend to be complex, so are the models used in this technique (Penido et al., 1999). Model-based Systems (MBS) have been mainly used in industry for the automation of engineer-ing tasks such as simulation, design, monitorengineer-ing and diagnosis (Isermann, 1997, 2005;

Angeli, 2010). However the same principles can be extended into real-time fault man-agement in a telecommunications network, where the network structure (NE types and topology, containment constraints) and behavior (dynamic process of alarm correlation) are modelled (Gardner and Harle, 1996).

The complexity on building a diagnostic system for Network Management resides on the following facts: a regular network can have a variety of types of hardware com-ponents and a large number of them; there are different types of software comcom-ponents (protocols, operating systems, services, applications); the equipment and connections may be changed, and yet some network protocols are based on dynamic configuration.

The construction of network models to build management tools involves the identifica-tion of all necessary knowledge and its organizaidentifica-tion in such way that the management task can be automatically performed as an activity of exchanging behavioral, structural and control information (Barros and Lemos, 1999). Several researchers proposed the application of model-based reasoning techniques to solve diagnosis problems.

Kehl et al. (Kehl et al., 1992) presented a generic maintenance system (GMS) for the telecommunication networks (e.g. broadband ISDN networks). The knowledge base of this GMS is divided into two parts: a functional and a physical model, with the functional model being he most important part of it (see Figure 4.3). The functional model consists of structural information and behavioural knowledge. It is built out

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Figure 4.3: Structure of the knowledge sources

of functional entities (FEs) corresponding to specific functionalities of the modelled telecommunication network. There is a mapping between the FEs of the functional model and the elements of the physical model. A functional entity consists out of internal attributes and ports. Such ports connect FEs to each other. The FEs to-gether with these port-to-port connections are building the structural model. FEs have been organized on four different levels of granularity which are connected via a has-subfunctions/is-subfunction-of relation. The reasoning is done on the level which has the finest granularity. Furthermore, a classification hierarchy has been set up over the functional entities: while the upper part of the hierarchy reflects generic telecom classi-fications, the lower part introduces network-specific classes. The behavioural knowledge is a description of how the network behaves in terms of functional entities and their ports. This generic maintenance system presents the typical characteristics of a model-based system, using an explicit model of the telecommunication network as well as a model of its behavior. It therefore exhibits the advantages of model-based systems including easy maintenance, reconfiguration and extension.

Frohlich et al. (Frohlich et al., 1997) introduced a model-based solution to the problem of alarm correlation in cellular phone networks. They proposed a model called the system description (SD) that consists of a set of axioms characterizing the behavior of system components of certain types while the topology is modeled separately by a set of facts. A set of predicate logic formulas is used to specify the alarm messages as well as their type. And another set of formulas describes the alarm behavior as well as the alarm propagation. The strengths of this alarm correlation system rely on the fact that is is based on: a) a small and maintainable model of the system called the system description (SD) that separates structural or topological knowledge from behavioral knowledge and thus makes changes of the network topology easy; b) a propagation model which allows to correctly diagnose unforeseen errors as well as multiple faults;

c) failure probability estimates, which lead to correct diagnoses even on noisy data, where alarm messages have been lost or suppressed.

For building network models, Barros et al. (Barros and Lemos, 1999) specified a Communication Fault Diagnostic System to support the network administrator task.

This system is based on the construction of models that represent the network in its

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multiple aspects, such as: configuration model, performance model, fault-states causal model, equipment models, and others. In particular, the Configuration Model con-struction is facilitated by a system, called Network Discovery System, able to collect and gather information about the configuration levels. Since the network configuration is the result of human activity, errors can be embedded in the discovered configuration model. For that reason they have also developed another system called the Config-uration Diagnosis System, which can detect a set of configConfig-uration errors during the acquisition and construction of the network models.

Dupuy et al. (Dupuy et al., 1991) described a generic network management system (Netmate) to address the management of large, heterogeneous and complex networks.

They proposed a model for network management information which emphasizes the definition of generic network objects and relationships. Netmate Structure for Manage-ment Information (SMI) defines four object classes and five relationships: 1)is-in-layer represents the fact that a node, a link, or a group may belong to a single layer; 2) is-connected-to represents the notion that a node (link) may connect to more than one link (node) in the same layer; 3)is-member-of represents the collection of groups, nodes and links into a group; 4)is-part-of represents the collection of (sub)nodes into a node, or of (sub)links into a link, or of (sub)layers into a layer; 5)is-implemented-in-terms-of represents the notion that elements in one layer use the services of elements in other layers, and therefore, are functionally dependent on the well-being of elements in the other layers. These object classes and relationships are able to support various man-agement operations in a number of manman-agement scenarios. This object-oriented model is extensible and well suited to accommodate current as well as future heterogeneous networks and protocols.

Miyazawa et al. (Miyazawa and Nishimura, 2011) addressed the issue of the in-creased time required to identify the root cause of a failure resulting from the inin-creased number and type of alarms caused by network or service failures. They proposed a root cause analysis (RCA) mechanism which classifies types of alarms based on a hier-archical alarm identification type of failure (resource, performance or service failure), but also execute a root cause analysis based on alarm types.

Discussion The model-based approach is easy to deploy and modify and is appropri-ate for a large-scale network if the network resource information is available (Miyazawa and Nishimura, 2011). Model based systems have the potential to solve novel prob-lems and their performance tends to degrade gracefully when confronted with probprob-lems outside their expertise. They also lend themselves well to providing explanations for their decisions and conclusions since each stage in an analysis can be followed and un-derstood. MBSs can be constructed in a modular fashion with different aspects of a physical system being modelled separately, if required, and hence they cater well for expandable, upgradable systems. Also, a model of a system may be used for purposes other than alarm correlation and take different ‘views’ of the contained knowledge ac-cording to the needs of the user or task. However, the application of a model based approach to many systems is often hindered by problem solving complexity. This can be solved, in part, by using more efficient problem-solving algorithms and adopting

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the correct system model or sub-models for the the tasks in hand. Selecting the cor-rect level of abstraction for the model is important and the relevant functional, causal, compositional, and structural semantics of the working of the device should be cap-tured (Gardner and Harle, 1996). While these expert systems seem preferable to first generation systems, they have not seen the same level of commercial success. In the field of telecommunications, this is because it is often too difficult to specify a behavioural or functional model at a sufficiently high level to make the model practical and yet have it to be useful (Weiss et al., 1998a).