PROBABILISTIC FAULT MANAGEMENT IN DISTRIBUTED SYSTEMS DISSERTATION zur Erlangung des akademischen Grades DOKTOR - INGENIEUR der Fakultät für Mathematik und Informatik der FernUniversität in Hagen von

With the growth of distributed systems in size, heterogeneity, pervasiveness and complexity of applications and network services, the effective management of distributed systems becomes more important and more difficult. Managers have to live with unstable, uncertain and incomplete management information. Individual hardware defects or software errors or combinations of such defects and errors in different system components may cause the degradation of services of other (remote) components in the network or even their complete failure due to functional dependencies between managed objects. Meanwhile, dynamic changes are unavoidable in complex distributed systems. This brings more challenges in fault management of distributed systems. Hence an effective mechanism for distributed fault detection is needed to support rapid decision making in the management of distributed systems and allow for partial automation of fault correction. Currently, available techniques in fault detection and commercial management software are unable to provide effective solutions for fault detection in face of uncertain and incomplete management information and dynamic changes in distributed systems. In this book Bayesian networks are applied to model the dependencies among managed objects and to provide efficient methods for locating the root causes of failures arising from inaccurate management information. A Strongest Dependence Route (SDR) algorithm for backward inference is offered based on Bayesian networks. The SDR algorithm will allow system managers to trace the strongest dependency route from effects and to identify the sequence of possible causes, so that the most probable causes can be investigated first. Unavoidable dynamic changes caused by system degeneration or improvement are investigated by importing a temporal factor to standard Bayesian networks, so that the time factor related changes can be integrated into the Bayesian networks model. Dynamic Bayesian Networks (DBNs) are applied in distributed systems management in order to address time factors and to model the dynamic changes of managed entities and the dependencies between them. Furthermore, prediction capabilities are investigated by means of the inference techniques when imprecise and dynamic management information occurs in distributed systems. Implementation details of the simulation in Bayesian Networks and Dynamic Bayesian Networks are provided for measurement of backward inference and prediction strategies in distributed fault management. Application issues of probabilistic fault management are investigated and a software architecture is designed to demonstrate how the probabilistic inference and prediction strategies can be brought into practice.