M. Hasan, B. Sugla and R. Viswanathan, "A conceptual framework for network management event correlation and filtering systems," IEEE/IFIP Symposium on Integrated Network Management, 1999, 233-246.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....propagation generates alarm propagation, techniques for alarm correlation are relevant to the third step in our program detecting activation of a generic propagation mechanism. There is a large body of literature on alarm correlation. Some examples include [JW93] JW95] KS95] N95] YK 96] [HSV99] among others. We also note that in the MAGDA Project [FBJ 00] the interaction of network elements in fault propagation is modeled using Petri nets. In this paper, we focus on a single example of a generic propagation mechanism: overload propagation in the control plane caused by excessive route ....

M. Hasan, B. Sugla and R. Viswanathan, "A conceptual framework for network management event correlation and filtering systems," IEEE/IFIP Symposium on Integrated Network Management, 1999, 233-246.

....graph fault models: A causality graph is a directed acyclic graph G c (E, C) whose nodes E correspond to events and whose edges C describe cause effect relationships between events. An edge (e i , e j ) C represents the fact that event e i causes event e j , which is denoted as e i e j [12]. A dependency graph is a directed graph G = O, D) where O is a finite, nonempty set of objects in use and D is a set of edges between these objects. The directed edge (o i , o j ) D denotes the fact that an error or fault in o i may cause an error in o j . Probabilities may be assigned to ....

....as a representation of a communication system in which every object may experience only one type of failure. Otherwise, causality graph has the potential to provide a more detailed view of the system by allowing multiple causality graph nodes per single network object. Causality graph is used in [12] as a framework for developing fault localization algorithms based on a deduction system. The technique assumes AND relationships between errors caused by the same event, i.e. if an event e i happens, then all events e j , such that e i e j , happen. Because of this determinism, when a ....

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M. Hasan, B. Sugla, and R. Viswanathan. A conceptual framework for network management event correlation and filtering systems. In IM'99

.... Support for uncertainty and lost events 13 6 Related work In the past various event correlation techniques were proposed including rule based systems [18, 26] model based reasoning systems [13, 21] model traversing techniques [14, 15] case based systems [17] fault propagation models [9, 16], and the code book approach [27] Rule based systems are composed of rules (productions) of the form conclusion if condition. The condition part is a logical combination of propositions about the current set of received alarms and the system state [18, 26] the conclusion determines the state of ....

....Another group of approaches incorporate an explicit representation of the structure and function of the system being diagnosed. The representation provides information about dependencies between network components [11, 13, 14, 15, 16] or about cause effect relationships between network events [9, 21]. The fault isolation process explores the network model to verify correlation between events. Model based reasoning systems [13, 21] utilize inference engines controlled by a set of correlation rules, which contain model exploration predicates. Modeltraversal techniques recursively search the ....

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M. Hasan, B. Sugla, and R. Viswanathan. A conceptual framework for network management event correlation and filtering systems. In M. Sloman, S. Mazumdar, and E. Lupu, editors, Integrated Network Management VI, pages 233--246, Boston, MA, May 1999.

....task into separate, simpler subtasks. Because of fault propagation, the effects of an abnormal operation of functions or services provided by lower layers may be observed in higher layers. Fault management systems model fault propagation by representing either causal relationships among events [12, 35] or dependencies among system entities [11, 18] In the layered fault model, the definition of entity dependencies is based on real life relationships between layers on a single host and among network nodes communicating within a single protocol layer. The model presented in this paper is based ....

.... Figure 9: Fault localization time with Algorithm 7 Related work In the past, various event correlation techniques were proposed including rule based systems [22, 34] model based reasoning systems [15, 24] model traversing techniques [16] case based systems [21] fault propagation models [12, 18], and the codebook approach [35] Most of the above approaches utilize deterministic reasoning. This paper focuses on non deterministic event correlation which is unavoidable in fault diagnosis related to quality of service degradation particularly in upper protocol layers. Katzela et al. 18] ....

M. Hasan, B. Sugla, and R. Viswanathan. A conceptual framework for network management event correlation and filtering systems. In Sloman et al. [28], pp. 233--246.

....of such a tool for several reasons. First, open source applications for network management have proliferated over the past few years, producing a variety of high quality, specialized tools. 32] Though no general theory of event correlation has been established, though progress has been made. [13] Even without a theoretical basis, the problems are well enough understood to provide partial solutions that provide clear bene ts. Commercial tools are clearly providing their 8 proprietary solutions for event correlation in a growing market without the bene t of complete theoretical solutions. ....

M. Hasan, B. Sugla, and R. Viswanathan. A conceptual framework for network management event correlation and ltering systems. Proceedings of Sixth IFIP/IEEE International Symposium on Integrated Network Management, May 1999.

....and improving the availability and performance of network services. The problems typically addressed in the literature are (1) incomplete knowledge about the existence of causal relationships between network events [13] 2) possibility of incomplete symptom observations or spurious symptoms [9, 25], 3) system adaptability to configuration changes [1] 4) ability to learn event correlation patterns [14] and (5) temporal event correlation [15] Most of these techniques rely on the assumption that the existence of multiple simultaneous faults is negligible. Relationships between network ....

M. Hasan, B. Sugla, and R. Viswanathan. A conceptual framework for network management event correlation and filtering systems. In Sloman et al. [20], pp. 233--246.

....to describe a wide range of fault localization problems. The paper expands on our previous research on applying belief networks techniques to fault localization [5] Section IV) by taking into account comprehensive information about the system behavior. Most previous work on this subject [6] [7] performs fault localization based solely on the information about malfunctioning system components (i.e. negative symptoms) In this paper, we show that positive information, i.e. the lack of any disorder in some system components, may be used to improve the accuracy of fault localization ....

.... A failure of a system entity that occurs in layer L on host H may also affect a system entity in layer L on host H # that communicates with host H (horizontal propagation) Fault management systems model fault propagation by representing either causal relationships among network events [7], 8] or dependencies among communication system entities [1] 6] 9] In the past, researchers focusing on fault localization frequently assumed a deterministic fault propagation model, in 0 7803 7476 2 02 17.00 (c) 2002 IEEE. which one can predict with certainty whether a failure of an ....

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M. Hasan, B. Sugla, and R. Viswanathan, "A conceptual framework for network management event correlation and filtering systems," In Sloman et al. [34], pp. 233--246.

....The reason for the actual need of such root cause analysis is that error conditions or problem reports brought to administrators or management systems, are just descriptions of symptoms. To be able to derive their causes, further knowledge about the dependencies among them is necessary. 8] [9] and [10] explain this subject in detail. Similar dependency models are needed when determining availability requirements on sub services (looking from a top down perspective) respectively for the calculation of service availability from the availability of underlying services (bottom up) as ....

M. Hasan, B. Sugla, and Viswanathan R., "A Conceptual Framework for Network Management Event Correlation and Filtering Systems," In Sloman et al. [22], pp. 233--246.

....The reason for the actual need of such root cause analysis is that error conditions or problem reports brought to administrators or management systems, are just descriptions of symptoms. To be able to derive their causes, further knowledge about the dependencies among them is necessary. 7] [8] and [9] explain this subject in detail. Similar dependency models are needed when determining availability requirements on sub services (looking from a top down perspective) respectively for the calculation of service availability from the availability of underlying services (bottom up) as ....

M. Hasan, B. Sugla, and Viswanathan R., "A Conceptual Framework for Network Management Event Correlation and Filtering Systems," In Sloman et al. [22], pp. 233--246.

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M. Hasan, B. Sugla, and R. Viswanathan, "A Conceptual Framework for Network Management Event Correlation and Filtering Systems", in Proceedings of the Sixth IEEE/IFIP International Conference on Integrated Management (IM'99), May 1999, Boston, Massachusetts.

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