Abstract
Management activities are based on the state of distributed system components, relations of these components, and their behaviour. Since management policies are applied across an abstraction of distributed systems, the quality of decisions is dependent on the representation fidelity of the real system state. Obviously, the data collection process updating the abstract representation of real distributed system components has a major impact on the quality of management decisions. Gathering most topical management data improves the quality of management decisions, but requires a high degree of monitoring activity. This is contradictory to the request for low impact management systems, where the amount of system resources used for management purpose should be as small as possible.
In this paper we present a twofold approach to this problem: First a high level management architecture is described where monitoring is performed by distributed agents with generic functionality for filtering and event creation. The distribution of active management agents reduces the amount of management related traffic and avoids a potential bottleneck on a centralized management station. Second an adaptive polling frequency approach is presented which enables the monitoring agents to adapt their polling frequency automatically to different behavioral parameters of managed components. The automatic adaptation reduces the performance impact of the agents significantly while at the same time a high accuracy of management relevant information about critical components is ensured. Implementation aspects of the introduced management architecture in a CORBA environment are also discussed.
Supported by the BMBF, Project No. INF 26
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© 1997 Springer Science+Business Media Dordrecht
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Dini, P., v. Bochmann, G., Koch, T., Krämer, B. (1997). Agent based Management of Distributed Systems with Variable Polling Frequency Policies. In: Lazar, A.A., Saracco, R., Stadler, R. (eds) Integrated Network Management V. IM 1997. IFIP — The International Federation for Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35180-3_41
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DOI: https://doi.org/10.1007/978-0-387-35180-3_41
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