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Context Monitoring Optimization in Autonomic Networks

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Abstract

The recent advances in network management systems suggest the adoption of autonomic mechanisms in order to minimize the need for human intervention while handling complex heterogeneous networks. Data acquisition performed by monitoring processes is an essential part of autonomic mechanisms. The rate of sampling is a crucial factor since it is related to (1) the successful/unsuccessful detection of events, (2) the processing power needed to perform the sampling and (3) the energy that a node consumes during such actions. In order to address these issues we designed a simple and efficient mechanism that dynamically adapts the sampling rate of the context monitoring procedure. The merits of the mechanism are quantified by means of an analytical model as well as through extensive simulations that validated the theoretic outcomes. Finally, we experimentally assessed the effectiveness and efficiency of our approach through two real-world experiments. Overall results showcase that our mechanism achieves high detection rates while in parallel minimizes significantly the number of monitoring loops thus, emerges as a viable approach for context monitoring optimization in autonomic networks.

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Notes

  1. The algorithm is generic in nature, thus the same procedure can be used for monitoring events which require minimum processing time (e.g. movement) or a significant amount (interference identification in IEEE 802.11 networks).

  2. A slot can have a duration of x milliseconds depending on the context of implementation. In order to simplify the presentation, we purposely omit time duration, however the concept of the time-slotted model inherently incorporates it.

  3. Self-evolving cognitive and autonomic networking Laboratory: http://scan.di.uoa.gr.

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Acknowledgments

This work has been performed in the framework of the ICT-257542 CONSERN project funded by the European Union.

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Authors and Affiliations

  1. Intracom Telecom S.A., 19th km Markopoulo Ave., 19002, Paiania, Athens, Greece

    Panagis Magdalinos

  2. Riesstraße 25, 80992, München, Germany

    Alexandros Kaloxylos

  3. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece

    Nancy Alonistioti

Authors
  1. Panagis Magdalinos
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  2. Alexandros Kaloxylos
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  3. Nancy Alonistioti
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Correspondence to Panagis Magdalinos.

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Magdalinos, P., Kaloxylos, A. & Alonistioti, N. Context Monitoring Optimization in Autonomic Networks. Wireless Pers Commun 81, 279–302 (2015). https://doi.org/10.1007/s11277-014-2128-x

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