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Self-Configuration of Network Services with Biologically Inspired Learning and Adaptation

  • Special Issue Autonomic
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Journal of Network and Systems Management Aims and scope Submit manuscript

This paper proposes a self-organizing scheme based on ant metaheuristics to optimize the operation of multiple classes of managed elements on an Operations Support Systems (OSSs) for mobile pervasive communications. Ant metaheuristics are characterized by learning and adaptation capabilities against dynamic environment changes and uncertainties. As an important division of swarm agent intelligence, it distinguishes itself from centralized management schemes due to its features of robustness and scalability. We have successfully applied ant metaheuristics to the network service configuration process, which is simply redefined as: the managed elements represented as graphic nodes, and ants traverse by selecting nodes with the minimum cost constraints until the eligible network elements are located along near-optimal paths—the located elements are those needed for the configuration or activation of a particular product and service. Although the configuration process is non-transparent to end users, the negotiated SLAs between users and providers affect the overall process. This proposed self-organized learning and adaptation scheme using Ant Colony Optimization (ACO) is evaluated by simulation in Java. A performance comparison is also made with a class of Genetic Algorithm known as PBIL. Finally, the simulation results show the scalability and robustness capability of autonomous ant-like agents able to adapt to dynamic networks.

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ACKNOWLEDGEMENT

The project of autonomic network operational management for the service discovery, selection, configuration, service activation and assurance requests for complex NGN applications is one of the key issues currently explored in Teleholonic R&D group (TSRG) at the University of Technology, Sydney. The first author would like to take this opportunity to thank Australian Government for providing financial support during his research.

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Authors and Affiliations

  1. Faculty of Engineering, University of Technology Sydney, Broadway, NSW 2007, Australia

    Frank Chiang, Robin Braun & Johnson I. Agbinya

  2. Faculty of Engineering, University of Technology Sydney, Broadway, NSW 2007, Australia

    Frank Chiang

Authors
  1. Frank Chiang
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  2. Robin Braun
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  3. Johnson I. Agbinya
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Corresponding author

Correspondence to Frank Chiang.

Additional information

Frank Chiang is a doctoral researcher in the faculty of engineering at University of Technology, Sydney (UTS), Australia. He received M.Sc. and B.Sc. degrees in Telecommunications and System Engineering in 1999 and 1997, respectively. His current research interests include Autonomic Communication Networks, Bio-inspired algorithms and metaheursitics for combinatorial optimization problems, Intelligent and mobile agents, Network Protocols and Mesh networks.

Robin Braun is Director of the UTS Institute for Information and Communications Technology. He obtained the Ph.D. and M.Sc. from the University of Cape Town in 1982 and 1986, respectively, and the BSc(Hons) from Brighton University, UK, in 1980. He has active research interests in the areas of Autonomic Network Management, Teletraffic Engineering, QoS, Network Protocols and Mesh Networks.

Johnson I. Agbinya received his Ph.D. in Electronic Engineering at La Trobe University in 1994. He is currently a Faculty of Engineering member at the University of Technology Sydney, an Adjunct Professor of Computer Science in the University of the Western Cape (UWC) and the Executive Editor of African Journal of Information and Communication Technology. His research interests are in wireless communications, sensor networks, digital identity management systems, networks on mobile platforms and in uncovered areas.

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Chiang, F., Braun, R. & Agbinya, J.I. Self-Configuration of Network Services with Biologically Inspired Learning and Adaptation. J Netw Syst Manage 15, 87–116 (2007). https://doi.org/10.1007/s10922-006-9056-3

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  • DOI: https://doi.org/10.1007/s10922-006-9056-3

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