Skip to main content
SpringerLink
Log in

A Policy-based Approach for Reconfiguration Management and Enforcement in Autonomic Communication Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper presents an overview of the policy-based reconfiguration management and enforcement for autonomic communication system platform (Pre-meacs). In contrast to existing management approaches, which require static priori policy configurations, policies are created dynamically. The proposed Pre-meacs framework creates new policies at runtime in response to the changing requirements. A hierarchical policy model is used to refine users and administrators’ high-level goals into low-level objectives. The new approach ensures the success of the reconfiguration through monitoring feedback. The main components of Pre-meacs framework for policy creation, storage, evaluation and enforcement are presented, and the procedures of Pre-meacs in networks reconfiguration management are also demonstrated. Illustrative example demonstrates the performance of the proposed framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. EU IST Project E2R II (End-to-End Reconfigurability - Phase 2), http://www.e2r.motlabs.com/

  2. Vogler, J. et al. (2005). “Equipment Management and Control Architecture”, E2R White paper, from http://e2r.motlabs.com/whitepapers/, July 2005.

  3. Chadha, R. et al. (2004). Policy-based mobile ad hoc network management. Proceedings of the Fifth IEEE Workshop on Policies for Distributed Systems and Networks (POLICY’04), New York, USA, June 2004.

  4. Moore, B., Ellesson, E., Strassner, J., & Westerinen, A. (2001). Policy Core Information Model Version 1 Specification. IETF, RFC3060, February 2001.

  5. Samaan N., Karmouch A. (2005). An automated policy-based management framework for differentiated communication systems. IEEE Journal on Selected Areas in Communications 23, 2236–2247

    Article  Google Scholar 

  6. Bandara, A. K., Lupu, E. C., Moffett, J., & Russo, A. (2004). A Goal-based approach to policy refinement. Proceedings of the Fifth IEEE Workshop on Policies for Distributed Systems and Networks (POLICY’04), New York, USA, June 2004.

  7. Dunlop, N., Indulska, J., & Raymond, K. (2002). Dynamic conflict detection in policy-based management systems. Proceedings of International Enterprise Distributed Object Computing Conference, pp. 15–26, Sept. 2002.

  8. Syukur, E., Loke, S. W., & Stanski, P. (2005). Methods for policy conflict detection and resolution in pervasive computing environments. Policy Management for Web workshop in conjunction with WWW2005 Conference, Chiba, Japan, 10–14 May 2005.

  9. Distributed Management Task Force, “CIM Policy Model, v.2.12,” from http://www.dmtf.org/standards/cim/cim_schema_v212/cimv212Final-Doc.zip, Apr. 20, 2006.

  10. Durham, D., Boyle, J., Cohen, R., Herzog, S., Rajan, R., & Sastry, A. (2000). The COPS (common open policy service) protocol. IETF, RFC 2748, Jan. 2000.

  11. Kaelbling L.P., et al. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 237–285

    Google Scholar 

  12. Nie J., Haykin S. (1999). A Q-learning-based Dynamic Channel Assignment Technique for Mobile Communication Systems. IEEE Transactions on Vehicular Technology 48, 1676–1687

    Article  Google Scholar 

  13. Senouci S.-M., Pujolle G. (2003). Dynamic channel assignment in cellular networks: A reinforcement learning solution. Proceedings of International Conference on Telecommunications 1, 302–309

    Google Scholar 

  14. Lilith N., Dogancay K. (2004). Dynamic channel allocation for mobile cellular traffic using reduced-state reinforcement learning. Proceedings of IEEE Wireless Communications and Networking Conference 4, 2195–2200

    Google Scholar 

  15. Watkins C.J.C.H., Dayan P. (1992). Q-learning. Machine Learning 8, 279–292

    MATH  Google Scholar 

  16. Radunovic B., Le Boudec J.Y. (2004). Rate performance objectives of multihop wireless networks. IEEE Transactions on Mobile Computing 3, 334–349

    Article  Google Scholar 

  17. Hagan M.T., Demuth H.B., Beale M.H. (1996). Neural Network Design. Boston, MA, PWS Publishing

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications (BUPT), P.O. Box 92, No. 10, Xi Tu Cheng Road, Haidian District, Beijing, 100876, China

    Jie Chen, Zhenzhen Zhao & Ping Zhang

  2. China Telecommunication Technology Labs, Beijing, China

    Di Qu

Authors
  1. Jie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenzhen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Di Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, J., Zhao, Z., Qu, D. et al. A Policy-based Approach for Reconfiguration Management and Enforcement in Autonomic Communication Systems. Wireless Pers Commun 45, 145–161 (2008). https://doi.org/10.1007/s11277-007-9405-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-007-9405-x

Keywords

Search

Navigation