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Decentralized Planning for Self-Adaptation in Multi-cloud Environment

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Advances in Service-Oriented and Cloud Computing (ESOCC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 508))

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Abstract

The runtime management of Internet of Things (IoT) oriented applications deployed in multi-clouds is a complex issue due to the highly heterogeneous and dynamic execution environment. To effectively cope with such an environment, the cross-layer and multi-cloud effects should be taken into account and a decentralized self-adaptation is a promising solution to maintain and evolve the applications for quality assurance. An important issue to be tackled towards realizing this solution is the uncertainty effect of the adaptation, which may cause negative impact to the other layers or even clouds. In this paper, we tackle such an issue from the planning perspective, since an inappropriate planning strategy can fail the adaptation outcome. Therefore, we present an architectural model for decentralized self-adaptation to support the cross-layer and multi-cloud environment. We also propose a planning model and method to enable the decentralized decision making. The planning is formulated as a Reinforcement Learning problem and solved using the Q-learning algorithm. Through simulation experiments, we conduct a study to assess the effectiveness and sensitivity of the proposed planning approach. The results show that our approach can potentially reduce the negative impact on the cross-layer and multi-cloud environment.

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References

  1. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of 1st Workshop on Mobile Cloud Computing, MCC 2012, pp. 13–16 (2012)

    Google Scholar 

  2. Boyle, D., Yates, D., Yeatman, E.: Urban sensor data streams: London 2013. IEEE Internet Comput. 17(6), 12–20 (2013)

    Article  Google Scholar 

  3. Celino, I., Kotoulas, S.: Smart cities [guest editors’ introduction]. IEEE Internet Comput. 17(6), 8–11 (2013)

    Article  Google Scholar 

  4. Chan, K., Bishop, J., Steyn, J., Baresi, L., Guinea, S.: A fault taxonomy for web service composition. In: Di Nitto, Elisabetta, Ripeanu, Matei (eds.) ICSOC 2007. LNCS, vol. 4907, pp. 363–375. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. De Oliveira, F., Ledoux, T., Sharrock, R.: A framework for the coordination of multiple autonomic managers in cloud environments. In: Proceedings of 7th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2013, pp. 179–188, Sep 2013

    Google Scholar 

  6. Dowling, J., Cahill, V.: Self-managed decentralised systems using k-components and collaborative reinforcement learning. In: Proceedings of 1st ACM SIGSOFT Workshop on Self-managed Systems, WOSS 2004, pp. 39–43 (2004)

    Google Scholar 

  7. Elkhodary, A., Esfahani, N., Malek, S.: FUSION: a framework for engineering self-tuning self-adaptive software systems. In: Proceedings of 18th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2010, pp. 7–16 (2010)

    Google Scholar 

  8. Fazio, M., Celesti, A., Villari, M.: Design of a message-oriented middleware for cooperating clouds. In: Canal, C., Villari, M. (eds.) ESOCC 2013. CCIS, vol. 393, pp. 25–36. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  9. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  10. Ismail, A., Cardellini, V.: Towards self-adaptation planning for complex service-based systems. In: Lomuscio, A.R., Nepal, S., Patrizi, F., Benatallah, B., Brandić, I. (eds.) ICSOC 2013. LNCS, vol. 8377, pp. 432–444. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  11. Issarny, V., Georgantas, N., Hachem, S., Zarras, A., Vassiliadist, P., Autili, M., Gerosa, M., Hamida, A.: Service-oriented middleware for the future internet: state of the art and research directions. J. Internet Serv. Appl. 2(1), 23–45 (2011)

    Article  Google Scholar 

  12. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Comput. 36(1), 41–50 (2003)

    Article  Google Scholar 

  13. Kim, D., Park, S.: Reinforcement learning-based dynamic adaptation planning method for architecture-based self-managed software. In: Proceedings of ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2009, pp. 76–85, May 2009

    Google Scholar 

  14. Kostakos, V., Ojala, T., Juntunen, T.: Traffic in the smart city: exploring city-wide sensing for traffic control center augmentation. IEEE Internet Comput. 17(6), 22–29 (2013)

    Article  Google Scholar 

  15. Nallur, V., Bahsoon, R.: A decentralized self-adaptation mechanism for service-based applications in the cloud. IEEE Trans. Softw. Eng. 39(5), 591–612 (2013)

    Article  Google Scholar 

  16. Panerati, J., Sironi, F., Carminati, M., Maggio, M., Beltrame, G., Gmytrasiewicz, P., Sciuto, D., Santambrogio, M.: On self-adaptive resource allocation through reinforcement learning. In: Proceedings of 2013 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2013, pp. 23–30, Jun 2013

    Google Scholar 

  17. Panerati, J., Maggio, M., Carminati, M., Sironi, F., Triverio, M., Santambrogio, M.D.: Coordination of independent loops in self-adaptive systems. ACM Trans. Reconfigurable Technol. Syst. 7(2), 12:1–12:16 (2014)

    Article  Google Scholar 

  18. Sterritt, R., Parashar, M., Tianfield, H., Unland, R.: A concise introduction to autonomic computing. Adv. Eng. Inform. 19(3), 181–187 (2005)

    Article  Google Scholar 

  19. Sykes, D., Corapi, D., Magee, J., Kramer, J., Russo, A., Inoue, K.: Learning revised models for planning in adaptive systems. In: Proceedings of 2013 International Conference on Software Engineering, ICSE 2013, pp. 63–71 (2013)

    Google Scholar 

  20. Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)

    MATH  Google Scholar 

  21. Weyns, D., et al.: On patterns for decentralized control in self-adaptive systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 7475, pp. 76–107. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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Acknowledgements

This work is supported by the Fundamental Research Grant Scheme (600-RMI/FRGS 5/3 (164/2013)) funded by the Ministry of Higher Education Malaysia (MOHE) and Universiti Teknologi MARA (UiTM), Malaysia.

V. Cardellini also acknowledges the support of the European ICT COST Action IC1304 Autonomous Control for a Reliable Internet of Services (ACROSS).

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Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

    Azlan Ismail

  2. Department of Civil Engineering and Computer Science Engineering, University of Roma Tor Vergata, Roma, Italy

    Valeria Cardellini

Authors
  1. Azlan Ismail
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  2. Valeria Cardellini
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Correspondence to Azlan Ismail .

Editor information

Editors and Affiliations

  1. University of Cádiz, Puerto Real, Spain

    Guadalupe Ortiz

  2. The University of Manchester, Manchester, United Kingdom

    Cuong Tran

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Ismail, A., Cardellini, V. (2015). Decentralized Planning for Self-Adaptation in Multi-cloud Environment. In: Ortiz, G., Tran, C. (eds) Advances in Service-Oriented and Cloud Computing. ESOCC 2014. Communications in Computer and Information Science, vol 508. Springer, Cham. https://doi.org/10.1007/978-3-319-14886-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-14886-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14885-4

  • Online ISBN: 978-3-319-14886-1

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