Resource Optimisation in IoT Cloud Systems by Using Matchmaking and Self-management Principles

  • Martin Serrano
  • Danh Le-Phuoc
  • Maciej Zaremba
  • Alex Galis
  • Sami Bhiri
  • Manfred Hauswirth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7858)


IoT Cloud systems provide scalable capacity and dynamic behaviour control of virtual infrastructures for running applications, services and processes. Key aspects in this type of complex systems are the resource optimisation and the performance of dynamic management based on distributed user data metrics and/or IoT application data demands and/or resource utilisation metrics. In this paper we particularly focus on Cloud management perspective – integrating IoT Cloud service data management - based on annotated data of monitored Cloud performance and user profiles (matchmaking) and enabling management systems to use shared infrastructures and resources to enable efficient deployment of IoT services and applications. We illustrate a Cloud service management approach based on matchmaking operations and self-management principles which enable improved distribution and management of IoT services across different Cloud vendors and use the results from the analysis as mechanism to control applications and services deployment in Cloud systems. For our IoT Cloud data management solution we utilize performance metrics expressed with linked data in order to integrate monitored performance data and end user profile information (via linked data relations).


Internet-of-Things Service Platforms Management Linked Data Cloud Computing Systems Elasticity Self-Management Autonomic Management Cloud Monitoring Interoperability Virtual Infrastructures 


  1. 1.
    The Economics of the Cloud, (online access Wednesday, January 05, 2011)
  2. 2.
    The ‘InterCloud’ and the Future of Computing, an interview: Vint Cerf at, the Churchill Club, SRI International Building, Menlo Park, CA (January 7, 2010), (January 2011)
  3. 3.
    Rochwerger, B., Caceres, J., Montero, R.S., Breitgand, D., Elmroth, E., Galis, A., Levy, E., Llorente, I.M., Nagin, K., Wolfsthal, Y., Elmroth, E., Caceres, J., Ben-Yehuda, M., Emmerich, W., Galan, F.: The RESERVOIR Model and Architecture for Open Federated Cloud Computing. IBM Journal of Research and Development 53(4) (2009)Google Scholar
  4. 4.
    Serrano, J.M.: Applied Ontology Engineering in Cloud Services, Networks and Management Systems, 222 pages. Springer Publishers, Hardcover (2012) (to be released on March 2012), ISBN-10:1461422353, ISBN-13:978-1461422358Google Scholar
  5. 5.
    Amazon Web Services,
  6. 6.
    Dai, Y., Xiang, Y., Zhang, G.: Self-healing and Hybrid Diagnosis in Cloud Computing. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing. LNCS, vol. 5931, pp. 45–56. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Zaremba, M., Vitvar, T., Bhiri, S., Hauswirth, M.: Service Offer Discovery Using Genetic Algorithms. In: IEEE European Conference on Web Services, ECOWS (2011)Google Scholar
  8. 8.
    Chapman, C., Emmerich, E., Marquez, F.G., Clayman, S., Galis, A.: Software Architecture Definition for On-demand Cloud Provisioning. Springer Journal on Cluster Computing (May 2011), doi:10.1007/s10586-011-0152-0Google Scholar
  9. 9.
    Clayman, S., Galis, A., Mamatas, L.: Monitoring Virtual Networks. In: 12th IEEE/IFIP Network Operations and Management Symposium (NOMS 2010) - International on Management of the Future Internet, Osaka, April 19-23, pp. 19–23 (2010),
  10. 10.
  11. 11.
  12. 12.
    Adoption of Cloud Computing, in Technology, Media & Telecom by askvisory, (online access Thursday, February 10, 2011)
  13. 13.
    Clayman, S., Galis, A., Toffetti, G., Vaquero, L.M., Rochwerger, B., Massonet, P.: Future Internet Monitoring Platform for Computing Clouds. In: Di Nitto, E., Yahyapour, R. (eds.) ServiceWave 2010. LNCS, vol. 6481, pp. 215–217. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Shao, J., Wei, H., Wang, Q., Mei, H.: A runtime model based monitoring approach for Cloud. In: IEEE 3rd International Conference on CLOUD 2010, pp. 313–320 (July 2010)Google Scholar
  15. 15.
    Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Le-Phuoc, D., Quoc, H.N.M., Parreira, J.X., Hauswirth, M.: The Linked Sensor Middleware: Connecting the real world and the Semantic Web. In: 9th Semantic Web Challenge co-located with ISWC 2011, Bonn, Germany, October 23-27 (2011)Google Scholar
  17. 17.
    Goscinski, A., Brock, M.: Toward dynamic and attribute based publication, discovery and selection for Cloud computing. Future Generation Comp. Syst. 26(7) (2010)Google Scholar
  18. 18.
    Chapman, C., Emmerich, W., Galn, F., Clayman, S., Galis, A.: Elastic Service Management in Computational Clouds. In: 12th IEEE/IFIP NOMS2010 / International Workshop on Cloud Management (CloudMan 2010), Osaka, April 19-23 (2010)Google Scholar
  19. 19.
    The Real Meaning of Cloud Security Revealed (online access Monday, May 04, 2009)
  20. 20.
    Holub, V., Parsons, T., O’Sullivan, P., Murphy, J.: Run-time correlation engine for system monitoring and testing. In: ICAC-INDST 2009: Proceedings of the 6th International Conference Industry Session on Autonomic Computing, pp. 9–18. ACM, New York (2009)Google Scholar
  21. 21.
    Keeney, J., Conlan, O., Holub, V., Wang, M., Chapel, L., Serrano, M.: A Semantic Monitoring and Management Framework for End-to-end Services. In: Proceedings of 12th IFIP/IEEE International Symposium on Integrated Management – IM 2011, Dublin, IE, May 23-27 (2011)Google Scholar

Copyright information

© Authors 2013

Authors and Affiliations

  • Martin Serrano
    • 1
  • Danh Le-Phuoc
    • 1
  • Maciej Zaremba
    • 1
  • Alex Galis
    • 2
  • Sami Bhiri
    • 1
  • Manfred Hauswirth
    • 1
  1. 1.NUIG – Digital Enterprise Research Institute, DERINational University of Ireland GalwayGalwayIreland
  2. 2.UCL – Department of Electronic and Electrical EngineeringUniversity College LondonLondonU.K.

Personalised recommendations