QoS Monitoring in a Cloud Services Environment: The SRT-15 Approach

  • Giuseppe Cicotti
  • Luigi Coppolino
  • Rosario Cristaldi
  • Salvatore D’Antonio
  • Luigi Romano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7155)

Abstract

The evolution of Cloud Computing environments has resulted in a new impulse to the service oriented computing, with hardware resources, whole applications and entire business processes provided as services in the so called “as a service” paradigm. In such a paradigm the resulting interactions should involve actors (users and providers of services) belonging to different entities and possibly to different companies, hence the success of such a new vision of the IT world is strictly tied to the possibility of guaranteed high quality levels in the provisioning of resources and services. In this paper we present QoSMONaaS (Quality of Service MONitoring as a Service), a QoS monitoring facility built on top of the SRT-15, a Cloud-oriented and CEP-based platform being developed in the context of the homonymous EU funded project. In particular we present the main components of QoSMONaaS and illustrate QoSMONaaS operation and internals with respect to a substantial case study of an Internet of Thing (IoT) application.

Keywords

Quality of Service Cloud Computing Complex Event Processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pankesh Patel, A.S., Ranabahu, A.: Service level agreement in cloud computing. In: UKPEW (2009)Google Scholar
  2. 2.
    Four keys for monitoring cloud services. White Paper from ManageEngine, http://www.manageengine.com/products/applications_manager/four-keys-for-monitoring-cloud-services.pdf
  3. 3.
    White Paper (2010), http://www.srt-15.eu/
  4. 4.
    Eugster, P.T., Felber, P.A., Guerraoui, R., Kermarrec, A.-M.: The many faces of publish/subscribe. ACM Computing Surveys 35, 114–131 (2003)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    IBM, Service Level Agreement Monitoring with IBM cognos now (2010)Google Scholar
  7. 7.
    Martin, A., Knauth, T., de Brum, D.B., Weigert, S., Creutz, S., Brito, A., Fetzer, C.: Low-overhead fault tolerance for high-throughput data processing systems. In: ICDCS (2011)Google Scholar
  8. 8.
    Ying, X., Pan, K., Wu, X., Guo, L.: Comparisons of randomization and k-degree anonymization schemes for privacy preserving social network publishing. In: SNA-KDD (2009)Google Scholar
  9. 9.
    Romano, L., Mari, D.D., Jerzak, Z., Fetzer, C.: A novel approach to qos monitoring in the cloud. In: 1st International Conference on Data Compression, Communication and Processing. IEEE Computer Society Press (June 2011)Google Scholar
  10. 10.
    Cau, A., Moszkowski, B.: Interval temporal logic (March 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Giuseppe Cicotti
    • 1
  • Luigi Coppolino
    • 1
  • Rosario Cristaldi
    • 1
  • Salvatore D’Antonio
    • 1
  • Luigi Romano
    • 1
  1. 1.Epsilon srlNaplesItaly

Personalised recommendations