Service Provisioning on the Cloud: Distributed Algorithms for Joint Capacity Allocation and Admission Control

  • Danilo Ardagna
  • Carlo Ghezzi
  • Barbara Panicucci
  • Marco Trubian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6481)

Abstract

Cloud computing represents a new way to deliver and use services on a shared IT infrastructure. Traditionally, IT hardware and software were acquired and provisioned on business premises. Software applications were built, possibly integrating off-the-shelf components, deployed and run on these privately owned resources. With service-oriented computing, applications are offered by service providers to clients, who can simply invoke them through the network. The offer specifies both the functionality and the Quality of Service (QoS). Providers are responsible for deploying and running services on their own resources. Cloud computing moves one step further. Computing facilities can also be delivered on demand in the form of services over a network. In this paper we take the perspective of a Software as a Service (SaaS) provider whose goal is to maximize the revenues from end users who access services on a pay-per-use basis. In turn, the SaaS provider exploits the cloud, which provides an Infrastructure as a Service (IaaS), where the service provider dynamically allocates hardware physical resources.

This paper presents a distributed algorithm for run-time management of SaaS cloud systems that jointly addresses the capacity allocation and admission control of multiple classes of applications providing an heuristic solution which closely approximates the global optimal solution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, B., Ledolter, J.: Statistical Methods for Forecasting. John Wiley and Sons, Chichester (1983)CrossRefMATHGoogle Scholar
  2. 2.
    Almeida, J.M., Almeida, V.A.F., Ardagna, D., Cunha, I.S., Francalanci, C., Trubian, M.: Joint admission control and resource allocation in virtualized servers. J. Parallel Distrib. Comput. 70(4), 344–362 (2010)CrossRefMATHGoogle Scholar
  3. 3.
    Amazon Inc. Amazon Elastic Cloud, http://aws.amazon.com/ec2/
  4. 4.
    Andreolini, M., Casolari, S., Colajanni, M.: Autonomic request management algorithms for geographically distributed internet-based systems. In: SASO (2008)Google Scholar
  5. 5.
    Ardagna, D., Panicucci, B., Trubian, M., Zhang, L.: Energy-Aware Autonomic Resource Allocation in Multi-tier Virtualized Environments. IEEE Trans. on Services Computing (to appear)Google Scholar
  6. 6.
    Bennani, M., Menascé, D.: Resource Allocation for Autonomic Data Centers Using Analytic Performance Models. In: IEEE Int’l Conf. Autonomic Computing Proc. (2005)Google Scholar
  7. 7.
    Bertsekas, D.: Nonlinear Programming. Athena Scientific (1999)Google Scholar
  8. 8.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.: Queueing Networks and Markov Chains. J. Wiley, Chichester (1998)CrossRefMATHGoogle Scholar
  9. 9.
    Dikaiakos, M.D., Katsaros, D., Mehra, P., Pallis, G., Vakali, A.: Cloud Computing: Distributed Internet Computing for IT and Scientific Research. IEEE Internet Computing 13(5), 10–13 (2009)CrossRefGoogle Scholar
  10. 10.
    Erdogmus, H.: Cloud computing: Does nirvana hide behind the nebula? IEEE Softw.  26(2), 4–6 (2009)Google Scholar
  11. 11.
    Felber, P., Kaldewey, T., Weiss, S.: Proactive hot spot avoidance for web server dependability. In: IEEE Symposium on Reliable Distributed Systems, pp. 309–318 (2004)Google Scholar
  12. 12.
    Feng, H., Liu, Z., Xia, C.H., Zhang, L.: Load shedding and distributed resource control of stream processing networks. Perform. Eval. 64(9-12), 1102–1120 (2007)CrossRefGoogle Scholar
  13. 13.
    Liu, Z., Squillante, M.S., Wolf, J.: On maximizing service-level-agreement profits. In: Proc. 3d ACM Conf. on Electronic Commerce (2001)Google Scholar
  14. 14.
    Menascé, D.A., Dubey, V.: Utility-based QoS Brokering in Service Oriented Architectures. In: IEEE International Conference on Web Services Proceedings, pp. 422–430 (2007)Google Scholar
  15. 15.
    Nitto, E.D., Dubois, D.J., Mirandola, R., Saffre, F., Tateson, R.: Self-aggregation techniques for load balancing in distributed systems. In: SASO (2008)Google Scholar
  16. 16.
    Urgaonkar, B., Pacifici, G., Shenoy, P.J., Spreitzer, M., Tantawi, A.N.: Analytic modeling of multitier Internet applications. ACM Transaction on Web, 1(1) (January 2007)Google Scholar
  17. 17.
    Wolf, J.L., Bansal, N., Hildrum, K., Parekh, S., Rajan, D., Wagle, R., Wu, K.-L., Fleischer, L.: SODA: An optimizing scheduler for large-scale stream-based distributed computer systems. In: Issarny, V., Schantz, R. (eds.) Middleware 2008. LNCS, vol. 5346, pp. 306–325. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Danilo Ardagna
    • 1
  • Carlo Ghezzi
    • 1
  • Barbara Panicucci
    • 1
  • Marco Trubian
    • 2
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoItaly
  2. 2.Dipartimento di Scienze dell’InformazioneUniversità degli Studi di MilanoItaly

Personalised recommendations