Negotiated Economic Grid Brokering for Quality of Service

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 203)


We demonstrate a Grid broker’s job submission system and its selection process for finding the provider that is most likely to be able to complete work on time and on budget. We compare several traditional site selection mechanisms with an economic and Quality of Service (QoS) oriented approach. We show how a greater profit and QoS can be achieved if jobs are accepted by the most appropriate provider. We particularly focus upon the benefits of a negotiation process for QoS that enables our selection process to occur.


Negotiation Grid brokering Quality of service Job admission control Provider selection 


  1. 1.
    Liu, C., Baskiyar, S.: A general distributed scalable grid scheduler for independent tasks. J. Parallel Distrib. Comput. 69(3), 307–314 (2009)CrossRefGoogle Scholar
  2. 2.
    Battre, D., et al.: Planning-based scheduling for SLA-awareness and grid integration. In: PlanSIG 2007 the 26th Workshop of the UK Planning and Scheduling Special Interest Group. 2007. Prague, Czech RepublicGoogle Scholar
  3. 3.
    Kokkinos, P., Varvarigos, E.A.: A framework for providing hard delay guarantees and user fairness in Grid computing. Future Gen Comput. Syst. 25(6), 674–686 (2009)CrossRefGoogle Scholar
  4. 4.
    AuYoung, A., et al.: Service contracts and aggregate utility functions, in 15th IEEE International Symposium on High Performance Distributed Computing (HPDC-15). 2005, IEEE: New York, pp. 119–131Google Scholar
  5. 5.
    Iosup, A., Epema, D.: Grid computing workloads. Internet Comput. IEEE 15(2), 19–26 (2011)CrossRefGoogle Scholar
  6. 6.
    Buyya, R., Abramson, D., Venugopal, S.: The grid economy. Proc. IEEE 93(3), 698–714 (2005)CrossRefGoogle Scholar
  7. 7.
    Lai, K.: Markets are dead, long live markets. SIGecom Exch. 5(4), 1–10 (2005)CrossRefGoogle Scholar
  8. 8.
    Open Grid Forum, WS-Agreement Negotiation Version 1.0. 2011. p. 63Google Scholar
  9. 9.
    Kavanagh, R., Djemame, K.: A grid broker pricing mechanism for temporal and budget guarantees. In: 8th European Performance Engineering Workshop (EPEW’2011). 2011. Borrowdale, The Lake District, UK: SpringerGoogle Scholar
  10. 10.
    Lee, Y.C., Zomaya, A.Y.: Practical scheduling of bag-of-tasks applications on grids with dynamic resilience. IEEE Trans. Comput. 56(6):815–825 (2007)Google Scholar
  11. 11.
    Buyya, R., et al.: Economic models for resource management and scheduling in Grid computing. Concurr. Comput.: Pract. Experience 14(13–15), 1507–1542 (2002)MATHCrossRefGoogle Scholar
  12. 12.
    Ganglia Project.: Ganglia Monitoring System. 2012; Available from:
  13. 13.
    NGS.: National Grid Service. 2009; Available from:
  14. 14.
    OpenNebula Project.: OpenNebula Homepage. 2012; Available from:
  15. 15.
    Citrix Systems.: Home of the Xen hypervisor. 2012; Available from:
  16. 16.
    Irwin, D.E., Grit, L.E., Chase, J.S.: Balancing risk and reward in a market-based task service. In: Proceedings of 13th IEEE International Symposium on High performance Distributed Computing, 2004Google Scholar
  17. 17.
    Chun, B.N., Culler, D.E.: User-centric performance analysis of market-based cluster batch schedulers. In: 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2002Google Scholar
  18. 18.
    Chee Shin, Y, Buyya, R.: Service level agreement based allocation of cluster resources: handling penalty to enhance utility. In: Cluster Computing, 2005Google Scholar
  19. 19.
    Schnizler, B., et al.: Trading grid services—a multi-attribute combinatorial approach. Eur. J. Oper. Res. 187(3), 943–961 (2008)MATHCrossRefGoogle Scholar
  20. 20.
    Popovici, F.I., Wilkes, J.: Profitable services in an uncertain world. in Supercomputing, 2005. In: Proceedings of the ACM/IEEE SC 2005 Conference. 2005Google Scholar
  21. 21.
    Han, Y., Youn, C.-H.: A new grid resource management mechanism with resource-aware policy administrator for SLA-constrained applications. Futur. Gener. Comput. Syst. 25(7), 768–778 (2009)CrossRefGoogle Scholar
  22. 22.
    Abramson, D., Giddy, J., Kotler, L.: High performance parametric modeling with Nimrod/G: Killer application for the global Grid? In: International Parallel and Distributed Processing Symposium (IPDPS). Cancun, Mexico, 2000Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.School of ComputingUniversity of LeedsLeedsUK

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