Building Online Performance Models of Grid Middleware with Fine-Grained Load-Balancing: A Globus Toolkit Case Study

  • Ramon Nou
  • Samuel Kounev
  • Jordi Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4748)


As Grid computing increasingly enters the commercial domain, performance and Quality of Service (QoS) issues are becoming a major concern. To guarantee that QoS requirements are continuously satisfied, the Grid middleware must be capable of predicting the application performance on the fly when deciding how to distribute the workload among the available resources. One way to achieve this is by using online performance models that get generated and analyzed on the fly. In this paper, we present a novel case study with the Globus Toolkit in which we show how performance models can be generated dynamically and used to provide online performance prediction capabilities. We have augmented the Grid middleware with an online performance prediction component that can be called at any time during operation to predict the Grid performance for a given resource allocation and load-balancing strategy. We evaluate the quality of our performance prediction mechanism and present some experimental results that demonstrate its effectiveness and practicality. The framework we propose can be used to design intelligent QoS-aware resource allocation and admission control mechanisms.


Performance Prediction Schedule Strategy Service Request Grid Environment Online Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ramon Nou
    • 1
  • Samuel Kounev
    • 2
  • Jordi Torres
    • 1
  1. 1.Barcelona Supercomputing Center (BSC), Technical University of Catalonia (UPC), BarcelonaSpain
  2. 2.University of Cambridge Computer Laboratory, Cambridge, CB3 0FDUK

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