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

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, the authors present a novel case study with the Globus Toolkit in which they show how performance models can be generated dynamically and used to provide online performance prediction capabilities.