Performance Information Services for Computational Grids

Abstract

Grid schedulers or resource allocators (whether they be human or automatic scheduling programs) must choose the right combination of resources from the available resource pool while the performance and availability characteristics of the individual resources within the pool change from moment to moment. Moreover, the scheduling decision fro each application component must be made before the component is executed making scheduling a predictive activity. A Grid scheduler, therefore, must be able to predict what the deliverable resource performance will be for the time period in which a particular application component will eventually use the resource. In this chapter we describe techniques for dynamically characterizing resources according to their predicted performance response to enable Grid scheduling and resource allocation. These techniques rely on three fundamental capabilities: extensible and non-intrusive performance monitoring, fast prediction models, and a flexible and high-performance reporting interface. We discuss these challenges in the context of the Network Weather Service (NWS)--an on-line performance monitoring and forecasting service developed for Grid environments. The NWS uses adaptive monitoring techniques to control intrusiveness, and non-parametric forecasting methods that are lightweight enough to generate forecasts in real-time. In addition, the service infrastructure used by the NWS is portable among all currently available Grid resources and is compatible with extant Grid middle ware such as Globus, Legion, and Condor.