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In this paper we describe the design and implementation of a system called the Network Weather Service (NWS) that takes periodic measurements of deliverable resource performance from distributed networked resources, and uses numerical models to dynamically generate forecasts of future performance levels. These performance forecasts, along with measures of performance fluctuation (e.g the mean square prediction error) and forecast lifetime that the NWS generates, are made available to schedulers and other resource management mechanisms at runtime so that they may determine the quality-of-service that will be available from each resource. We describe the architecture of the NWS and implementations that we have developed and are currently deploying for the Legion [13] and Globus/Nexus [7] metacomputing infrastructures. We also detail NWS forecasts of resource performance using both the Legion and Globus/Nexus implementations. Our results show that simple forecasting techniques substantially outperform measurements of current conditions (commonly used to gauge resource availability and load) in terms of prediction accuracy. In addition, the techniques we have employed are almost as accurate as substantially more complex modeling methods. We compare our techniques to a sophisticated time-series analysis system in terms of forecasting accuracy and computational complexity.

Implementing a Performance Forecasting System for Metacomputing The Network Weather Service