This paper describes and evaluates the application of three local learning algorithms --- nearest-neighbor, weighted-average, and locally-weighted polynomial regression --- for the prediction of run-specific resourceusage on the basis of run-time input parameters supplied to tools. A two-level knowledge base allows the learning algorithms to track short-term fluctuations in the performance of computing systems, and the use of instance editing techniques improves the scalability of the performance-modeling system. The learning algorithms assist PUNCH, a network-computing system at Purdue University, in emulating an ideal user in terms of its resource management and usage policies. 1. Introduction It is now recognized that the heterogeneous nature of the network-computing environment cannot be effectively exploited without some form of adaptive or demand-driven resource management (e.g., [10, 11, 12, 14, 18, 27]). A demand-driven resource management system can be characterized by its a...