Resource-usage prediction for demand-based network-computing

This paper reports on an application of artificial intelligence to achieve demand-based scheduling within the context of a network-computing infrastructure. The described AI system uses tool-specific, run-time input to predict the resource-usage characteristics of runs. Instance-based learning with locally weighted polynomial regression is employed because of the need to simultaneously learn multiple polynomial concepts and the fact that knowledge is acquired incrementally in this domain. An innovative use of a two-level knowledge base allows the system to account for short-term variations in compute-server and network performance and exploit temporal and spatial locality of runs. Instance editing allows the approach to be tolerant to noise and computationally feasible for extended use. The learning system was tested on three tools during normal use of the Purdue University Network Computing Hubs. Results indicate that the described instance-based learning technique using locally weighted regression with a locally linear model works well for this domain.