A central problem in executing performance critical parallel and distributed applications on shared networks is the selection of computation nodes and communication paths for execution. Automatic selection of nodes is complex as the best choice depends on the application structure as well as the expected availability of computation and communication resources. This paper presents a solution to this problem for realistic application and network scenarios. A new algorithm to jointly analyze computation and communication resources for different application demands is introduced and a framework for automatic node selection is developed on top of Remos, which is a query interface to network information. The paper reports results from a set of applications, including Airshed pollution modeling and magnetic resonance imaging, executing on a high speed network testbed. The results demonstrate that node selection is effective in enhancing application performance in the presence of computation load as well as network traffic. Under the network conditions used for experiments, the increase in execution time due to compute loads and network congestion was reduced by half with node selection. The node selection algorithms developed in this research are also applicable to dynamic migration of long running jobs.