Performance Surface Prediction for WAN-Based Clusters

The last ve years have been a period of exponen-tial growth in the number of machines connected to the Internet and the speed at which these machines communicate. The infrastructure is now in place to consider a nationwide cluster of workstations as a vi-able parallel processing platform. In order to achieve acceptable performance on this kind of a machine, per-formance prediction tools must provide information on where to place computational objects. Incorrect object placement can result in poor performance and congestion in the network. This research develops a new paradigm for predicting performance in the Wide Area Network (WAN) based cluster arena. Statistical samples of the performance of clusters and applica