Performance Surface Prediction for WAN-Based Clusters

Abstract

The last five years have been a period of exponential 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 viable parallel processing platform. In order to achieve acceptable performance on this kind of a machine, performance 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 applications are used to build characteristic surfaces. These surfaces are then used to provide guidance in placement of new applications. This prediction method is intended to minimize both the execution time of the application and the impact of the application on the nationwide virtual machine. Performance prediction tools are an important prerequisite to effectively utilizing WAN based clusters.