A Modeling Approach to Hardware Analysis of the Heterogeneous DEAC Cluster

The employment of five distinct benchmarks on the Distributed Environment for Academic Computing (DEAC) Cluster at Wake Forest University provides meaningful metrics of cluster processor and memory performance. Given the heterogeneous nature of the DEAC Cluster, the benchmarks taken consider the specific processor architectures comprising the cluster. The data obtained will be assessed via two modeling approaches: (1) linear and polynomial regression and (2) Bayes' Theorem. The most suitable modeling approach for characterizing the DEAC Cluster is sought through the assessment of these models.