Abstract: A computing device executes an application having a number of phases. The computing device collects performance data indicative of a number of performance attributes of the computing device during execution of the application. The performance attributes include page swap data, page fault data, and process queue data. The computing device merges data collected from a processor performance monitoring unit with data collected from an operating system of the computing device. The computing device partitions the performance data into a number of cluster models, applies a classification algorithm to each cluster model, and selects the cluster model with the lowest misclassification rate. The computing device associates each cluster of the cluster model to a phase of the software application. Compatible phases of software applications are scheduled based on the selected cluster model.

Abstract: Technologies for application performance analysis include a computing device capable of executing an application having a number of phases. The computing device collects performance data indicative of a number of performance attributes of the computing device during execution of the application. The computing device may merge data collected from a processor performance monitoring unit with data collected from an operating system of the computing device. The computing device partitions the performance data into a number of clusters using an unsupervised clustering algorithm such as K-means clustering, forming a cluster model. The computing device may partition the performance data into a number of cluster models, apply a classification algorithm such as regularized discriminant analysis to each cluster model, and select the cluster model with the lowest misclassification rate. The computing associates each cluster of the cluster model to a phase of the software application.