Full-Stack Performance Model Evaluation using Probabilistic Garbage Collection Simulation

Abstract

Performance models can represent the performance relevant aspects of an enterprise application. Corresponding simulation engines use such models for simulating performance metrics (e.g., response times, resource utilization, throughput) and allow for performance evaluations without load testing the actual system. Creating such models manually often outweighs their benefits. Therefore, recent research created performance model generators, which can generate such models out of Application Performance Management software. However, a full-stack evaluation containing all relevant resources of an enterprise application (Central Processing Unit, memory, network and Hard Disk Drive) has not been conducted to the best of our knowledge. This work closes this gap using a pre-release version of the next generation industry benchmark SPECjEnterpriseNEXT of the Standard Performance Evaluation Corporation as example enterprise application, the Palladio Component Model as performance model and the performance model generator of the RETIT Capacity Manager. Furthermore, this work extends the generated model with a probabilistic garbage collection model to simulate memory allocation and releases more accurately.