Analyzing Simulation Model Profile Data to Assist Synthetic Model Generation

Abstract

Synthetic workloads are commonly used to exercise simulation tools for performance, performance tuning, and scalability studies. Sometimes these workloads are simple streams of test data following various distributions and in other cases these workloads are generated by more complex, configurable systems. An example of the former is a stream of input events at different arrival rates that might be used to test the performance of an event queue data structure. An example of the latter is the PHOLD simulation model that is often used to contrast the performance implications of different design solutions in a parallel simulation engine. One of the key challenges for synthetic workloads is the question of setting the parameters so that the workload properly reflects the behavior of actual workloads. This paper collects profile data from multiple real-world discrete-event simulation models in multiple configurations and sizes from the ROSS and WARPED2 repositories. A principle focus of this paper is the capture and reporting of profiling data to understand event granularities and event profile data to assist in the configuration of synthetic discrete event model generators.