A Prediction Model for Software Performance in Symmetric Multiprocessing Environments

Abstract

The broad introduction of multi-core processors made symmetric multiprocessing (SMP) environments mainstream. The additional cores can significantly increase software performance. However, their actual benefit depends on the operating system scheduler's capabilities, the system's workload, and the software's degree of concurrency. The load distribution on the available processors (or cores) strongly influences response times and throughput of software applications. Hence, understanding the operating system scheduler's influence on performance and scalability is essential for the accurate prediction of software performance (response time, throughput, and resource utilisation). Existing prediction approaches tend to approximate the influence of operating system schedulers by abstract policies such as processor sharing and its more sophisticated extensions. However, these abstractions often fail to accurately capture software performance in SMP environments. In this paper, we present a performance Model for general-purpose Operating System Schedulers (MOSS). It allows analyses of software performance taking the influences of schedulers in SMP environments into account. The model is defined in terms of timed Coloured Petri Nets and predicts the effect of different operating system schedulers (e.g., Windows 7, Vista, Server 2003, and Linux 2.6) on software performance. We validated the prediction accuracy of MOSS in a case study using a business information system. In our experiments, the deviation of predictions and measurements was below 10% in most cases and did not exceed 30%.