Focused Layered Performance Modelling by Aggregation

Abstract

Performance models of server systems, based on layered queues, may be very complex. This is particularly true for cloud-based systems based on microservices, which may have hundreds of distinct components, and for models derived by automated data analysis. Often only a few of these many components determine the system performance, and a smaller simplified model is all that is needed. To assist an analyst, this work describes a focused model that includes the important components (the focus) and aggregates the rest in groups, called dependency groups. The method Focus-based Simplification with Preservation of Tasks described here fills an important gap in a previous method by the same authors. The use of focused models for sensitivity predictions is evaluated empirically in the article on a large set of randomly generated models. It is found that the accuracy depends on a “saturation ratio” (SR) between the highest utilization value in the model and the highest value of a component excluded from the focus; evidence suggests that SR must be at least 2 and must be larger to evaluate larger model changes. This dependency was captured in an “Accurate Sensitivity Hypothesis” based on SR, which can be used to indicate trustable sensitivity results.