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 which includes the important components (the focus ) and aggregates the rest in groups, called dependency groups. The method Focus-based Simplification with Preservation of Tasks (FSPT) 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 paper 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.