Order Graphs and Cross-Layer Parametric Significance-Driven Modelling

Abstract

Traditional hierarchical modelling methods tend to have layers of abstraction corresponding to naturally existing layers of concern in multi-level systems. Although logically and functionally intuitive, this is not always optimal for analysis and design. For instance, parts of a system in the same logical layer may not contribute to the same degree on some metric, e.g. system power consumption. When focusing on a specific parameter or set of parameters, to moderate the analysis, design and runtime effort, less significant parts of the system should be modelled at higher levels of abstraction and more significant ones with more detail. This parametric significance-driven modelling approach focuses more on optimal parametric fidelity than on logical intuition. Using system power consumption as an example parameter, this paper presents Order Graphs (OGs), which have a clear hierarchical structure, and provide straightforward vertical zooming across multiple layers (orders) of model abstraction, resulting in the discovery of power-proportional cuts that run through different orders to be analysed together in a flat manner. Stochastic Activity Networks (SANs), a good flat modelling method, is suggested as an example of studying techniques for cuts discovered with OGs. A series of experiments on an Odroid development system consisting of an ARM big.LITTLE multi-core structure provides initial validation for the approach.