An Extended Survey and a Comparison Framework for Dataflow Models of Computation and Communication

TL;DR

This paper provides a comprehensive survey and classification of Dataflow Models of Computation and Communications (DF MoCCs) used in Cyber-Physical Systems, along with a framework for comparing their expressiveness and analyzability.

Contribution

It offers the first extensive classification and feature analysis of DF MoCCs, and introduces a quantitative comparison framework for their expressiveness and static analysis capabilities.

Findings

Eight categories of DF MoCCs identified

Features and static analyses systematically characterized

A framework for quantitative comparison of models proposed

Abstract

Dataflow Model of Computation and Communications (DF MoCCs) is a formalism used to specify the behavior of Cyber-Physical Systems (CPSs). DF MoCCs are widely used in the design of CPSs, as they provide a high-level of abstraction to specify the system's behavior. DF MoCCs rules give semantics to a dataflow specification of a CPS, and static analysis algorithms rely on these semantics to guarantee safety properties of the dataflow specification, such as bounded memory usage and deadlock freeness. A wide range of DF MoCCs exists, each with its own characteristics and static analyses. This paper presents a survey of those DF MoCCs and a classification in eight categories. In addition, DF MoCCs are characterized by a comprehensive list of features and static analyses, which reflect their expressiveness and analyzability. Based on this characterization, a framework is proposed to compare the expressiveness and the analyzability of DF MoCCs quantitatively.