Relative expressiveness of calculi for reversible concurrency

The Concurrency Column of EATCS Bulletin, No 129A number of formalisms have been proposed to model various approaches to reversibility and to better understand its properties and characteristics. However, the relation between these formalisms has hardly been studied. This paper examines the expressiveness of the causal-consistent reversibility in process algebras CCS and π-calculus. In particular, we show, by means of encodings, that LTSs of two reversible extensions of CCS, Reversible CCS [1] and CCS with Keys [2], are isomorphic up to some structural transformations of processes. To study different causal semantics for π-calculus, we devise a uniform framework for reversible π-calculi that is parametric with respect to a data structure that stores information about the extrusion of a name. Depending on the used data structure, different causal semantics can be obtained. We show that reversibility induced by our framework when instantiated with three different data structures is causally-consistent and prove a causal correspondence between certain causal semantics and matching instance of the framework

A Parametric Framework for Reversible pi-Calculi

This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in pi-calculus, which differ in the treatment of parallel extrusions of the same name. In this paper we present a uniform framework for reversible pi-calculi that is parametric with respect to a data structure that stores information about an extrusion of a name. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We show that the (parametric) reversibility induced by our framework is causally-consistent and prove a causal correspondence between an appropriate instance of the framework and Boreale and Sangiorgi's causal semantics

A Systematic Mapping Study of Italian Research on Workflows

An entire ecosystem of methodologies and tools revolves around scientific workflow management. They cover crucial non-functional requirements that standard workflow models fail to target, such as interactive execution, energy efficiency, performance portability, Big Data management, and intelligent orchestration in the Computing Continuum. Characterizing and monitoring this ecosystem is crucial to developing an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, analyzing 25 tools and 10 applications from several scientific domains in the context of the "National Research Centre for HPC, Big Data, and Quantum Computing" (ICSC). The study aims to outline the main current research directions and determine how they address the critical needs of modern scientific applications. The findings highlight a variegated research ecosystem of tools, with a prominent interest in advanced workflow orchestration and still immature but promising efforts toward energy efficiency