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 Parametric Framework for Reversible pi-Calculi

Medic, D;
2018-01-01

Abstract

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.
2018
International Workshop on Expressiveness in Concurrency
Beijing, China
September 3, 2018
Proceedings Combined 25th International Workshop on Expressiveness in Concurrency and 15th Workshop on Structural Operational Semantics, {EXPRESS/SOS}
Jorge A. Perez and Simone Tini
276
276
87
103
Medic, D; Mezzina, CA; Phillips, L; Yoshida, N
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1891839
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