The notion of reversible computing is attracting interest because of its applications in diverse fields, in particular the study of programming abstractions for fault tolerant systems. Most computational models are not naturally reversible since computation causes loss of information, and history information must be stored to enable reversibility. In the literature, two approaches to reverse the CCS process calculus exist, differing on how history information is kept. Reversible CCS (RCCS), proposed by Danos and Krivine, exploits dedicated stacks of memories attached to each thread. CCS with Keys (CCSK), proposed by Phillips and Ulidowski, makes CCS operators static so that computation does not cause information loss. In this paper we show that RCCS and CCSK are equivalent in terms of LTS isomorphism.

/pdf/static-versus-dynamic-reversibility-in-ccs-1a3z721p4x.pdf

Static versus dynamic reversibility in CCS

CCSK is a reversible form of CCS which is causal, meaning that actions can be reversed if and only if each action caused by them has already been reversed; there is no control on whether or when a computation reverses. We propose an event structure semantics for CCSK. For this purpose we define a category of reversible bundle event structures, and use the causal subcategory to model CCSK. We then modify CCSK to control the reversibility with a rollback primitive, which reverses a specific action and all actions caused by it. To define the event structure semantics of rollback, we change our reversible bundle event structures by making the conflict relation asymmetric rather than symmetric, and we exploit their capacity for non-causal reversibility.

/pdf/event-structure-semantics-of-controlled-reversible-ccs-3ykgxpi52a.pdf

Event structure semantics of (controlled) reversible CCS

https://pure.rug.nl/ws/files/48555902/Authors_version_Reversibility_in_Session_Based_Concurrency.pdf

Reversibility in session-based concurrency: A fresh look

The notion of reversible computing is attracting interest because of its applications in diverse fields, in particular the study of programming abstractions for fault tolerant systems. Reversible CCS (RCCS), proposed by Danos and Krivine, enacts reversibility by means of memory stacks. Ulidowski and Phillips proposed a general method to reverse a process calculus given in a particular SOS format, by exploiting the idea of making all the operators of a calculus static. CCSK is then derived from CCS with this method. In this paper we show that RCCS is at least as expressive as CCSK.

/pdf/static-vs-dynamic-reversibility-in-ccs-3c1kgk8qw5.pdf

Static VS Dynamic Reversibility in CCS

Petri Nets are a well-known model of concurrency and provide an ideal setting for the study of fundamental aspects in concurrent systems. Despite their simplicity, they still lack a satisfactory causally reversible semantics. We develop such semantics for Place/Transitions Petri Nets (P/T nets) based on two observations. Firstly, a net that explicitly expresses causality and conflict among events, e.g., an occurrence net, can be straightforwardly reversed by adding reversal for each of its transitions. Secondly, the standard unfolding construction associates a P/T net with an occurrence net that preserves all of its computation. Consequently, the reversible semantics of a P/T net can be obtained as the reversible semantics of its unfolding. We show that such reversible behaviour can be expressed as a finite net whose tokens are coloured by causal histories. Colours in our encoding resemble the causal memories that are typical in reversible process calculi.

Reversing P/T Nets

This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in π-calculus, which differ in the treatment of parallel extrusions of the same name. Hence, by using a parametric way of bookkeeping the order and the dependencies among extruders it is possible to map different causal semantics into the same framework. Starting from this simple observation, we present 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. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We prove causal-consistency for the three instances of our framework. Furthermore, we prove a causal correspondence between the appropriate instances of the framework and the Boreale-Sangiorgi semantics and an operational correspondence with the reversible π-calculus causal semantics.

/pdf/a-parametric-framework-for-reversible-p-calculi-4cl9ds7i3o.pdf

A parametric framework for reversible π-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 Parametric Framework for Reversible $\pi$-Calculi

Reversible computing is a paradigm where programs can execute backward as well as in the usual forward direction. Reversible computing is attracting interest due to its applications in areas as different as biochemical modelling, simulation, robotics and debugging, among others. In concurrent systems the main notion of reversible computing is called causal-consistent reversibility, and it allows one to undo an action if and only if its consequences, if any, have already been undone.
This paper presents a general and automatic technique to define a causal-consistent reversible extension for given forward models. We support models defined using a reduction semantics in a specific format and consider a causality relation based on resources consumed and produced. The considered format is general enough to fit many formalisms studied in the literature on causal-consistent reversibility, notably Higher-Order π-calculus and Core Erlang, an intermediate language in the Erlang compilation. Reversible extensions of these models in the literature are ad hoc, while we build them using the same general technique. This also allows us to show in a uniform way that a number of relevant properties, causal-consistency in particular, hold in the reversible extensions we build. Our technique also allows us to go beyond the reversible models in the literature: we cover a larger fragment of Core Erlang, including remote error handling based on links, which has never been considered in the reversibility literature.

https://drops.dagstuhl.de/opus/volltexte/2020/12845/pdf/LIPIcs-CONCUR-2020-33.pdf/

Doriana Medić

Papers

Static versus dynamic reversibility in CCS

Static VS Dynamic Reversibility in CCS

A parametric framework for reversible π-calculi

A Parametric Framework for Reversible $\pi$-Calculi

A General Approach to Derive Uncontrolled Reversible Semantics