Reversible Communicating Processes

Reversible distributed programs have the ability to abort unproductive computation paths and backtrack, while unwinding communication that occurred in the aborted paths. While it is natural to assume that reversibility implies full state recovery (as with traditional roll-back recovery protocols), an interesting alternative is to separate backtracking from local state recovery. For example, such a model could be used to create complex transactions out of nested compensable transactions where a programmer-supplied compensation defines the work required to "unwind" a transaction. Reversible distributed computing has received considerable theoretical attention, but little reduction to practice; the few published implementations of languages supporting reversibility depend upon a high degree of central control. The objective of this paper is to demonstrate that a practical reversible distributed language can be efficiently implemented in a fully distributed manner. We discuss such a language, supporting CSP-style synchronous communication, embedded in Scala. While this language provided the motivation for the work described in this paper, our focus is upon the distributed implementation. In particular, we demonstrate that a "high-level" semantic model can be implemented using a simple point-to-point protocol

ReverCSP: Time-Travelling in CSP Computations

[EN] This paper presents reverCSP, a tool to animate both forward and backward CSP computations. This ability to reverse computations can be done step by step or backtracking to a given desired state of interest. reverCSP allows us to reverse computations exactly in the same order in which they happened, or also in a causally-consistent way. Therefore, reverCSP is a tool that can be especially useful to comprehend, analyze, and debug computations. reverCSP is an open-source project publicly available for the community. We describe the tool and its functionality, and we provide implementation details so that it can be reimplemented for other languages.This work has been partially supported by the EU (FEDER) and the Spanish MCI/AEI under grants TIN2016-76843-C4-1-R and PID2019- 104735RB-C41, and by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust).Galindo-Jiménez, CS.; Nishida, N.; Silva, J.; Tamarit, S. (2020). ReverCSP: Time-Travelling in CSP Computations. Springer. 239-245. https://doi.org/10.1007/978-3-030-52482-1_14S239245Bernadet, A., Lanese, I.: A modular formalization of reversibility for concurrent models and languages. In: Proceedings of ICE 2016, EPTCS (2016)Brown, G., Sabry, A.: Reversible communicating processes. Electron. Proc. Theor. Comput. Sci. 203, 45–59 (2016)Conserva Filhoa, M., Oliveira, M., Sampaio, A., Cavalcanti, A.: Compositional and local livelock analysis for CSP. Inf. Process. Lett 133, 21–25 (2018)Danos, V., Krivine, J.: Reversible communicating systems. In: Gardner, P., Yoshida, N. (eds.) CONCUR 2004. LNCS, vol. 3170, pp. 292–307. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28644-8_19Elnozahy, E.N.M., Alvisi, L., Wang, Y.-M., Johnson, D.B.: A survey of rollback- recovery protocols in message-passing systems. ACM Comput. Surv. 34(3), 375–408 (2002)Fang, Y., Zhu, H., Zeyda, F., Fei, Y.: Modeling and analysis of the disruptor framework in csp. In: Proceedings of CCWC 2018. IEEE Computer Society (2018)Ladkin, P.B., Simons, B.B.: Static deadlock analysis for CSP-type communications. In: Fussell, D.S., Malek, M. (eds.) Responsive Computer Systems: Steps Toward Fault-Tolerant Real-Time Systems. The Springer International Series in Engineering and Computer Science, vol. 297, pp. 89–102. Springer, Boston (1995). https://doi.org/10.1007/978-1-4615-2271-3_5Landauer, R.: Irreversibility and heat generation in the computing process. IBM J. Res. Dev. 5, 183–191 (1961)Lanese, I., Antares Mezzina, C., Tiezzi, F.: Causal-consistent reversibility. Bull. EATCS 114, 17 (2014)Lanese, I., Nishida, N., Palacios, A., Vidal, G.: CauDEr: a causal-consistent reversible debugger for erlang. In: Gallagher, J.P., Sulzmann, M. (eds.) FLOPS 2018. LNCS, vol. 10818, pp. 247–263. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90686-7_16Lanese, I., Palacios, A., Vidal, G.: Causal-consistent replay debugging for message passing programs. In: Pérez, J.A., Yoshida, N. (eds.) FORTE 2019. LNCS, vol. 11535, pp. 167–184. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21759-4_10Llorens, M., Oliver, J., Silva, J., Tamarit, S.: Dynamic slicing of concurrent specification languages. Parallel Comput. 53, 1–22 (2016)Llorens, M., Oliver, J., Silva, J., Tamarit, S.: Tracking CSP computations. J. Log. Algebr. Meth. Program. 102, 138–175 (2019)Perera, R., Garg, D., Cheney, J.: Causally consistent dynamic slicing. In Proceedings of CONCUR 2016, LIPIcs, vol. 59, pp. 18:1–18:15 (2016)Phillips, I., Ulidowski, I., Yuen, S.: A reversible process calculus and the modelling of the ERK signalling pathway. In: Glück, R., Yokoyama, T. (eds.) RC 2012. LNCS, vol. 7581, pp. 218–232. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36315-3_18Roscoe, A.W.: The Theory and Practice of Concurrency. Prentice Hall PTR, Upper Saddle River (1997)Zhao, H., Zhu, H., Yucheng, F., Xiao, L.: Modeling and verifying storm using CSP. In: Proceedings of HASE 2019. IEEE Computer Society (2019

Causal-Consistent Reversibility in a Tuple-Based Language

Causal-consistent reversibility is a natural way of undoing concurrent computations. We study causal-consistent reversibility in the context of µKlaim, a formal coordination language based on distributed tuple spaces. We consider both uncontrolled reversibility, suitable to study the basic properties of the reversibility mechanism, and controlled reversibility based on a rollback operator, more suitable for programming applications. The causality structure of the language, and thus the definition of its reversible semantics, differs from all the reversible languages in the literature because of its generative communication paradigm. In particular, the reversible behavior of µKlaim read primitive, reading a tuple without consuming it, cannot be matched using channel-based communication. We illustrate the reversible extensions of µKlaim on a simple, but realistic, application scenario

Reversible CSP Computations

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Reversibility enables a program to be executed both forwards and backwards. This ability allows programmers to backtrack the execution to a previous state. This is essential if the computation is not deterministic because re-running the program forwards may not lead to that state of interest. Reversibility of sequential programs has been well studied and a strong theoretical basis exists. Contrarily, reversibility of concurrent programs is still very young, especially in the practical side. For instance, in the particular case of the Communicating Sequential Processes (CSP) language, reversibility is practically missing. In this article, we present a new technique, including its formal definition and its implementation, to reverse CSP computations. Most of the ideas presented can be directly applied to other concurrent specification languages such as Promela or CCS, but we center the discussion and the implementation on CSP. The technique proposes different forms of reversibility, including strict reversibility and causal-consistent reversibility. On the practical side, we provide an implementation of a system to reverse CSP computations that is able to highlight the source code that is being executed in each forwards/backwards computation step, and that has been optimized to be scalable to real systems.A preliminary version of this work was presented at the 12th Conference on Reversible Computation [31]. The authors would like to thank the anonymous reviewers for their useful comments and constructive feedback that helped them to improve this work. This work was supported in part by the EU (FEDER) and the Spanish MCI/AEI under Grant TIN2016-76843-C4-1-R and Grant PID2019-104735RB-C41, in part by the Generalitat Valenciana under Grant Prometeo/2019/098 (DeepTrust), in part by JSPS KAKENHI under Grant JP17H01722, and in part by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA 952215.Galindo-Jiménez, CS.; Nishida, N.; Silva, J.; Tamarit, S. (2021). Reversible CSP Computations. IEEE Transactions on Parallel and Distributed Systems. 32(6):1425-1436. https://doi.org/10.1109/TPDS.2021.3051747S1425143632

A general approach to derive uncontrolled reversible semantics

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

A General Approach to Derive Uncontrolled Reversible Semantics

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 ?-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

Causal-consistent rollback in a tuple-based language

Rollback is a fundamental technique for ensuring reliability of systems, allowing one, in case of troubles, to recover a past system state. However, the definition of rollback in a concurrent/distributed scenario is quite tricky. We propose an approach based on the notion of causal-consistent reversibility: any given past action can be undone, provided that all the actions caused by it are undone as well. Given that, we define a rollback as the minimal causal-consistent sequence of backward steps able to undo a given action. We define the semantics of such a rollback operator, and show that it satisfies the above specification. The approach that we present is quite general, but we instantiate it in the case of muKlaim, a formal coordination language based on distributed tuple spaces. We remark that this is the first definition of causal-consistent rollback in a shared–memory setting. We illustrate the use of rollback in muKlaim on a simple, but realistic, application scenario

Advances in Rule-based Modeling: Compartments, Energy, and Hybrid Simulation, with Application to Sepsis and Cell Signaling

Biological systems are commonly modeled as reaction networks, which describe the system at the resolution of biochemical species. Cellular systems, however, are governed by events at a finer scale: local interactions among macromolecular domains. The multi-domain structure of macromolecules, combined with the local nature of interactions, can lead to a combinatorial explosion that pushes reaction network methods to their limits. As an alternative, rule-based models (RBMs) describe the domain-based structure and local interactions found in biological systems. Molecular complexes are represented by graphs: functional domains as vertices, macromolecules as groupings of vertices, and molecular bonding as edges. Reaction rules, which describe classes of reactions, govern local modifications to molecular graphs, such as binding, post-translational modification, and degradation. RBMs can be transformed to equivalent reaction networks and simulated by differential or stochastic methods, or simulated directly with a network-free approach that avoids the problem of combinatorial complexity. Although RBMs and network-free methods resolve many problems in systems modeling, challenges remain. I address three challenges here: (i) managing model complexity due to cooperative interactions, (ii) representing biochemical systems in the compartmental setting of cells and organisms, and (iii) reducing the memory burden of large-scale network-free simulations. First, I present a general theory of energy-based modeling within the BioNetGen framework. Free energy is computed under a pattern-based formalism, and contextual variations within reaction classes are enumerated automatically. Next, I extend the BioNetGen language to permit description of compartmentalized biochemical systems, with treatment of volumes, surfaces and transport. Finally, a hybrid particle/population method is developed to reduce memory requirements of network-free simulations. All methods are implemented and available as part of BioNetGen. The remainder of this work presents an application to sepsis and inflammation. A multi-organ model of peritoneal infection and systemic inflammation is constructed and calibrated to experiment. Extra-corporeal blood purification, a potential treatment for sepsis, is explored in silico. Model simulations demonstrate that removal of blood cytokines and chemokines is a sufficient mechanism for improved survival in sepsis. However, differences between model predictions and the latest experimental data suggest directions for further exploration