MAG$π$!: The Role of Replication in Typing Failure-Prone Communication
April 24, 2024 · Declared Dead · 🏛 Formal Techniques for (Networked and) Distributed Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Matthew Alan Le Brun, Ornela Dardha
arXiv ID
2404.16213
Category
cs.PL: Programming Languages
Citations
1
Venue
Formal Techniques for (Networked and) Distributed Systems
Last Checked
4 months ago
Abstract
MAG$π$ is a Multiparty, Asynchronous and Generalised $π$-calculus that introduces timeouts into session types as a means of reasoning about failure-prone communication. Its type system guarantees that all possible message-loss is handled by timeout branches. In this work, we argue that the previous is unnecessarily strict. We present MAG$π$!, an extension serving as the first introduction of replication into Multiparty Session Types (MPST). Replication is a standard $π$-calculus construct used to model infinitely available servers. We lift this construct to type-level, and show that it simplifies specification of distributed client-server interactions. We prove properties relevant to generalised MPST: subject reduction, session fidelity and process property verification.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Programming Languages
R.I.P.
👻
Ghosted
R.I.P.
👻
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
👻
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
👻
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
👻
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
👻
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
👻
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
👻
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
👻
Ghosted