Document number: N4024 Date: 2014-05-22 Project:

Reply-to: Nat Goodspeed ( nat at lindenlab dot com) Oliver Kowalke (oliver dot kowalke at gmail dot com) Distinguishing coroutines and fiber

A fault tolerance bisimulation proof for consensus

The possibility of partial failure occuring at any stage of computation complicates rigorous formal treatment of distributed algorithms. We propose a methodology for formalising and proving the correctness of distributed algorithms which alleviates this complexity. The methodology uses fault-tolerance bisimulation proof techniques to split the analysis into two phases, that is a failure-free phase and a failure phase, permitting separation of concerns. We design a minimal partial-failure calculus, develop a corresponding bisimulation theory for it and express a consensus algorithm in the calculus. We then use the consensus example and the calculus theory to demonstrate the benefits of our methodology.peer-reviewe

Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

Proving distributed algorithm correctness using fault tolerance bisimulations

The possibility of partial failure occuring at any stage of computation complicates rigorous formal treatment of distributed algorithms. We propose a methodology for formalising and proving the correctness of distributed algorithms which alleviates this complexity. The methodology uses fault-tolerance bisimulation proof techniques to split the analysis into two phases, that is a failure-free phase and a failure phase, permitting separation of concerns. We design a minimal partial-failure calculus, develop a corresponding bisimulation theory for it and express commit and consensus algorithms in the calculus. We then use the consensus example and the calculus theory as the framework in which to demonstrate the benefits of our methodology.peer-reviewe

A constraint programming approach to the hospitals/residents problem

An instance I of the Hospitals/Residents problem (HR) involves a set of residents (graduating medical students) and a set of hospitals, where each hospital has a given capacity. The residents have preferences for the hospitals, as do hospitals for residents. A solution of I is a <i>stable matching</i>, which is an assignment of residents to hospitals that respects the capacity conditions and preference lists in a precise way. In this paper we present constraint encodings for HR that give rise to important structural properties. We also present a computational study using both randomly-generated and real-world instances. We provide additional motivation for our models by indicating how side constraints can be added easily in order to solve hard variants of HR