MBrace

Dzik, Jan; Palladinos, Nick; Rontogiannis, Konstantinos; Tsarpalis, Eirik; Vathis, Nikolaos

doi:10.1145/2525528.2525531

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…During this time, the consultancy Nessos in Athens developed an innovative cloud programming system called MBrace implemented in F# [Dzik et al 2013]. Originally conceived as a distributed programming system, later iterations emphasized cloud computing and big-data processing, and was used internally at Microsoft to demonstrate the relevance of F# for cloud programming.…”

Section: Net F# and The Shift To Cloud And Mobile Computingmentioning

confidence: 99%

The early history of F#

Syme

2020

Proc. ACM Program. Lang.

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This paper describes the genesis and early history of the F# programming language. I start with the origins of strongly-typed functional programming (FP) in the 1970s, 80s and 90s. During the same period, Microsoft was founded and grew to dominate the software industry. In 1997, as a response to Java, Microsoft initiated internal projects which eventually became the .NET programming framework and the C# language. From 1997 the worlds of academic functional programming and industry combined at Microsoft Research, Cambridge. The researchers engaged with the company through Project 7, the initial effort to bring multiple languages to .NET, leading to the initiation of .NET Generics in 1998 and F# in 2002. F# was one of several responses by advocates of strongly-typed functional programming to the "object-oriented tidal wave" of the mid-1990s. The development of the core features of F# 1.0 happened from 2004-2007, and I describe the decision-making process that led to the "productization" of F# by Microsoft in 2007-10 and the release of F# 2.0. The origins of F#'s characteristic features are covered: object programming, quotations, statically resolved type parameters, active patterns, computation expressions, async, units-of-measure and type providers. I describe key developments in F# since 2010, including F# 3.0-4.5, and its evolution as an open source, cross-platform language with multiple delivery channels. I conclude by examining some uses of F# and the influence F# has had on other languages so far.

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Section: Net F# and The Shift To Cloud And Mobile Computingmentioning

confidence: 99%

The early history of F#

Syme

2020

Proc. ACM Program. Lang.

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“…Dryad [Isard and Yu 2009], PigLatin [Olston et al 2008], and FlumeJava [Chambers et al 2010] are similar to MapReduce -the difference is in their support for more flexible operations than just map and reduce. Spark [Zaharia et al 2012], Flink [Alexandrov et al 2014;Carbone et al 2015], and MBrace [Dzik et al 2013] express computations as embedded domain specific functional dataflow languages. Viering et al [2018] develop a typing discipline to ensure correct execution even in the presence of faults.…”

Section: Cluster-based Systemsmentioning

confidence: 99%

A fault-tolerant programming model for distributed interactive applications

Mogk

Drechsler

Salvaneschi

et al. 2019

Proc. ACM Program. Lang.

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Ubiquitous connectivity of web, mobile, and IoT computing platforms has fostered a variety of distributed applications with decentralized state. These applications execute across multiple devices with varying reliability and connectivity. Unfortunately, there is no declarative fault-tolerant programming model for distributed interactive applications with an inherently decentralized system model. We present a novel approach to automating fault tolerance using high-level programming abstractions tailored to the needs of distributed interactive applications. Specifically, we propose a calculus that enables formal reasoning about applications' dataflow within and across individual devices. Our calculus reinterprets the functional reactive programming model to seamlessly integrate its automated state change propagation with automated crash recovery of device-local dataflow and disconnection-tolerant distribution with guaranteed automated eventual consistency semantics based on conflict-free replicated datatypes. As a result, programmers are relieved of handling intricate details of distributing change propagation and coping with distribution failures in the presence of interactivity. We also provides proofs of our claims, an implementation of our calculus, and an empirical evaluation using a common interactive application. CCS Concepts: • Theory of computation → Distributed computing models; • Software and its engineering → Software fault tolerance; Data flow languages.

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