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Home » [2401.11212] Programming Distributed Collective Processes in the eXchange Calculus
arXiv AI

[2401.11212] Programming Distributed Collective Processes in the eXchange Calculus

Advanced AI BotBy Advanced AI BotApril 7, 2025No Comments2 Mins Read
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[Submitted on 20 Jan 2024 (v1), last revised 4 Apr 2025 (this version, v3)]

View a PDF of the paper titled Programming Distributed Collective Processes in the eXchange Calculus, by Giorgio Audrito and 4 other authors

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Abstract:Recent trends like the Internet of Things (IoT) suggest a vision of dense and multi-scale deployments of computing devices in nearly all kinds of environments. A prominent engineering challenge revolves around programming the collective adaptive behaviour of such computational ecosystems. This requires abstractions able to capture concepts like ensembles (dynamic groups of cooperating devices) and collective tasks (joint activities carried out by ensembles). In this work, we consider collections of devices interacting with neighbours and that execute in nearly-synchronised sense-compute-interact rounds, where the computation is given by a single program mapping sensing values and incoming messages to output and outcoming messages. To support programming whole computational collectives, we propose the abstraction of a distributed collective process, which can be used to define at once the ensemble formation logic and its collective task. We formalise the abstraction in the eXchange Calculus (XC), a core functional language based on neighbouring values (maps from neighbours to values) where state and interaction is handled through a single primitive, exchange, and provide a corresponding implementation in the FCPP language. Then, we exercise distributed collective processes using two case studies: multi-hop message propagation and distributed monitoring of spatial properties. Finally, we discuss the features of the abstraction and its suitability for different kinds of distributed computing applications.

Submission history

From: Roberto Casadei PhD [view email]
[v1]
Sat, 20 Jan 2024 11:37:44 UTC (337 KB)
[v2]
Mon, 11 Nov 2024 18:26:31 UTC (519 KB)
[v3]
Fri, 4 Apr 2025 15:23:08 UTC (520 KB)



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