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Bringing Aggregate Programming Towards the Cloud

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Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning (ISoLA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13703))

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Abstract

Aggregate Programming (AP) is a paradigm for developing applications that execute on a fully distributed network of communicating, resource-constrained, spatially-situated nodes (e.g., drones, wireless sensors, etc.). In this paper, we address running an AP application on a high-performance, centralized computer such as the ones available in a cloud environment. As a proof of concept, we present preliminary results on the computation of graph statistics for centralised data sets, by extending FCPP, a C++ library implementing AP. This: (i) opens the way to the application of the AP paradigm to problems on large centralised graph-based data structures, enabling massive parallelisation across multiple machines, dynamically joining and leaving the computation; and (ii) represents a first step towards developing collective adaptive systems where computations dynamically move across the IoT/edge/fog/cloud continuum, based on mutable conditions such as availability of resources and network infrastructures.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università di Torino, Turin, Italy

    Giorgio Audrito, Ferruccio Damiani & Gianluca Torta

Authors
  1. Giorgio Audrito
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  2. Ferruccio Damiani
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  3. Gianluca Torta
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Corresponding author

Correspondence to Ferruccio Damiani .

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Editors and Affiliations

  1. University of Limerick, CSIS and Lero, Limerick, Ireland

    Tiziana Margaria

  2. TU Dortmund, Dortmund, Germany

    Bernhard Steffen

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Audrito, G., Damiani, F., Torta, G. (2022). Bringing Aggregate Programming Towards the Cloud. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_19

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  • DOI: https://doi.org/10.1007/978-3-031-19759-8_19

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