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Portals: An Extension of Dataflow Streaming for Stateful Serverless
RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-7119-5234
RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-9351-8508
KTH Royal Institute of Technology, Sweden.
2022 (English)In: Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Association for Computing Machinery , 2022, p. 153-171Conference paper, Published paper (Refereed)
Abstract [en]

Portals is a serverless, distributed programming model that blends the exactly-once processing guarantees of stateful dataflow streaming frameworks with the message-driven compositionality of actor frameworks. Decentralized applications in Portals can be built dynamically, scale on demand, and always satisfy strict atomic processing guarantees that are natively embedded in the framework’s principal elements of computation, known as atomic streams. In this paper, we describe the capabilities of Portals and demonstrate its use in supporting several popular existing distributed programming paradigms and use-cases. We further introduce all programming model invariants and the corresponding system methods used to satisfy them.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2022. p. 153-171
Series
Onward! 2022
Keywords [en]
stateful serverless, dataflow streaming, exactly-once processing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-62375DOI: 10.1145/3563835.3567664OAI: oai:DiVA.org:ri-62375DiVA, id: diva2:1730378
Conference
2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software
Note

This work was partially funded by Digital Futures (Resilient Decentralized Computingž research pairs project),the Swedish Foundation for Strategic Research under GrantNo.: BD15-0006, as well as RISE AI. 

Available from: 2023-01-24 Created: 2023-01-24 Last updated: 2023-06-02Bibliographically approved

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Publisher's full texthttps://doi.org/10.1145/3563835.3567664

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Spenger, JonasCarbone, Paris

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CiteExportLink to record
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