Publikation: Towards Extreme and Sustainable Graph P...
Stammdaten
Titel: | Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe |
Untertitel: | |
Kurzfassung: | The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations. |
Schlagworte: | Extreme data, graph processing, serverless computing, sustainability |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 10.2022 (Print) |
Erschienen in: |
Proceedings of the IEEE Cloud Summit 2022
Proceedings of the IEEE Cloud Summit 2022
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IEEE Xplore Digital Library;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 23 - 30 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 10.2022 |
ISBN: |
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ISSN: | - |
Homepage: | https://ieeexplore.ieee.org/document/9973125 |
Erscheinungsdatum: | 13.12.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/cloudsummit54781.2022.00010 |
Homepage: | https://ieeexplore.ieee.org/document/9973125 |
Open Access |
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AutorInnen
Radu Aurel Prodan (intern) |
Dragi Kimovski (intern) |
Andrea Bartolini (extern) |
Michael Cochez (extern) |
Alexandru Iosup (extern) |
Evgeny Kharlamov (extern) |
Joze Rozanec (extern) |
Laurentiu Vasiliu (extern) |
Ana Lucia Varbanescu (extern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
Sachgebiete | |
Forschungscluster | Kein Forschungscluster ausgewählt |
Peer Reviewed |
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Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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Arbeitsgruppen |
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Kooperationen
Organisation | Adresse | ||||
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University of Bologna
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IT - 40126 Bologna |
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Vrije Universiteit Amsterdam
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NL - 1081 HV Amsterdam |
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Bosch Center for Artificial Intelligence (BCAI), Robert Bosch GmbH
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DE - 70839 Gerlingen-Schillerhöhe |
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JOZEF STEFAN INSTITUTE
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SI - 1000 Ljubljana |
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Peracton Limited
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IE Galway |
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Universiteit Twente (UT)
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NL - 7522 NB ENSCHEDE |
Forschungsaktivitäten
(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
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Publikationen: | Keine verknüpften Publikationen vorhanden |
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