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OASEES: Leveraging DAO-Based Programmable Swarms for Optimized Edge-to-Cloud Data Processing

  • M. A. Kourtis
  • , I. Gutierrez
  • , E. Areizaga
  • , G. Alexandridis
  • , W. Tavernier
  • , A. Imeri
  • , N. Tcholtchev
  • , G. Xilouris
  • , P. Trakadas
  • , I. Chochliouros
  • , I. Koufos*
  • *Autor correspondiente de este trabajo
  • Demokritos National Centre for Scientific Research
  • National and Kapodistrian University of Athens
  • Interuniversitair Micro-Elektronica Centrum
  • Luxembourg Institute of Science and Technology
  • Fraunhofer Institute for Open Communication Systems
  • Hellenic Telecommunications Organization S.A.

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

As traditional linear models stagnate decision-making and data federation, there’s a pressing need for a novel, swarm-based cloud-edge computing approach to enhance European data sovereignty and foster a sustainable, circular economy across various market sectors. To that end, the EU-backed OASEES project identifies a need for an innovative, inclusive, and disruptive approach to the cloud-to-edge continuum, swarm programmability, and data federation over GAIA-X. This paper underscores the actual challenges associated with managing and orchestrating edge infrastructure and services, thereby harnessing the potential of edge processing and federated learning. Moreover, it delves into the core features of the OASEES approach, taking into account technological challenges anticipated in system development. We also explore the integration of multi-tenant, interoperable, secure, and trustworthy deployments into the cloud-to-edge paradigm, in line with the conference’s scope. Briefly, we discuss several vertical edge applications with substantial market impact, demonstrating how our approach partially addresses the existing gaps and contributes to a decentralized AI ecosystem.

Idioma originalInglés
Título de la publicación alojadaDistributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference
EditoresRashid Mehmood, Guillermo Hernández, Isabel Praça, Jaroslaw Wikarek, Roussanka Loukanova, Arsénio Monteiro dos Reis, Antonio Skarmeta, Eleonora Lombardi
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas311-318
Número de páginas8
ISBN (versión impresa)9783031764585
DOI
EstadoPublicada - 2025
Evento21st International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024 - Salamanca, Espana
Duración: 25 jun 202427 jun 2024

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen1198 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia21st International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024
País/TerritorioEspana
CiudadSalamanca
Período25/06/2427/06/24

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 9: Industria, innovación e infraestructura
    ODS 9: Industria, innovación e infraestructura
  2. ODS 17: Alianzas para lograr los objetivos
    ODS 17: Alianzas para lograr los objetivos

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