logseq/pages/@Big Data Pipelines on the Computing Continuum%3A Ecosystem and Use Cases Overview.md

tags:: ReadingNotes date:: 05-09-2021 publisher:: IEEE place:: "Athens, Greece" conference-name:: 2021 IEEE Symposium on Computers and Communications (ISCC) proceedings-title:: 2021 IEEE Symposium on Computers and Communications (ISCC) isbn:: 978-1-66542-744-9 doi:: 10.1109/ISCC53001.2021.9631410 title:: @Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview pages:: 1-4 item-type:: ConferencePaper access-date:: 2023-01-17T09:22:48Z original-title:: Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview language:: en url:: https://ieeexplore.ieee.org/document/9631410/ short-title:: Big Data Pipelines on the Computing Continuum authors:: Dumitru Roman, Nikolay Nikolov, Ahmet Soylu, Brian Elvesaeter, Hui Song, Radu Prodan, Dragi Kimovski, Andrea Marrella, Francesco Leotta, Mihhail Matskin, Giannis Ledakis, Konstantinos Theodosiou, Anthony Simonet-Boulogne, Fernando Perales, Evgeny Kharlamov, Alexandre Ulisses, Arnor Solberg, Raffaele Ceccarelli library-catalog:: DOI.org (Crossref) links:: Local library, Web library

• Abstract
  • Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.
• Attachments
  • Roman et al. - 2021 - Big Data Pipelines on the Computing Continuum Eco.pdf {{zotero-imported-file TVIRF2SK, "Roman et al. - 2021 - Big Data Pipelines on the Computing Continuum Eco.pdf"}}
• Highlights PROJECTS/PODIUM
  • ((63c66be1-9c35-4c36-afcb-e8cde7371892))
  • ((63c676b9-3c80-453e-8e82-888a24d62550))
  • ((63c677a1-f70a-45e8-a2bd-7915bf68a9e5))
    • @The rise of serverless computing
  • Pipeline at
    • DESIGN-TIME: ((63c6a293-9c12-4bb2-a9f1-8a74cbfd15c2))
    • RUN-TIME: ((63c6a2c1-766b-4738-8569-cdf9613db539))
      • question Is it possible to change the deploymend at run-time depending on the characteristis of the input data?
  • ((63c6a527-ff3b-4b40-801c-8987d9f17400)) New Motivations
  • ((63c6a57c-4b14-4a25-9bc9-3e49467e37c1)) New Motivations
  • ((63c6a5be-1031-4248-8a92-5c52da5182f4)) New Motivations
  • ((63c6a81d-8ab4-4044-8313-8213b8248625)) New Motivations
    • ((63c6a855-718c-47b7-b5ed-92c8f647615d))
      • question What kind of intelligent resource management do they employ?
  • ((63c6a95e-4e60-47b3-a529-23ca0f4ee525))
    • ((63c6a98a-46f9-4642-9c5d-ff1dc1ccf7a8)) New Motivations Very good example.

  • ((63c6abbb-f2be-4c69-aa2c-9bae5f2281e9))

  • STAKEHOLDERS covered by the project:
    • Data Providers
    • Business domain experts
    • Data scientists
    • Resource providers
    • DataOps operators
    • Data consumers
  • PIPELINE LIFECYCLE supported by the project:
    • Pipeline discovery
    • Pipeline definition
    • Pipeline simulation
    • Resource provisioning
    • Pipeline deployment
    • Pipeline adaptations