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Pregled bibliografske jedinice broj: 1277675

Big data BPMN workflow resource optimization in the cloud


Simić, Srđan Daniel; Tanković, Nikola; Etinger, Darko
Big data BPMN workflow resource optimization in the cloud // Parallel computing, In Press (2023), 103025, 11 doi:10.1016/j.parco.2023.103025 (međunarodna recenzija, članak, znanstveni)


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Naslov
Big data BPMN workflow resource optimization in the cloud

Autori
Simić, Srđan Daniel ; Tanković, Nikola ; Etinger, Darko

Izvornik
Parallel computing (0167-8191) In Press (2023); 103025, 11

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
BPMN ; Cloud resource allocation ; Optimization ; Big data workflow ; Run-time distribution

Sažetak
Cloud computing is one of the critical technologies that meet the demand of various businesses for the high-capacity computational processing power needed to gain knowledge from their ever-growing business data. When utilizing cloud computing resources to deal with Big Data processing, companies face the challenge of determining the optimal use of resources within their business processes. The miscalculation of the necessary resources directly affects their budget and can cause delays in the cycle time of their key processes. This study investigates the simulation of cloud resource optimization for Big Data workflows modeled with the Business Process Modeling Notation (BPMN). To this end, a BPMN performance evaluation framework was developed. The framework’s capabilities were presented using real-world data science workflow and later evaluated on workflows consisting of 13, 52, and 104 tasks. The results show that the developed framework is adequate for estimating the overall run-time distribution and optimizing the cloud resource deployment and that the BPMN can be utilized for Big Data processing workflows. Therefore, this study contributes to BPMN practitioners by providing a tool to apply BPMN for their Big Data workflows and decision- makers by giving them critical insights into their key business processes. The framework source code is available at https://github.com/ntankovic/python-bpmn-engine.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Sveučilište Jurja Dobrile u Puli

Poveznice na cjeloviti tekst rada:

doi doi.org www.sciencedirect.com

Citiraj ovu publikaciju:

Simić, Srđan Daniel; Tanković, Nikola; Etinger, Darko
Big data BPMN workflow resource optimization in the cloud // Parallel computing, In Press (2023), 103025, 11 doi:10.1016/j.parco.2023.103025 (međunarodna recenzija, članak, znanstveni)
Simić, S., Tanković, N. & Etinger, D. (2023) Big data BPMN workflow resource optimization in the cloud. Parallel computing, In Press, 103025, 11 doi:10.1016/j.parco.2023.103025.
@article{article, author = {Simi\'{c}, Sr\djan Daniel and Tankovi\'{c}, Nikola and Etinger, Darko}, year = {2023}, pages = {11}, DOI = {10.1016/j.parco.2023.103025}, chapter = {103025}, keywords = {BPMN, Cloud resource allocation, Optimization, Big data workflow, Run-time distribution}, journal = {Parallel computing}, doi = {10.1016/j.parco.2023.103025}, volume = {In Press}, issn = {0167-8191}, title = {Big data BPMN workflow resource optimization in the cloud}, keyword = {BPMN, Cloud resource allocation, Optimization, Big data workflow, Run-time distribution}, chapternumber = {103025} }
@article{article, author = {Simi\'{c}, Sr\djan Daniel and Tankovi\'{c}, Nikola and Etinger, Darko}, year = {2023}, pages = {11}, DOI = {10.1016/j.parco.2023.103025}, chapter = {103025}, keywords = {BPMN, Cloud resource allocation, Optimization, Big data workflow, Run-time distribution}, journal = {Parallel computing}, doi = {10.1016/j.parco.2023.103025}, volume = {In Press}, issn = {0167-8191}, title = {Big data BPMN workflow resource optimization in the cloud}, keyword = {BPMN, Cloud resource allocation, Optimization, Big data workflow, Run-time distribution}, chapternumber = {103025} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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