Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum

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Publicado en:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2022)
Autor principal: Mehran, Narges
Otros Autores: Samani, Zahra Najafabadi, Kimovski, Dragi, Prodan, Radu
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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024 7 |a 10.1109/CLUSTER51413.2022.00021  |2 doi 
035 |a 2726129521 
045 2 |b d20220101  |b d20221231 
084 |a 228229  |2 nlm 
100 1 |a Mehran, Narges  |u Institute of Information Technology, Alpen-Adria-Universität,Klagenfurt,Austria 
245 1 |a Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum 
260 |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  |c 2022 
513 |a Conference Proceedings 
520 3 |a Conference Title: 2022 IEEE International Conference on Cluster Computing (CLUSTER)Conference Start Date: 2022, Sept. 5 Conference End Date: 2022, Sept. 8 Conference Location: Heidelberg, GermanyToday's distributed computing infrastructures en-compass complex workflows for real-time data gathering, transferring, storage, and processing, quickly overwhelming centralized cloud centers. Recently, the computing continuum that federates the Cloud services with emerging Fog and Edge devices represents a relevant alternative for supporting the next-generation data processing workflows. However, eminent challenges in automating data processing across the computing continuum still exist, such as scheduling heterogeneous devices across the Cloud, Fog, and Edge layers. We propose a new scheduling algorithm called C3 -MATCH, based on matching theory principles, involving two sets of players negotiating different utility functions: 1) workflow microservices that prefer computing devices with lower data processing and queuing times; 2) computing continuum devices that prefer microservices with corresponding resource requirements and less data transmission time. We evaluate $C^{3}$ -MATCH using real-world road sign inspection and sentiment analysis workflows on a federated computing continuum across four Cloud, Fog, and Edge providers. Our combined simulation and real execution results reveal that $C^{3}$ -MATCH achieves up to 67% lower completion time than three state-of-the-art methods with 10 ms-1000 ms higher transmission time. 
653 |a Data mining 
653 |a Scheduling 
653 |a Data processing 
653 |a Inspection 
653 |a Matching 
653 |a Electronic devices 
653 |a Cloud computing 
653 |a Workflow 
653 |a COMPASS (programming language) 
653 |a Algorithms 
653 |a Data transmission 
653 |a Clusters 
653 |a Completion time 
653 |a Distributed processing 
653 |a Computer networks 
653 |a Queueing 
700 1 |a Samani, Zahra Najafabadi  |u Institute of Information Technology, Alpen-Adria-Universität,Klagenfurt,Austria 
700 1 |a Kimovski, Dragi  |u Institute of Information Technology, Alpen-Adria-Universität,Klagenfurt,Austria 
700 1 |a Prodan, Radu  |u Institute of Information Technology, Alpen-Adria-Universität,Klagenfurt,Austria 
773 0 |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings  |g (2022) 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2726129521/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch