Imagen de Portada

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

Guardado en:
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
Publicado:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Materias:
Data mining
Scheduling
Data processing
Inspection
Matching
Electronic devices
Cloud computing
Workflow
COMPASS (programming language)
Algorithms
Data transmission
Clusters
Completion time
Distributed processing
Computer networks
Queueing
Acceso en línea:Citation/Abstract
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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.
DOI:10.1109/CLUSTER51413.2022.00021
Fuente:Science Database