Edge-Enhanced QoS Aware Compression Learning for Sustainable Data Stream Analytics

Existing Cloud systems involve large volumes of data streams being sent to a centralised data centre for monitoring, storage and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns. However, Machine Learning (ML) algorithms typ...

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Veröffentlicht in:	IEEE transactions on sustainable computing Jg. 8; H. 3; S. 1 - 17
Hauptverfasser:	Amaizu, Maryleen Uluaku, Ali, Muhammad, Anjum, Ashiq, Liu, Lu, Liotta, Antonio, Rana, Omer
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Biological system modeling Cloud Computing Computational efficiency Computational modeling Computing costs Data centers Data compression Data models Data transfer (computers) Data transmission Deep Autoencoders Edge Computing Machine learning Network latency Quality of service Quality of service architectures Real-time analytics Search methods Task analysis Transmission Optimisation
ISSN:	2377-3782, 2377-3790
Online-Zugang:	Volltext
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