An autoscalable approach to optimize energy consumption using smart meters data in serverless computing

An autoscalable approach to optimize energy consumption using smart meters data in serverless computing

Serverless computing has evolved as a prominent paradigm within cloud computing, providing on-demand resource provisioning and capabilities crucial to Science and Technology for Energy Transition (STET) applications. Despite the efficiency of the auto-scalable approaches in optimizing performance an...

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Bibliographic Details
Main Authors: Kaur Jasmine, Chana Inderveer, Bala Anju
Format: Article
Language:English
Published: EDP Sciences 2024-01-01
Series:Science and Technology for Energy Transition
Subjects:
serverless computing
autoscaling
q-learning
performance
energy consumption
Online Access:https://www.stet-review.org/articles/stet/full_html/2024/01/stet20240095/stet20240095.html
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