VEEP: Video Encoding Energy and CO2 Emission Prediction
Authors: 
Armin Lachini, Manuel Hoi, Samira Afzal, Sandro Linder, 
Farzad Tashtarian, Radu Prodan, 
Christian TimmererAuthors Info & Claims
Armin Lachini, Manuel Hoi, Samira Afzal, Sandro Linder, Farzad Tashtarian, Radu Prodan, Christian TimmererAuthors Info & ClaimsPages 16 - 21
Abstract
In the context of rising environmental concerns, this paper introduces VEEP, an architecture designed to predict energy consumption and CO2 emissions in cloud-based video encoding. VEEP combines video analysis with machine learning (ML)-based energy prediction and real-time carbon intensity, enabling precise estimations of CPU energy usage and CO2 emissions during the encoding process. It is trained on the Video Complexity Dataset (VCD) and encoding results from various AWS EC2 instances. VEEP achieves high accuracy, indicated by an R2-score of 0.96, a mean absolute error (MAE) of 2.41 x 10-5, and a mean squared error (MSE) of 1.67 x 10-9. An important finding is the potential to reduce emissions by up to 375 times when comparing cloud instances and their locations. These results highlight the importance of considering environmental factors in cloud computing.
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Index Terms
- VEEP: Video Encoding Energy and CO2 Emission Prediction
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Published In
April 2024
33 pages
ISBN:9798400706172
DOI:10.1145/3652104
- Workshop Chairs:
- Samira Afzal,
- Robert Seeliger,
- Christian Herglotz
Copyright © 2024 Owner/Author(s).
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 15 April 2024
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- Research-article
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- Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association
- ustrian Research Promotion Agency (FFG)
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GMSys '24
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