VEEP: Video Encoding Energy and CO2 Emission Prediction
ACM MMsys, GMSys workshop (2024)
15-18 April, 2024 in Bari, Italy
[PDF], [Slides]
Manuel Hoi* (AAU, Austria), Armin Lachini* (AAU, Austria), Samira Afzal (AAU, Austria), Sandro Linder (AAU, Austria), Farzad Tashtarian (AAU, Austria), Radu Prodan (AAU, Austria), and Christian Timmerer (AAU, Austria)
*These authors contributed equally to this work
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 × 10−5, and a mean squared error (MSE) of 1.67 × 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.
