Authors:
Samira Afzal, Christian Bauer, Sandro Linder, Armin Lachini, Manuel Hoi, Daniele Lorenzi, Farzad Tashtarian, Hadi Amirpour, Radu Prodan, Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)
Editors:
Tobias Hoßfeld (University of Würzburg, Germany)
Christian Timmerer (Alpen-Adria-Universität Klagenfurt and Bitmovin Inc., Austria)
Abstract: Energy efficiency has become a crucial aspect of today’s IT infrastructures, and video (streaming) accounts for over half of today’s Internet traffic. This column highlights open-source tools, datasets, and solutions addressing energy efficiency in video streaming presented at ACM Multimedia Systems 2024 and its co-located workshop ACM Green Multimedia Systems.
Introduction
Across various platforms, users seek the highest Quality of Experience (QoE) in video communication and streaming. Whether it’s a crucial business meeting or a relaxing evening of entertainment, individuals desire seamless and high-quality video experiences. However, meeting this demand for high-quality video comes with a cost: increased energy usage [1],[2]. This energy consumption occurs at every stage of the process, including content provision via cloud services and consumption on end users’ devices [3]. Unfortunately, this heightened energy consumption inevitably leads to higher CO2 emissions (except for renewable energy sources), posing environmental challenges. It emphasizes the need for studies to assess the carbon footprint of video streaming.
Content provision is a critical stage in video streaming, involving encoding videos into various formats, resolutions, and bitrates. Encoding demands computing power and energy, especially in cloud-based systems. Cloud computing has become famous for video encoding due to its scalability [4] to adjust cloud resources to handle changing workloads and flexibility [5] to scale their operations based on demand. However, this convenience comes at a cost. Data centers, the heart of cloud computing, consume a significant portion of global electricity, around 3% [6]. Video encoding is one of the biggest energy consumers within these data centers. Therefore, optimizing video encoding for lower energy consumption is crucial for reducing the environmental impact of cloud-based video delivery.
Content consumption [7] involves the device using the network interface card to request and download video segments from the server, decompressing them for playback, and finally rendering the decoded frames on the screen, where the energy consumption depends on the screen technology and brightness settings.
The GAIA project showcased its research on the environmental impact of video streaming at the recent 15th ACM Multimedia Systems Conference (April 15-18, Bari, Italy). We presented our findings at relevant conference sessions: Open-Source Software and Dataset and the Green Multimedia Systems (GMSys) workshop.
Open Source Software
GREEM: An Open-Source Benchmark Tool Measuring the Environmental Footprint of Video Streaming [PDF] [Github] [Poster]
GREEM (Gaia Resource Energy and Emission Monitoring) aims to measure energy usage during video encoding and decoding processes. GREEM tracks the effects of video processing on hardware performance and provides a suite of analytical scenarios. This tool offers easy-to-use scenarios covering the most common video streaming situations, such as measuring sequential and parallel video encoding and decoding.
Contributions:
- Accessible: GREEM is available in a GitHub repository (https://github.com/cd-athena/GREEM) for energy measurement of video processing.
- Automates experimentation: It allows users to easily configure and run various encoding scenarios with different parameters to compare results.
- In-depth monitoring: The tool traces numerous hardware parameters, specifically monitoring energy consumption and GPU metrics, including core and memory utilization, temperature, and fan speed, providing a complete picture of video processing resource usage.
- Visualization: GREEM offers scripts that generate analytic plots, allowing users to visualize and understand their measurement results easily.
Verifiable: GREEM empowers researchers with a tool that has earned the ACM Reproducibility Badge, which allows others to reproduce the experiments and results reported in the paper.
Open Source Datasets
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances [PDF] [Github] [Poster]
As video encoding increasingly shifts to cloud-based services, concerns about the environmental impact of massive data centers arise. The Video Encoding Energy and CO2 Emissions Dataset (VEED) provides the energy consumption and CO2 emissions associated with video encoding on Amazon’s Elastic Compute Cloud (EC2) instances. Additionally, VEED goes beyond energy consumption as it also captures encoding duration and CPU utilization.
Contributions:
- Findability: A comprehensive metadata description file ensures VEED’s discoverability for researchers.
- Accessibility: VEED is open for download on GitHub (https://github.com/cd-athena/VEEDdataset), removing access barriers for researchers. Core findings in the research that leverages the VEED dataset have been independently verified (ACM Reproducibility Badge).
- Interoperability: The dataset is provided in a comma-separated value (CSV) format, allowing integration with various analysis applications.
- Reusability: Description files empower researchers to understand the data structure and context, facilitating its use in diverse analytical projects.
COCONUT: Content Consumption Energy Measurement Dataset for Adaptive Video Streaming [PDF] [Github]
COCONUT is a dataset comprising the energy consumption of video streaming across various devices and different HAS (HTTP Adaptive Streaming) players. COCONUT captures user data during MPEG-DASH video segment streaming on laptops, smartphones, and other client devices, measuring energy consumption at different stages of streaming, including segment retrieval through the network interface card, video decoding, and rendering on the device.This paper has been designated the ACM Artifacts Available badge, signifying that the COCONUT dataset is publicly accessible. COCONUT can be accessed at https://athena.itec.aau.at/coconut/.
Second International ACM Green Multimedia Systems Workshop — GMSys 2024
VEEP: Video Encoding Energy and CO2 Emission Prediction [pdf] [slides]
In VEEP, a machine learning (ML) scheme that empowers users to predict the energy consumption and CO2 emissions associated with cloud-based video encoding.
Contributions:
- Content-aware energy prediction: VEEP analyzes video content to extract features impacting encoding complexity. This understanding feeds an ML model that accurately predicts the energy consumption required for encoding the video on AWS EC2 instances. (High Accuracy: Achieves an R² score of 0.96)
- Real-time carbon footprint: VEEP goes beyond energy. It also factors in real-time carbon intensity data based on the location of the cloud instance. This allows VEEP to calculate the associated CO2 emissions for your encoding tasks at encoding time.
- Resulting impact: By carefully selecting the type and location of cloud instances based on VEEP’s predictions, CO2 emissions can be reduced by up to 375 times. This significant reduction signifies VEEP’s potential to contribute to greener video encoding.
Conclusions
This column provided an overview of the GAIA project’s research on the environmental impact of video streaming, presented at the 15th ACM Multimedia Systems Conference. GREEM measurement tool empowers developers and researchers to measure the energy and CO2 emissions of video processing. VEED provides valuable insights into energy consumption and CO2 emissions during cloud-based video encoding on AWS EC2 instances. COCONUT sheds light on energy usage during video playback on various devices and with different players, aiding in optimizing client-side video streaming. Furthermore, VEEP, a machine learning framework, takes energy efficiency a step further. It allows users to predict energy consumption and CO2 emissions associated with cloud-based video encoding, allowing users to select cloud instances that minimize environmental impact. These studies can help researchers, developers, and service providers to optimize video streaming for a more sustainable future. The focus on encoding and playback highlights the importance of a holistic approach considering the entire video streaming lifecycle. While these papers primarily focus on the environmental impact of video streaming, a strong connection exists between energy efficiency and QoE [8],[9],[10]. Optimizing video processing for lower energy consumption can sometimes lead to trade-offs regarding video quality. Future research directions could explore techniques for optimizing video processing while ensuring a consistently high QoE for viewers.
References
[1] A. Katsenou, J. Mao, and I. Mavromatis, “Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs.” arXiv, Oct. 02, 2022. Accessed: Oct. 06, 2022. [Online]. Available: http://arxiv.org/abs/2210.00618
[2] H. Amirpour, V. V. Menon, S. Afzal, R. Prodan, and C. Timmerer, “Optimizing video streaming for sustainability and quality: The role of preset selection in per-title encoding,” in 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2023, pp. 1679–1684. Accessed: May 05, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10219577/
[3] S. Afzal, R. Prodan, C. Timmerer, “Green Video Streaming: Challenges and Opportunities – ACM SIGMM Records.” Accessed: May 05, 2024. [Online]. Available: https://records.sigmm.org/2023/01/08/green-video-streaming-challenges-and-opportunities/
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[5] B. Zeng, Y. Zhou, X. Xu, and D. Cai, “Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets,” Appl. Energy, vol. 357, p. 122406, Mar. 2024, doi: 10.1016/j.apenergy.2023.122406.
[6] M. Law, “Energy efficiency predictions for data centers in 2023.” Accessed: May 03, 2024. [Online]. Available: https://datacentremagazine.com/articles/efficiency-to-loom-large-for-data-centre-industry-in-2023
[7] C. Yue, S. Sen, B. Wang, Y. Qin, and F. Qian, “Energy considerations for ABR video streaming to smartphones: Measurements, models and insights,” in Proceedings of the 11th ACM Multimedia Systems Conference, 2020, pp. 153–165, doi: 10.1145/3339825.3391867.
[8] G. Bingöl, A. Floris, S. Porcu, C. Timmerer, and L. Atzori, “Are Quality and Sustainability Reconcilable? A Subjective Study on Video QoE, Luminance and Resolution,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2023, pp. 19–24. Accessed: May 06, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10178513/
[9] G. Bingöl, S. Porcu, A. Floris, and L. Atzori, “An Analysis of the Trade-Off Between Sustainability and Quality of Experience for Video Streaming,” in 2023 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, 2023, pp. 1600–1605. Accessed: May 06, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10283614/
[10] C. Herglotz, W. Robitza, A. Raake, T. Hossfeld, and A. Kaup, “Power Reduction Opportunities on End-User Devices in Quality-Steady Video Streaming.” arXiv, May 24, 2023. doi: 10.48550/arXiv.2305.15117.