EcoStream: A Resource Utilization and Power Consumption Dataset in Multimedia Streaming for Sustainability Analysis
Bibliographic record
Abstract
Multimedia streaming has become essential in various applications, such as security, healthcare, and education. However, it is an operation that demands a high amount of resources from CPU, GPU, memory, and network. This creates the need to develop solutions to predict the amount of resources required to provision services to aid in better decision-making for functionalities such as adaptive video quality control and load balancing, thus ensuring the optimal quality of service (QoS) possible to users given any condition. The existing resource utilization prediction solutions in the literature tend to focus on applications related to cloud computing. However, serverbased architectures play a significant role in use cases where data privacy issues require data to remain on-premise such as security camera feeds. Due to the constrained resources within server-based architectures, accurately predicting resource utilization becomes imperative for optimal system performance. In this paper, we present a dataset of the utilization of resources including hardware, network, and power consumption of serverbased architectures in multimedia streaming. The dataset consists of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$37^{\prime} 800$</tex> different multimedia streaming use cases covering a variety of video resolutions, client numbers, and stream qualities. We detail the process used to collect the dataset and the parameters obtained from the involved systems, analyzing information that can be extracted from the data. Moreover, we establish a benchmark test by evaluating the performance of a plethora of regression models in predicting the resource utilization required from servers for multimedia streaming on a per-resource level pre-service provisioning, where we show that standard regression models can predict the utilization of resources with a root mean squared error of 2.08%, and that power utilization can also be predicted for both CPU and GPU with an error of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$<2$</tex> Watts. Finally, we evaluate multivariate systems' ability to predict the values of various parameters together, and show that with a deep learning model we can predict resource utilization and power consumption with an accuracy based on the mean absolute error of 94.42% for utilization and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$<2$</tex> Watts of power consumption error. The data collected on resource utilization can be found on the GitHub repo: https://github.com/talshoura/Resource-Utilization-of-Multimedia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".