CharmSeeker: Automated Pipeline Configuration for Serverless Video Processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Video processing plays an essential role in a wide range of cloud-based applications. It typically involves multiple pipelined stages, which well fits the latest fine-grained serverless computing paradigm if properly configured to match the cost and delay constraints of video. Existing configuration tools, however, are primarily developed for traditional virtual machine clusters with general workloads. This paper presents CharmSeeker, an automated configuration tuning tool for serverless video processing pipelines. We first carefully examine the key steps and the performance bottlenecks for video processing over modern serverless platforms. Then, we identify the configuration space for processing pipelines and leverage a carefully designed Sequential Bayesian Optimization search scheme to identify promising configurations. We further address the practical challenges toward integrating our solution into real-world systems and develop a prototype with AWS Lambda. Evaluation results show that CharmSeeker can find out the optimal or near-optimal configurations that improve the relative processing time up to 408.77%. It is also more robust and scalable to various video processing pipelines compared with state-of-the-art solutions.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it