Resource Auto-Scaling and Sparse Content Replication for Video Storage Systems
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
Many video-on-demand (VoD) providers are relying on public cloud providers for video storage, access, and streaming services. In this article, we investigate how a VoD provider may make optimal bandwidth reservations from a cloud service provider to guarantee the streaming performance while paying for the bandwidth, storage, and transfer costs. We propose a predictive resource auto-scaling system that dynamically books the minimum amount of bandwidth resources from multiple servers in a cloud storage system to allow the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of different videos for statistical multiplexing to hedge the risk of under-provisioning. The optimal load direction from video channels to cloud servers without replication constraints is derived with provable performance. We further study the joint load direction and sparse content placement problem that aims to reduce bandwidth reservation cost under sparse content replication requirements. We propose several algorithms, and especially an iterative L 1 -norm penalized optimization procedure, to efficiently solve the problem while effectively limiting the video migration overhead. The proposed system is backed up by a demand predictor that forecasts the expectation, volatility, and correlation of the streaming traffic associated with different videos based on statistical learning. Extensive simulations are conducted to evaluate our proposed algorithms, driven by the real-world workload traces collected from a commercial VoD system.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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