Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach
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Bibliographic record
Abstract
Content caching at base stations is a promising solution to address the large demands for mobile data services over cellular networks. Content caching is a challenging problem as it requires predicting the future popularity of the content and the operating characteristics of the cellular networks. In this paper, we focus on constructing an algorithm that improves the users' quality of experience (QoE) and reduces network traffic. The algorithm accounts for users' behavior and properties of the cellular network (e.g. cache size, bandwidth, and load). The constructed content and network aware adaptive caching scheme uses an extreme-learning machine neural network to estimate the popularity of content, and mixed-integer linear programming to compute where to place the content and select the physical cache sizes in the network. The proposed caching scheme simultaneously performs efficient cache deployment and content caching. Additionally, a simultaneous perturbation stochastic approximation method is developed to reduce the number of neurons in the extreme-learning machine method while ensuring a sufficient predictive performance is maintained. Using real-world data from YouTube and a NS-3 simulator, we demonstrate how the caching scheme improves the QoE of users and network performance compared with industry standard caching schemes.
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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 | 0.001 | 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 | 0.002 | 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 it