Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".