Caching in Dynamic Environments: A Near-Optimal Online Learning Approach
Bibliographic record
Abstract
The rapid growth of rich multimedia data in today’s Internet, especially video traffic, has challenged the content delivery networks (CDNs). Caching serves as an important means to reduce user access latency so as to enable faster content downloads. Motivated by the dynamic nature of the real-world edge traces, this paper introduces aprovably wellonline caching policy in dynamic environments where: 1) the popularity is highly dynamic; 2) no regular stochastic pattern can model this dynamic evaluation process. First, we design an online optimization framework, which aims to minimize thedynamic regretthat finds the distance between an online caching policy and the best dynamic policy in hindsight. Second, we propose a dynamic online learning method to solve the non-stationary caching problem formulated in the previous framework. Compared to the linear dynamic regret of previous methods, our proposal is proved to achieve asublinear dynamic regret, from which it is guaranteed to be nearly optimal. We verify the design using both synthetic and real-world traces: the proposed policy achieves the best performance in the synthetic traces with different levels of dynamicity, which verifies the dynamic adaptation; our proposal consistently achieves at least 9.4% improvement than the baselines, including LRU, LFU, Static Online Learning based replacement, and Deep Reinforcement Learning based replacement, in random edge areas from real-world traces (from iQIYI), further verifying the effectiveness and robustness on the edge.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".