An Optimal Peak Hour Content Server Cache Update Scheduling Algorithm for 5G HetNets
Why this work is in the frame
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Bibliographic record
Abstract
Most of the existing caching schemes assume that the pushing of popular contents from the macro base station (MBS) to content servers (CSs) is performed during off-peak hours when the network traffic is low. However, since popular files, such as breaking news, may also be generated during peak hours, performing CS content update during peak hours is necessary. In this paper, we propose an optimal cache content update scheduling algorithm for heterogeneous networks (HetNets). The decision-making module is located in the MBS. The action set includes performing CS content update, letting the CSs simultaneously serve user requests, and using the MBS to directly serve user requests. The MBS aims to maximize the total throughput of the system within the duration of the peak hour under the uncertainty of the arrival of new user requests and the addition of new files. We formulate the peak hour CS cache content update scheduling problem as a Markov decision process and propose an optimal cache content update scheduling algorithm based on dynamic programming. We perform simulations and compare our proposed optimal scheduling algorithm with the periodic update and greedy scheduling heuristics. Simulation results show that our proposed algorithm outperforms those two heuristics under different scenarios.
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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 | 0.000 | 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 | 0.000 | 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