An Optimal Peak Hour Content Server Cache Update Scheduling Algorithm for 5G HetNets
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".