Optimizing Video Request Routing in Mobile Networks with Built-in Content Caching
Bibliographic record
Abstract
Built-in content caching in mobile core networks can help improve quality of service, reduce operation expenses, simplify inter-network cooperation, and thus is a promising approach for more efficient networking architectures. In addition to the complexity of content placement as revealed in the literature, routing video requests remains a challenging issue. Two problems must be addressed: (i) how to distribute video requests among multiple internal servers (i.e., server selection); and (ii) how to route so-generated video flows (i.e., flow routing). In this work, we jointly formulate these two problems with two traffic-engineering objectives considered, namely, minimizing maximum link utilization and minimizing total link cost. We develop fast algorithms to solve the problems with provable approximation guarantees. We then propose a hop-by-hop routing protocol, which implements the optimization solutions by generating a set of flow-splitting and routing decisions for each router/caching node. Simulation results show that our algorithms significantly outperform existing routing schemes under various system settings, reducing up to 68 percent of maximum link utilization and more than 50 percent of link cost, and supporting over 60 percent more of traffic load.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".