Bandwidth Provisioning in Cache-Enabled Software-Defined Mobile Networks: A Robust Optimization Approach
Bibliographic record
Abstract
Software-defined networking (SDN) and in-network caching are promising technologies in next generation wireless networks. In this paper, motivated by the flow control in SDN, we propose an approach to solve bandwidth provisioning problem in software-defined mobile networks (SDMNs) with jointly considering in-network cache under uncertain flow rate. We present a flow control problem supporting bandwidth provisioning while providing optimum forwarding strategies and resource allocation. Moreover, due to the centralized control mechanism, the information collected by the SDN controller may not be real- time or accurate. To deal with this uncertainty and fluctuation of flows rate, chance constraints are used to pose bandwidth provisioning. Specifically, with recent advances in robust optimization and approximation techniques, we formulate the flow control problem as a robust optimization problem and transform it to a convex problem, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".