Robust Proactive Mobility Management in Named Data Networking under Erroneous Content Prediction
Bibliographic record
Abstract
Named Data Networking (NDN) is a promising paradigm for the future Internet to survive the growing data demand. Supporting seamless operation during user mobility is one of the main challenges in NDN. In this paper, we investigate optimal caching for producer mobility under prediction uncertainties. Mainly, we propose a stochastic optimization framework that exploits location and data requests' predictors to cache data proactively before handover. We model the problem using Chance Constraint Programming (CCP) that probabilistically incorporates the uncertainty in data prediction and models the trade-off between network overhead and Consumer satisfaction. A deterministic formulation is derived to obtain a closed form Integer Linear Programming model based on the prediction error model. The proposed framework is then implemented in ndnSIM and Gurobi, and simulation experiments are conducted to provide benchmark solutions for robust proactive caching. The results show that such robust scheme satisfies the consumers' quality of experience under imperfect prediction of future content requested from mobile producers. Hence, sustains the prediction gains over conventional non- predictive schemes without compromising the network overhead. We believe that such results drive incentives for deploying proactive mobility management in future NDN.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| 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".