Proactive Caching to support mobility in Named Data Networks
Bibliographic record
Abstract
Information-centric Networks (ICNs) offer a promising paradigm for the future Internet to cope with an ever increasing growth in data and shifts in access models. Different architectures of ICNs, including Named Data Networks (NDNs) are designed around content distribution, where data is the core entity in the network instead of hosts. Given the amount of forecast traffic by mobile users, supporting mobility in NDNs to maintain seamless operation is one of the main challenges yet to be resolved. Accordingly, attempts at handling mobility in NDNs in the literature are mostly studied under simplistic or special cases, and relied on content retransmission as a fallback. This is in addition to the lack of benchmarking tools to analyze and compare such schemes. In this thesis, we investigate how predicting the future state of the network can enable seamless support of mobility in NDN. We propose a set of proactive benchmark solutions which exploit location and data traffic prediction to deliver the content of mobile users (both Consumers and Producers) under application delay constraints. Particularly, the network detects roaming users and caches their prospective content ahead of handover events while considering the maximum tolerable delay and network overheads. Unlike existing literature that focused solely on Consumer mobility, we also handle Producer mobility that impacts the content availability and Quality of Service (QoS). Furthermore, we introduce a practical mobility management scheme that is resilient to prediction uncertainties using stochastic optimization. A guided heuristic search algorithm is also developed to provide real-time near-optimal caching decisions instead of commercial solvers that suffer from poor scalability. All benchmark and heuristic schemes proposed in this thesis are evaluated using a comprehensive assessment framework, which is also used to assess the state-of-the-art NDN mobility support. Simulation results show that our proposed solutions maintain user’s QoS during mobility events. 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 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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".