Reverse Auction-Based Dynamic Caching and Pricing Scheme in Producer-Driven ICN
Bibliographic record
Abstract
Dynamic cache allocation and pricing in Information-Centric Networks (ICNs) is a challenging problem, especially when multiple content producers and competing ICN cache service providers are involved. Many existing ICN caching schemes generalize their frameworks as producer-agnostic architectures while considering a single ICN cache service provider. Realistically, as ICNs grow, multiple cache providers will compete for valuable content that would generate higher cache hits, and the ecosystem will inevitably become market-driven. In this paper, we investigate the dynamic cache allocation and price determination problem considering a caching system consisting of multiple content producers who act as the buyers and multiple competing ICN cache providers who act as the sellers of the caching resources. We propose a novel reverse auction-based caching and pricing scheme named SEMRA that aims to maximize the caching benefits of content producers. Simulation results demonstrate how the proposed scheme improved ICN caching over several caching metrics across varying cache sizes and popularity skewness values. Future work in this domain is highlighted in the conclusion.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".