A game theoretic approach for energy-efficient in-network caching in content-centric networks
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Bibliographic record
Abstract
Recently, content-centric networking (CCN) has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Consequently, the energy consumption aspect of CCN is largely ignored. In this paper, we propose a distributed energy-efficient in-network caching scheme for CCN, where each content router only needs locally available information to make caching decisions considering both caching energy consumption and transport energy consumption. We formulate the in-network caching problem as a non-cooperative game. Through rigorous mathematical analysis, we prove that pure strategy Nash equilibria exist in the proposed scheme, and it always has a strategy profile that implements the socially optimal configuration, even if the routers are self-interested in nature. Simulation results are presented to show that the distributed solution is competitive to the centralized scheme, and has superior performance compared to other popular caching schemes in CCN. Besides, it exhibits a fast convergence speed when the capacity of content routers varies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it