An energy-efficiency assessment of Content Centric Networking (CCN)
Bibliographic record
Abstract
Content-Centric Networking (CCN) is a recently proposed networking architecture that can potentially lead to reduced bandwidth usage and better scalability and security as compared to the current IP-based architecture. In this paper, we conduct an energy consumption analysis of content-centric networking and IP-based networking for a video streaming scenario. We consider two types of energy consumption: the energy required to manufacture the network devices and the energy required for operation. We perform simulations of content-centric networking over a general-tree topology to assess the traffic rate reductions achieved by CCN's insertion of caches at routers. Although CCN network devices have a higher intrinsic energy consumption compared to the IP-based devices because of the presence of additional memory, by exploiting their caching capabilities it is possible to reduce the overall energy consumption of the network. We consider both the incorporation of an online rate adaptation mechanism as well as a static network provisioning approach and observe that these approaches can lead to an energy consumption reduction of 10–20 percent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".