Cache Management in Information-Centric Networks using Convolutional Neural Network
Bibliographic record
Abstract
Traditional cache algorithms for information centric networks cache every seen item and rely on simple statistics, such as usage recency, to determine the target for replacement. While they result in a simple and efficient O(1) algorithms, such simple statistics perform poorly when dealing with realistic request patterns, that are non-uniform and that often exhibit daily, weekly and monthly seasonality. As the computational power of processors increased dramatically over the years, it is worth trying to use more complex cache algorithms in order to yield better performance. In this paper, we investigate the use of convolutional neural networks as a means to capture the seasonal patterns in the requests stream, and propose a cache algorithm that is able to consider seasonality and make decisions accordingly. We implement our cache algorithm by building an interface to the real-world ICN forwarder from the CICN open source project, the so-called Metis forwarder; and conduct our experiments on a real-world dataset representing the access statistics of Wikipedia to demonstrate the effectiveness of our algorithm.
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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 | 0.001 |
| 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".