A Bee Colony-based Algorithm for Micro-cache Placement Close to End Users in Fog-based Content Delivery Networks
Why this work is in the frame
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Bibliographic record
Abstract
Fog-based Content Delivery Networks (CDNs) distribute contents from origin servers to cloud replica servers and to fog caches. Some of these fog caches may be located on Set-top Boxes (STBs) close to end users, assuming that the STBs can host micro-caches. This can greatly reduce Latency. Network function virtualization (NFV) is a technology that can be used in fog systems. NFV-enabled STBs can therefore host micro-caches implemented as virtual network functions (VNFs). Appropriate placement mechanisms are needed to place these micro-caches to improve end-users' QoS in terms of Latency while still minimizing cost. In this paper, this placement problem is modeled as an optimization problem. A bee colony-based algorithm is suggested to find the placement that minimizes an aggregation of Latency and micro-cache storage cost. The simulation results show an improvement in the aggregated Latency and cost when the proposed algorithm is deployed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it