A distributed controller for a virtualized router
Bibliographic record
Abstract
Abstract In this paper, a distributed controller for a virtualized router is proposed. This controller enables the dynamic and automatic resource allocation between the different virtual routers (called slices) running on top of the physical router. The controller is designed on a two-layer architecture. A slice controller (one for each slice) estimates the relationship between the past performances and resource allocations of the slice using a linear model, and then determines the requested allocation for the slice to meet its target performance. The physical router consists of a set of modular linecards. A resource controller (one for each linecard), collects the resource allocation requests from the different slices using the resources it controls and determines the allocations based on the available capacities of the resources. Resources are allocated to slices to guarantee their target performances if possible, or provide service differentiation if the total requests from all the slices exceeds the capacities of the shared resources. We have found that the convergence of the controller depends on different parameters (such as the number of slices and the parameters of the linear model) and therefore some tuning of these parameters is needed for the system to achieve the stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".