A Clustering-based Consistency Adaptation Strategy for Distributed SDN\n Controllers
Bibliographic record
Abstract
Distributed controllers are oftentimes used in large-scale SDN deployments\nwhere they run a myriad of network applications simultaneously. Such\napplications could have different consistency and availability preferences.\nThese controllers need to communicate via east/west interfaces in order to\nsynchronize their state information. The consistency and the availability of\nthe distributed state information are governed by an underlying consistency\nmodel. Earlier, we suggested the use of adaptively-consistent controllers that\ncan autonomously tune their consistency parameters in order to meet the\nperformance requirements of a certain application. In this paper, we examine\nthe feasibility of employing adaptive controllers that are built on-top of\ntunable consistency models similar to that of Apache Cassandra. We present an\nadaptation strategy that uses clustering techniques (sequential k-means and\nincremental k-means) in order to map a given application performance indicator\ninto a feasible consistency level that can be used with the underlying tunable\nconsistency model. In the cases that we modeled and tested, our results show\nthat in the case of sequential k-means, with a reasonable number of clusters\n(>= 50), a plausible mapping (low RMSE) could be estimated between the\napplication performance indicators and the consistency level indicator. In the\ncase of incremental k-means, the results also showed that a plausible mapping\n(low RMSE) could be estimated using a similar number of clusters (>= 50) by\nusing a small threshold (~$ 0.01).\n
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".