Bibliographic record
Abstract
Recently, attributed graphs have been extensively employed in modeling, studying and analyzing complex interactions in real world systems. A myriad of techniques have been proposed to partition these graphs into clusters that exhibit small entropy with respect to both compositional attributes and the structural properties of the graph. In cloud network infrastructures, they play an important role to understand end users, compute nodes and their interactions. One of the main challenges in today's large scale cloud infrastructures is to categorize these compute nodes into clusters that share similar attributes. Existing unsupervised machine learning techniques such as k-Means and DBSCAN, are inadequate to partition large scale computer network infrastructures due to their non suitability for such contexts and their algorithmic complexities that prevent them from being scalable to such sizes in a reasonable time. In this paper, we first formulate the problem of partitioning attributed graphs in the context of cloud infrastructures as a Quadratic Assignment Problem to solve small to medium scale instances and show its NP-Hardness. We then propose Cheetah a fast and scalable multi-objective topology-aware unsupervised machine learning technique that is tailored to effectively partition large scale cloud network infrastructures. Yet, in terms of complexity, Cheetah is linear as it leverages Breadth First Search algorithm. Experimental results demonstrate its ability to quickly construct good-quality clusters (≈ 1.63 seconds) given 1000 nodes compared to K-Means (≈ 2.78 seconds) and DBSCAN (≈ 24.76 seconds), respectively, and reveal its suitability for large scale infrastructures making it an appealing solution to be integrated into orchestration systems.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".