Incorporating Data Preparation and Clustering Techniques for Workload Segmentation in Large-Scale Cloud Data Centers
Bibliographic record
Abstract
The interconnected dependencies of various cloud-hosted services and applications make managing cloud resources more difficult. Observing fully operational virtual computing instances based on task profiles can aid in identifying workload characteristics. Scheduling and managing workloads may be optimized by tailoring selection and decision-making processes in response to workload segmentation. Cloud data center managers use models that group operations and tasks with similar structures, allowing for a more straightforward comparison of outcomes and improved cloud service performance and availability. However, conventional clustering algorithms can cluster the cloud workload into a single data grouping pattern. Since cloud workload data is open to diverse interpretations, several legitimate categories are hiding in the various data outlooks. Considering high-dimensional data, where several attribute profiles define each job, we provide a strategy for grouping cloud workloads at the task scheduling level. The proposed model is applied to a real-world data center workload, which includes a trace of virtual instances derived from the Microsoft Azure public dataset. Several data clustering methods and pipelines are examined and contrasted. The results show that different feature engineering methods used in data pipelines lead to various valid clustering schemes that can be used to improve the performance of workload segmentation in big cloud data centers.
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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 | 0.001 | 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 | 0.004 |
| 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".