An Ensemble Clustering Approach for Modeling Hidden Categorization Perspectives for Cloud Workloads
Bibliographic record
Abstract
Abstract Effectively managing cloud resources is a complex task due to the interdependencies of various cloud-hosted services and applications. This task is integral to workload categorization, which groups similar cloud workloads to inform workload scheduling and resource management procedures. Although traditional clustering algorithms can categorize workloads into single data grouping patterns, the multifaceted nature of cloud workloads may conceal multiple valid categorization perspectives, indicating a need for a more adaptable clustering approach. This paper presents a novel ensemble clustering approach to enhance the scheduling workload categorization process in cloud computing. Our approach combines various normalization and transformation techniques, including principal component analysis, to form multiple data preprocessing pipelines. The data derived from these pipelines then serve as input for multiple base clustering learners. A novel combined score based on the Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index is then employed to select the optimal models and preprocessing setups. The clustering outcomes of these models are encoded and inputted into a meta-clustering algorithm, effectively capturing complex categorization perspectives. Evaluation of this approach using real-world workload trace data from Microsoft Azure significantly enhances workload segmentation efficacy, thereby improving resource management and quality of service in cloud data centers. This method offers a promising pathway toward improved workload clustering in cloud computing, demonstrating the practical utility of advanced ensemble clustering techniques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".