Generic SDE and GA-based workload modeling for cloud systems
Bibliographic record
Abstract
Workload models are typically built based on user and application behavior in a system, limiting them to specific domains. Undoubtedly, such a practice creates a dilemma in a cloud computing (cloud) environment, where a wide range of heterogeneous applications are running and many users have access to these resources. The workload model in such an infrastructure must adapt to the evolution of the system configuration parameters, such as job load fluctuation. The aim of this work is to propose an approach that generates generic workload models (1) which are independent of user behavior and the applications running in the system, and can fit any workload domain and type, (2) model sharp workload variations that are most likely to appear in cloud environments, and (3) with high degree of fidelity with respect to observed data, within a short execution time. We propose two approaches for workload estimation, the first being a Hull-White and Genetic Algorithm (GA) combination, while the second is a Support Vector Regression (SVR) and Kalman-filter combination. Thorough experiments are conducted on real CPU and throughput datasets from virtualized IP Multimedia Subsystem (IMS), Web and cloud environments to study the efficiency of both propositions. The results show a higher accuracy for the Hull-White-GA approach with marginal overhead over the SVR-Kalman-Filter combination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".