Adaptive auto-scaling for virtual resources in software-defined infrastructure
Bibliographic record
Abstract
Auto-scaling is a key challenge and benefit in cloud computing infrastructures where applications are deployed on one or more virtual machines (VMs) to balance efficiency in use against delivered performance. In different scenarios, there may be a need for either horizontal or vertical scaling. Therefore, scaling is an important operation of cloud management systems. One way to enable scaling as an automated service is to use a model to predict the VM's future state as a function of time. However, this method is not completely feasible, because the performance of a VM is so dynamic and depends on many parameters. A simpler approach to enable auto-scaling is to use real-time utilization data of VM's and a set of fixed thresholds to execute scaling when thresholds are crossed. However, this method is prone to false positive decisions. Uses this paper, we propose an adaptive method that uses threshold-based mechanisms to control the auto-scaling process and leverages the accuracy and precision given by threshold-based methods to reduce the number of false positives. We present performance results, comparing the fixed threshold methods with our proposed method. We show that our method frequently correctly triggers scaling process in situations where fixed threshold based measurement methods fail.
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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 | 0.010 |
| 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 | 0.001 | 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".