Formally Verified Scalable Look Ahead Planning For Cloud Resource Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we propose and implement a distributed autonomic manager that maintains service level agreements (SLA) for each application scenario. The proposed autonomic manager supports SLAs by configuring the bandwidth ratios for each application scenario and uses an overlay network as an infrastructure. The most important aspect of the proposed autonomic manager is its scalability which allows us to deal with geographically distributed cloud-based applications and a large volume of computation. This can be useful in look ahead optimization and in adaptations using complex models, such as machine learning. We formally prove the safety and liveness properties of the implemented distributed algorithms. Through experiments on the Amazon AWS cloud, using two different use cases, we demonstrate the elasticity and flexibility of the autonomic manager as a measure of its applicability to different cloud applications with different types of workloads. Experiments also demonstrate that increasing the size of a look ahead window, up to a certain size, improves the accuracy of the adaptation decisions by up to 50%.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it