Cloud Service Level planning under burstiness
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
Cloud Service Providers (SPs) need tools to help them plan their infrastructure capacity and decide on Service Level Agreements (SLAs) with their customers prior to deploying customer's applications. Existing approaches have not considered several important challenges such as workload burstiness, workload uncertainty, and scalability to large number of applications. This paper proposes a trace-based framework to address these challenges simultaneously. The core of the framework is a novel Resource Allocation Planning (RAP) methodology which allows SPs to take into account workload burstiness in Service Level Planning (SLP). This methodology works in consort with a Monte Carlo simulation technique to systematically consider the impact of workload uncertainty in SLP. Furthermore, we propose a novel burstiness-aware clustering technique that groups applications with similar workload characteristics to improve the scalability of the SLP framework. Results show that our approach can yield near optimal resource allocations and can achieve lower SLO violations with fewer resources than competing approaches, especially for bursty workloads. Furthermore, our proposed clustering technique is able to improve the scalability of our SLP framework without significantly impacting resource allocation accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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