Chorus: model knowledge base for performance modeling in datacenters
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
Due to the imperative need to reduce the management costs, operators multiplex several concurrent applications in large datacenters. However, uncontrolled resource sharing between co-hosted applications often results in performance degradation problems, thus creating violations of service level agreements (SLAs) for service providers. Therefore, in order to meet per-application SLAs, per-application performance modeling for dynamic resource allocation in shared resource environments has recently become promising. We introduce Chorus, an interactive performance modeling framework for building application performance models incrementally and on the fly. It can be used to support complex, multi-tier resource allocation, and/or what-if performance inquiry in modern datacenters, such as Clouds. Chorus consists of (i) a declarative high-level language for providing semantic model guidelines, such as model templates, model functions, or sampling guidelines, from a sysadmin or a performance analyst, as model approximations to be learned or refined experimentally, (ii) a runtime engine for iteratively collecting experimental performance samples, validating and refining performance models. Chorus efficiently builds accurate models online, reuses and adjusts archival models over time, and combines them into an ensemble of models. We perform an experimental evaluation on a multi-tier server platform, using several industry- standard benchmarks. Our results show that Chorus is a flexible modeling framework and knowledge base for validating, extending and reusing existing models while adapting to new situations.
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 | 0.001 |
| 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