Chorus: an interactive approach to incremental modeling and validation in clouds
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
Performance modeling is an emerging approach towards automating management in Clouds. The need for fast model adaptation to dynamic changes, such as workload mix changes and hardware upgrades, has, however, not been previously addressed. Towards this we introduce Chorus, an interactive framework for fast refinement of old models in new contexts and building application end-to-end latency models, incrementally, on-the-fly. Chorus consists of (i) a declarative high-level language for expressing expert hypotheses, and system inquiries (ii) a runtime system for collecting experimental performance samples, learning and refining models for parts of the end-to-end configuration space, on-the-fly. We present our experience with building the Chorus infrastructure, and the corresponding model evolution for two industry-standard applications, running on a multi-tier dynamic content server platform. We show that the Chorus on-the-fly modeling framework provides accurate, fast and flexible performance modeling by reusing old approximate models, while adapting them 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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