Chorus: an interactive approach to incremental modeling and validation in clouds
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.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".