A query language and runtime tool for evaluating behavior of multi-tier servers
Bibliographic record
Abstract
As modern multi-tier systems are becoming increasingly large and complex, it becomes more difficult for system analysts to understand the overall behavior of the system, and diagnose performance problems. To assist analysts inspect performance behavior, we introduce SelfTalk, a novel declarative language that allows analysts to query and understand the status of a large scale system. SelfTalk is sufficiently expressive to encode an analyst's high-level hypotheses about system invariants, normal correlations between system metrics, or other a priori derived performance models, such as, "I expect that the throughputs of interconnected system components are linearly correlated". Given a hypothesis, Dena, our runtime support system, instantiates and validates it using actual monitoring data within specific system configurations. We evaluate SelfTalk/Dena by posing several hypotheses about system behavior and querying Dena to validate system behavior in a multi-tier dynamic content server. We find that Dena automatically validates the system performance based on the pre-existing hypotheses and helps to diagnose system misbehavior.
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How this classification was reachedexpand
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 | 0.010 | 0.006 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".