Performance-based design of distributed real-time systems
Bibliographic record
Abstract
The paper presents a design method for distributed systems with statistical, end-to-end real-time constraints, and with underlying stochastic resource requirements. A system is modeled as a set of chains, where each chain is a distributed pipeline of tasks, and a task can represent any activity requiring nonzero load from some CPU or network resource. Every chain has two end-to-end performance requirements: its delay constraint denotes the maximum amount time a computation can take to flow through the pipeline, from input to output. A chain's quality constraint mandates a minimum allowable success rate for outputs that meet their delay constraints. The design method solves this problem by deriving (1) a fixed proportion of resource load to give each task; and (2) a deterministic processing rate for every chain, an which the objective is to optimize the output success rate (as determined by an analytical approximation). They demonstrate their technique on an example system, and compare the estimated success rates with those derived via simulated on-line behavior.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".