On Budgeting and Quality, with an Application to Safety-Critical Real-time Systems
Bibliographic record
Abstract
Mandated by modern real-time applications that operate for long durations under (random) bandwidth limitations, we develop a suitable notion of quality of service (QoS) that makes explicit the inherent tradeoffs between the required execution demand and the available budget. We derive bounds on central timing parameters relating the execution demands of tasks to the available budgets which, if satisfied by the tasks, allows us to establish probably approximately correct (PAC) bounds that quantify the long-term evolution of quality of execution. Such large-deviation bounds furnish proof that tasks achieve their desired QoS levels at an exponentially-decaying rate, and, once attained, these levels are sustained and guaranteed for the entire (possibly indefinite) operation horizon, in spite of random fluctuations in budget availability. We study the case when task execution requirements and/or available budgets are dependent, and we derive sufficient conditions under which non-trivial system-wide PAC-type quality guarantees exist under limited dependence. We do so through a novel application of the Lovász Local Lemma. We also present a use-case involving an application of our bounds to safety-critical systems, where the goal is to synthesize runtime monitors and their timing characteristics under a rather general isochronous execution model on multiple processors. We show how to compute monitor worst-case execution times so that tasks attain given QoS levels and also meet their hard deadlines. We treat the related scheduling and feasibility questions, and we show how to derive feasible isochronous Dp-Fair schedules, if they exist, in polynomial-time.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| 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".