Pre-Run-Time Scheduling of Asynchronous and Periodic Processes With Offsets, Release Times, Deadlines, Precedence and Exclusion Relations
Bibliographic record
Abstract
Utilizing non-zero offsets when scheduling real-time periodic processes significantly increases the chances of satisfying all the timing constraints in a real-time system. In this paper, a method that enables the utilization of non-zero offsets in the pre-run-time scheduling of asynchronous and periodic processes with release times, deadlines, precedence and exclusion relations on either a uniprocessor or on a multiprocessor in real-time embedded systems is presented. This paper also identifies for the first time, the set of general conditions that a periodic process newpi with release time rnewpi, computation time cnewpi, deadline dnewpi, period prdnewpi, permitted range of offset onewpi, must satisfy, in order to satisfy the timing constraints of any given asynchronous process ai with computation time cai, deadline dai, minimum time between two consecutive requests minai, and earliest time that asynchronous process ai can make a request for execution lai. A method based on these general conditions for converting asynchronous processes with earliest request times into new periodic processes with offset constraints is also introduced.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".