Edge scheduling framework for real-time and non real-time tasks
Bibliographic record
Abstract
This paper presents a two-stage edge scheduling framework that maps the tasks of a real-time artificial intelligence (AI) application across a collection of edge computing resources. The first stage is global and it creates schedules with execution slots for tasks with real-time constraints. The second stage is local and it uses the schedules from the first stage and places non real-time tasks in the free slots. By creating global schedules for time-critical tasks, the two-stage design allows a group of such tasks to run in a coordinated manner across edge computers while providing the local autonomy to execute other tasks according to a local schedule. We implemented the framework over a heterogeneous collection of machines and measured its performance under different conditions. Results show that the two-stage architecture is better because the flexibility offered by the architecture can be used by the edge servers to obtain higher overall performance (i.e., increase the batch and interactive execution rates or deadline compliance rates).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".