High Performance Real-Time Scheduling Framework for Multiprocessor Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Embedded systems, performing specific functions in modern devices, have become pervasive in today's technology landscape. As many of these systems are real-time systems, they necessitate operations with stringent time constraints. This is especially evident in sectors like automotive and aerospace. This thesis introduces a High Performance Real-time Scheduling (HPRTS) framework, which is designed to navigate the multifaceted challenges faced by multiprocessor real-time systems. \n \nTo begin with, the research attempts to bridge the gap between system reliability and resource sharing in Mixed-Criticality Systems (MCS). In addressing this, a novel fault-tolerance solution is presented. Its main goal is to enhance fault management and reduce blocking time during fault tolerance. Following this, the thesis delves into task allocation in systems with shared resources. In this context, we introduce a distinct Resource Contention Model (RCM). Using this model as a foundation, our allocation strategy is formulated with the aim to reduce resource contention. Moreover, in light of the escalating system complexity where tasks are represented using Directed Acyclic Graph (DAG) models, the research unveils a new Response Time Analysis (RTA) for multi-DAG systems. This particular analysis has been tailored to provide a safe and more refined bound. \n \nReflecting on the contributions made, the achievements of the thesis highlight the potency of the HPRTS framework in steering real-time embedded systems toward high performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it