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What is Optimizing Fixed Priorities in Real-Time Systems about?
This paper presents a novel approach to optimize fixed priority assignments in partitioned preemptive real-time systems using gradient descent algorithms commonly utilized in machine learning. The proposed method adapts techniques such as the Adam optimizer and Gradient Noise to improve priority assignment efficiency while maintaining compliance with timing constraints. Evaluation results demonstrate that this approach can yield more schedulable solutions compared to existing heuristics, approximating optim
- Author
- mhmad240
- Language
- EN