Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation
Modern real-time systems require accurate characterization of task timing behavior to ensure predictable performance, particularly on complex hardware architectures. Existing methods, such as worst-case execution time analysis, often fail to capture the fine-grained timing behaviors of a task under varying resource contexts (e.g., an allocation of cache, memory bandwidth, and CPU frequency), which is necessary to achieve efficient resource utilization. In this paper, we introduce a novel generative profiling approach that synthesizes context-dependent, fine-grained timing profiles for real-time tasks, including those for unmeasured resource allocations. Our approach leverages a nonparamet...
| Authors | Georgiy A. Bondar, Abigail Eisenklam, Yifan Cai et al. |
|---|---|
| Primary category | eess.SY |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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