Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917379971219456 |
|---|---|
| author | Bondar, Georgiy A. Eisenklam, Abigail Cai, Yifan Gifford, Robert Sial, Tushar Phan, Linh Thi Xuan Halder, Abhishek |
| author_facet | Bondar, Georgiy A. Eisenklam, Abigail Cai, Yifan Gifford, Robert Sial, Tushar Phan, Linh Thi Xuan Halder, Abhishek |
| contents | 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 nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real-world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real-time systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01441 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation Bondar, Georgiy A. Eisenklam, Abigail Cai, Yifan Gifford, Robert Sial, Tushar Phan, Linh Thi Xuan Halder, Abhishek Systems and Control Machine Learning Operating Systems Signal Processing 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 nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real-world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real-time systems. |
| title | Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation |
| topic | Systems and Control Machine Learning Operating Systems Signal Processing |
| url | https://arxiv.org/abs/2604.01441 |