Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation

• URL: http://arxiv.org/abs/2604.01441v1
• Date: Wed, 01 Apr 2026 22:27:27 GMT
• Title: Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation
• Abstract summary: Real-time systems require accurate characterization of task timing behavior to ensure predictable performance.<n>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.<n>We introduce a novel generative profiling approach that synthesizes context-dependent, fine-grained timing profiles for real-time tasks.
• Score: 1.6495587226184654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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