用生成模型预测任务在不同资源下的运行时间，提升实时系统资源分配效率。
Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation
- 基于非参数条件多边缘薛定谔桥生成未测量资源配置下的任务执行时序
- 在真实基准测试中实现高精度时序预测，支持未采样配置的泛化
- 适用于多核实时系统的自适应资源调度，尤其适合复杂硬件场景
现代实时系统需要对任务的时序行为进行精确刻画以保证性能可预测性，尤其是在复杂的硬件架构下。现有方法如最坏情况执行时间分析常无法捕捉任务在不同资源上下文（如缓存、内存带宽、CPU频率）下的细粒度时序特性，这限制了资源利用效率。本文提出一种新的生成式剖析方法，可为实时任务生成依赖上下文的细粒度时序剖面，包括未测量资源配置下的剖面。该方法采用非参数、条件化的多边缘薛定谔桥（MSB）形式，能在最大似然保证下生成未见资源配置下的准确执行剖面。通过真实世界基准测试验证了该方法的高效性与有效性，并在一个典型的实时系统多核资源自适应分配案例研究中展示了其实际应用价值。
原文摘要 · Abstract (English)
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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