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

TL;DR

This paper introduces a generative profiling method using a Schr"odinger Bridge formulation to accurately predict task timing under various resource contexts, improving resource allocation in real-time systems.

Contribution

It presents a novel nonparametric, conditional multi-marginal Schr"odinger Bridge approach for synthesizing fine-grained, context-dependent timing profiles, including for unmeasured resource scenarios.

Findings

Accurately predicts execution profiles for unseen resource contexts.

Demonstrates efficiency and effectiveness on real-world benchmarks.

Showcases utility in adaptive multicore resource allocation.

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 Schr\"odinger 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.