A Timing-Anomaly Free Dynamic Scheduling on Heterogeneous Systems

TL;DR

This paper introduces a novel deterministic dynamic scheduling algorithm for heterogeneous systems that guarantees timing-anomaly freedom, enabling accurate Worst-Case Response Time estimation and reducing system response times.

Contribution

It presents the first timing-anomaly-free dynamic scheduling method with a formal correctness proof and practical constraint generation techniques for heterogeneous systems.

Findings

Eliminates timing anomalies in heterogeneous system scheduling.

Provides tight Worst-Case Response Time estimates with a single offline simulation.

Reduces response time jitter compared to traditional algorithms.

Abstract

Heterogeneous systems commonly adopt dynamic scheduling algorithms to improve resource utilization and enhance scheduling flexibility. However, such flexibility may introduce timing anomalies, wherein locally reduced execution times can lead to an increase in the overall system execution time. This phenomenon significantly complicates the analysis of Worst-Case Response Time (WCRT), rendering conventional analysis either overly pessimistic or unsafe, and often necessitating exhaustive state-space exploration to ensure correctness. To address this challenge, this paper presents the first timing-anomaly-free dynamic scheduling algorithm for heterogeneous systems, referred to as Deterministic Dynamic Execution. It achieves a safe and tight WCRT estimate through a single offline simulation execution. The core idea is to apply deterministic execution constraints, which partially restrict the resource allocation and execution order of tasks at runtime. Based on a formally defined execution progress model for heterogeneous system scheduling, we prove the correctness of the proposed execution constraints and their ability to eliminate timing anomalies. Furthermore, we propose two methods to generate execution constraints. The first method derives execution constraints directly from the execution traces produced by existing scheduling algorithms. The second method is a heuristic-based approach that constructs execution constraints, enabling further reduction of the WCRT. Experimental results on synthetically generated DAG task sets under various system configurations demonstrate that, compared to traditional dynamic scheduling algorithms, our approach not only eliminates timing anomalies but also effectively reduces both the WCRT and response time jitter.