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March 10, 2026Concurrency and Computation Practice and Experience0 citationsOpen Access

Efficient Scheduling Algorithms for Multicore Cyclic Executives With Precedence and Exclusion Relations

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BNBruno NogueiraUniversidade Federal de PernambucoALAlfredo LimaUniversidade Federal FluminenseETEduardo TavaresUniversidade Federal de Pernambuco

Key Points

  • To generate optimal cyclic executives for multicore processors by addressing task precedence and exclusion relations.
  • Proposing integer linear programming models for task scheduling.
  • Considering both preemptive and non-preemptive cyclic executives.
  • Utilizing partitioned and global work allocation schemes.
  • Implementing a local search-based heuristic for approximate solutions.
  • Evaluating methods on synthetic and benchmark instances with complex dependencies.
  • The proposed methods effectively generate optimal or near-optimal cyclic executives.
  • Experimental comparisons show superior performance against a state-of-the-art approximation method.
  • The approach accommodates thousands of tasks with intricate inter-task dependencies.

Abstract

ABSTRACT Cyclic executives (CEs) offer the advantage of ensuring complete determinism with minimal runtime overhead, often making them the preferred choice for safety‐critical real‐time systems. However, generating CEs for multicore processors while addressing task precedence and exclusion relations presents significant challenges. In this paper, unlike previous work, we tackle these challenges by proposing integer linear programming (ILP) models to generate optimal preemptive and non‐preemptive CEs, considering both partitioned and global work allocation schemes. Additionally, we introduce a local search‐based heuristic to efficiently produce approximate solutions. Our methods are evaluated on both synthetic and benchmark instances from the literature, encompassing thousands of tasks and complex inter‐task dependencies, and include a direct comparison with a state‐of‐the‐art approximation method. The experimental results highlight the effectiveness of the proposed approaches in generating optimal or near‐optimal CEs for large‐scale task sets.

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Cite This Study

Nogueira et al. (2026) studied this question.

synapsesocial.com/papers/69af950a70916d39fea4c3b6https://doi.org/10.1002/cpe.70629
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