The Computational Complexity of Feasibility Analysis for Conditional DAG Tasks

Pub Date : 2023-07-05 DOI:10.1145/3606342
Sanjoy Baruah, A. Marchetti-Spaccamela
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Abstract

The Conditional DAG (CDAG) task model is used for modeling multiprocessor real-time systems containing conditional expressions for which outcomes are not known prior to their evaluation. Feasibility analysis for CDAG tasks upon multiprocessor platforms is shown to be complete for the complexity class pspace; assuming np ≠ pspace, this result rules out the use of Integer Linear Programming solvers for solving this problem efficiently. It is further shown that there can be no pseudo-polynomial time algorithm that solves this problem unless p = pspace.
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条件DAG任务可行性分析的计算复杂度
条件DAG (CDAG)任务模型用于建模包含条件表达式的多处理器实时系统,这些条件表达式的结果在评估之前是未知的。对于复杂度类pspace,在多处理器平台上完成了CDAG任务的可行性分析;假设np≠pspace,这个结果排除了使用整数线性规划求解器来有效地解决这个问题。进一步证明,除非p = pspace,否则不可能存在伪多项式时间算法来解决这个问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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