Partitioning and Selection of Data Consistency Mechanisms for Multicore Real-Time Systems
Partitioning and Selection of Data Consistency Mechanisms for Multicore Real-Time Systems
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DOI：
10.1145/3320271
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发表时间：
2019-06
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影响因子：
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通讯作者：
Zaid Al-bayati;Youcheng Sun;Haibo Zeng;M. Natale;Qi Zhu;B. Meyer
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文献类型：
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作者：
Zaid Al-bayati;Youcheng Sun;Haibo Zeng;M. Natale;Qi Zhu;B. Meyer
多核平台在实时系统中越来越受欢迎。设计多核实时系统的主要挑战之一是确保一致和及时地访问共享资源。已经提出了诸如MPCP和MSRP的基于锁的保护机制，以保证以阻塞为代价的多核系统中的互斥访问。在这篇文章中，我们考虑在资源共享的多核实时系统的分区和调度。首先，我们提出了一个资源感知的任务划分算法的系统与基于锁的保护。当共享资源是通信缓冲区时，无等待方法是一种合适的替代方法，该方法确保对共享内存资源的一致访问，而以额外内存空间为代价的阻塞可以忽略不计。我们提出了几种方法来解决任务划分和数据一致性机制（基于锁或无等待）的选择的联合问题。该问题首先被表述为一个非线性规划（ILP）。对于大型系统的ILP解决方案是不可扩展的，我们提出了两个启发式算法。实验结果比较了所提出的方法在寻找具有低内存成本的可扩展系统方面的有效性，并显示了使用无等待方法可以显着提高可扩展性。
Multicore platforms are becoming increasingly popular in real-time systems. One of the major challenges in designing multicore real-time systems is ensuring consistent and timely access to shared resources. Lock-based protection mechanisms such as MPCP and MSRP have been proposed to guarantee mutually exclusive access in multicore systems at the expense of blocking. In this article, we consider partitioning and scheduling in multicore real-time systems with resource sharing. We first propose a resource-aware task partitioning algorithm for systems with lock-based protection. Wait-free methods, which ensure consistent access to shared memory resources with negligible blocking at the expense of additional memory space, are a suitable alternative when the shared resource is a communication buffer. We propose several approaches to solve the joint problem of task partitioning and the selection of a data consistency mechanism (lock-based or wait-free). The problem is first formulated as an Integer Linear Programming (ILP). For large systems where an ILP solution is not scalable, we propose two heuristic algorithms. Experimental results compare the effectiveness of the proposed approaches in finding schedulable systems with low memory cost and show how the use of wait-free methods can significantly improve schedulability.