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<p>In multiprocessor Mixed-Criticality System (MCS), tasks must share resources (e.g., memory, I/O devices). Traditional synchronization mechanisms (e.g., suspension locks, spin locks) suffer from priority inversion and remote blocking issues in multiprocessor environments. Existing protocols (e.g., MSRP, FMLP, MrsP) optimize resource contention through mechanisms like priority ceiling and priority inheritance. However, these protocols primarily target static MCS and struggle to adapt to dynamic criticality mode switching in MCS. Since in MCS with escalating resource contention level such as autonomous driving systems: high resource contention levels may lead to the discarding of all LO-Criticality (low criticality) tasks, reducing system utilization. In this paper, we propose a Switchable Protocol Framework (SWPF), which achieves adaptability to resource contention levels by dynamically switching between optimistic and pessimistic locking protocols. Experimental results demonstrate that SWPF outperforms traditional FMLP with an average performance improvement of 5.5% across various resource scenarios, reaching up to 13.5% enhancement when threshold is set reasonably under high resource contention. Additionally, we present a real-time scheduling support Optimistic Protocol (OP) with comprehensive Response Time Analysis (RTA) to facilitate switching with existing pessimistic protocols. Furthermore, we extend the FMLP’s nested resource rules to better align with system scenarios in practice.</p>

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Adaptive Resource Sharing Switchable Protocol Framework for Mixed-Criticality Systems in Autonomous

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Luhan Li,
  • Wenle Wang,
  • Shuai Zhao,
  • Zhonghua Cao

摘要

In multiprocessor Mixed-Criticality System (MCS), tasks must share resources (e.g., memory, I/O devices). Traditional synchronization mechanisms (e.g., suspension locks, spin locks) suffer from priority inversion and remote blocking issues in multiprocessor environments. Existing protocols (e.g., MSRP, FMLP, MrsP) optimize resource contention through mechanisms like priority ceiling and priority inheritance. However, these protocols primarily target static MCS and struggle to adapt to dynamic criticality mode switching in MCS. Since in MCS with escalating resource contention level such as autonomous driving systems: high resource contention levels may lead to the discarding of all LO-Criticality (low criticality) tasks, reducing system utilization. In this paper, we propose a Switchable Protocol Framework (SWPF), which achieves adaptability to resource contention levels by dynamically switching between optimistic and pessimistic locking protocols. Experimental results demonstrate that SWPF outperforms traditional FMLP with an average performance improvement of 5.5% across various resource scenarios, reaching up to 13.5% enhancement when threshold is set reasonably under high resource contention. Additionally, we present a real-time scheduling support Optimistic Protocol (OP) with comprehensive Response Time Analysis (RTA) to facilitate switching with existing pessimistic protocols. Furthermore, we extend the FMLP’s nested resource rules to better align with system scenarios in practice.