An important trend of real-time systems is to integrate applications with different criticality levels on a single multicore platform, enabling resource sharing among applications . However, the existing task allocation schemes suffer from the issue of severe resource contention between cores for accessing mutually exclusively shared resources, resulting in significant blocking time. This jeopardizes the system schedulability and leads to the application difficulty of mixed-criticality systems (MCS) in real-world systems. To tackle this issue, this paper proposes a resource-aware task allocation (RATA) algorithm for multicore mixed-criticality systems. The proposed allocation takes the resource usage of tasks into account and aims to localize the most frequently accessed resources by allocating the requesting tasks on the same core, which effectively reduces the inter-core resource contention, hence, improving system schedulability. In addition, a specialized allocation process is constructed for different execution modes in MCS with a task-splitting mechanism, enabling direct support of MCS with improved resource utilization. The experimental results show that RATA outperforms existing methods by 71.18% on average (up to 217.85%) in terms of system schedulability.

Fully-partitioned fixed-priority scheduling (FP-FPS) multiprocessor systems are widely found in real-time applications, where spin-based protocols are often deployed to manage the mutually exclusive access of shared resources. Unfortunately, existing approaches either enforce rigid spin priority rules for resource accessing or carry significant pessimism in the schedulability analysis, imposing substantial blocking time regardless of task execution urgency or resource over-provisioning. This paper proposes FRAP, a spin-based flexible resource accessing protocol for FP-FPS systems. A task under FRAP can spin at any priority within a range for accessing a resource, allowing flexible and finegrained resource control with predictable worst-case behaviour. Under flexible spinning, we demonstrate that the existing analysis techniques can lead to incorrect timing bounds and present a novel MCMF (minimum cost maximum flow)-based blocking analysis, providing predictability guarantee for FRAP. A spin priority assignment is reported that fully exploits flexible spinning to reduce the blocking time of tasks with high urgency, enhancing the performance of FRAP. Experimental results show that FRAP outperforms the existing spin-based protocols in schedulability by $\mathbf{1 5. 2 0 \%} \mathbf{- 3 2. 7 3 \%}$ on average, up to $\mathbf{6 5. 8 5 \%}$.