Phased execution models separate computation from access to shared resources to make task execution predictable. These task models minimize interference between tasks, making them suitable for modern complex multi-core platforms. In the phased execution model, tasks perform computations only using the local memory to avoid accessing the shared memory during task execution. All instructions and data, including the intermediate results, are stored in the local memory during execution. Thus, the local memory size becomes a crucial factor in contrast to conventional execution. In the literature, non-preemptive and fully preemptive execution of phased execution models are studied. While the non-preemptive approaches utilize the local memory well, schedulability is reduced due to blocking. On the other hand, fully preemptive execution phases allow for better schedulability but require significantly more local memory capacity to implement preemptions at runtime without violating the model’s execution semantics. Thus, this work evaluates different approaches to limited preemptive scheduling of the phased execution model under partitioned fixed-priority scheduling. We demonstrate that preemption thresholds and non-preemptive regions can successfully be used to satisfy both timing and memory constraints of phased tasks.

Commercial off-the-shelf (COTS) multi-core platforms offer high performance and large availability of processing resources. Increased contention when accessing shared resources is a result of the high parallelism and one of the main challenges when realtime applications are deployed to these platforms. As a result, several execution models have been proposed to avoid contention by separating access to shared resources from execution.

Phased execution models are a good solution to tame the increased complexity and contention of commercial off-the-shelf (COTS) multi-core platforms, e.g., Acquisition-Execution-Restitution (AER) model, PRedictable Execution Model (PREM). Such models separate execution from access to shared resources on the platform to minimize contention. All data and instructions needed during an execution phase are copied into the local memory of the core before starting to execute. Phased execution models are generally used with non-preemptive scheduling to increase predictability. However, the blocking time in non-preemptive systems can reduce schedulability. Therefore, an investigation of preemption methods for phased execution models is warranted. Although, preemption for phased execution models must be carefully designed to retain its execution semantics, i.e., the handling of local memory during preemption becomes non-trivial. This paper investigates different methods to realize preemption in phased execution models while preserving their semantics. To the best of our knowledge, this is the first paper to explore different approaches to implement preemption in phased execution models from the perspective of data management. We introduce two strategies to realize preemption of execution phases based on different methods of handling local data of the preempted task. Heuristics are used to create time-triggered schedules for task sets that follow the proposed preemption methods. Additionally, a schedulability-aware preemption heuristic is proposed to reduce the number of preemptions by allowing preemption only when it is beneficial in terms of schedulability. Evaluations on a large number of synthetic task sets are performed to compare the proposed preemption models against each other and against a non-preemptive version. Furthermore, our schedulability-aware preemption heuristic has higher schedulability with a clear margin in all our experiments compared to the non-preemptive and fully-preemptive versions.