Schedulability analysis for 3-phase tasks with partitioned fixed-priority scheduling

Multicore platforms are being increasingly adopted in Cyber-Physical Systems (CPS) due to their advantages over single-core processors, such as raw computing power and energy efficiency. Typically, multicore platforms use a shared memory bus that connects the cores to the off-chip main memory. This sharing of memory bus may cause tasks running on different cores to compete for access to the main memory whenever data/instructions are need to be read/written from/to the main memory. Such competition is problematic, as it may cause variations in the execution time of tasks in a non-deterministic way. To reduce the complexity of analyzing this problem, the 3-phase task model was proposed that divides tasks’ executions into distinct memory and execution phases. The distinctive memory phases are then scheduled to eliminate/minimize main memory contention between concurrently executing tasks. However, 3-phase tasks running on different cores may still compete to access the shared memory bus/main memory in order to execute memory phases. This paper presents a partitioned scheduling-based approach that allows one to derive memory bus contention-aware worst-case response time of tasks that follow the 3-phase task model. In particular, the bus-contention analysis is derived by considering two memory access models, i.e., (i) dedicated memory access model, where a core having allowed to access the main memory via memory bus is permitted to execute more than one memory phase, and (ii) fair memory access model, that restrict each core to execute only one memory phase in its allocated bus access. Both these models represent different system and application requirements, and the resulting bus contention of tasks may vary depending on the considered model. To evaluate the effectiveness of the proposed bus contention analysis, we compare its performance against an existing analysis in the state-of-the-art by performing (i) case-study experiments, using benchmarks from the Mälardalen Benchmark suite, and (ii) empirical evaluation using synthetic task sets. Results show that our proposed analysis can improve task set schedulability of 3-phase tasks by up to 88 percentage points