Extending Timing Analysis for Non Preemptive Task Sets on Multicore Under the AER Model
Abstract: Real-time multi-core systems with shared memory are harder to analyze due to varying execution times caused by congestion in accessing the shared memory. A promising way to make these systems more deterministic is the Acquisition-Execution-Restitution (AER)-model which takes a memory-centric approach to scheduling which enables the multicore problem to be seen as a single core scheduling problem. Memorycentric scheduling means that instead of scheduling tasks onto cores the memory accesses are scheduled onto the memory, where in this case the memory phases are allowed to access the shared memory nonpreemptively during their allocated slots. In this thesis an extended version of the AER-model and a new Idle-time Insertion Policy (IIP), which can make decisions on whether to leave resources idle when there is work to be done as an attempt to improve schedulability, is presented. These can be used in conjunction with a state-of-the-art scheduling analysis tool to perform timing analysis on job sets under the AER-model. The extended AER-model introduces scheduling windows for the Acquisition (A)-phase and Restitution (R)-phase of tasks in a way that no further precedence constraints need to be considered by the scheduler as not to schedule any of the A-phase or R-phase in the wrong order. The developed IIP is then used by the analysis tool to make sure that there is at least one free core in the system before a new task is scheduled as this needs to be put into consideration in a memory-centric scheduler. The extended AER-model and the developed IIP are then benchmarked with task sets coherent to the AUTOSAR-framework. The benchmarks explore the schedulability ratio when varying task amount, core amount, scheduling algorithms and the window ratio for the phases. The results show that Earliest Deadline First (EDF) outperforms other algorithms that are special versions of Rate-Monotonic Scheduling (RMS) where priorities are assigned based on a combination of a tasks period and its utilization. The results also show that tasks with a window ratio of 60% as well as task sets of sizes between 100 to 150 yields the highest schedulability ratio.
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