Schedulability Analysis of Non-Preemptive Strictly Periodic Tasks in Multi-Core Real-Time Systems

Non-preemptive tasks with strict periods are usually adopted in practical real-time systems where missing deadlines may lead to catastrophic situations. Their schedulability analysis plays a crucial role in guiding the design and development of such real-time systems. In this paper, we study the schedulability analysis problem of partitioned non-preemptive scheduling for strictly periodic tasks on multiprocessors. We propose a set of schedulability conditions, which determines whether a new task can be scheduled on a processor without changing the offsets of the existing tasks and identifies all valid start time offsets for the new task if it is schedulable. Based on these conditions, we present a task assignment algorithm, which is not optimal, but provides an upper bound on the number of cores required by a periodic task set. We illustrate this algorithm with a practical example and conduct stimulation experiments with randomly generated task sets to evaluate the performance of our approach from several aspects

A survey of techniques for reducing interference in real-time applications on multicore platforms

This survey reviews the scientific literature on techniques for reducing interference in real-time multicore systems, focusing on the approaches proposed between 2015 and 2020. It also presents proposals that use interference reduction techniques without considering the predictability issue. The survey highlights interference sources and categorizes proposals from the perspective of the shared resource. It covers techniques for reducing contentions in main memory, cache memory, a memory bus, and the integration of interference effects into schedulability analysis. Every section contains an overview of each proposal and an assessment of its advantages and disadvantages.This work was supported in part by the Comunidad de Madrid Government "Nuevas Técnicas de Desarrollo de Software de Tiempo Real Embarcado Para Plataformas. MPSoC de Próxima Generación" under Grant IND2019/TIC-17261