Minimizing stack memory for hard real-time applications on multicore platforms

Abstract

Multicore platforms are increasingly used in real-time embedded applications. In the development of such applications, an efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and allowing savings in stack space. In this paper, we target at the optimal mapping of tasks to cores and the assignment of the scheduling parameters for systems scheduled with preemption thresholds. We formulate the optimization problems using Mixed Integer Linear Programming framework, and propose an efficient heuristic as an alternative. We demonstrate the efficiency and quality of both approaches with extensive experiments using random systems as well as two industrial case studies.