MC-Fluid: Fluid Model-Based Mixed-Criticality Scheduling on Multiprocessors

Abstract

A mixed-criticality system consists of multiple components with different criticalities. While mixed-criticality scheduling has been extensively studied for the uniprocessor case, the problem of efficient scheduling for the multiprocessor case has largely remained open. We design a fluid model-based multiprocessor mixed-criticality scheduling algorithm, called MC-Fluid, in which each task is executed in proportion to its criticality-dependent rate. We propose an exact schedulability condition for MC-Fluid and an optimal assignment algorithm for criticality-dependent execution rates with polynomial complexity. Since MC-Fluid cannot construct a schedule on real hardware platforms due to the fluid assumption, we propose MC-DP-Fair algorithm, which can generate a non-fluid schedule while preserving the same schedulability properties as MC-Fluid. We show that MC-Fluid has a speedup factor of (1 + v 5)/2 ( 1.618), which is best known in multiprocessor MC scheduling, and simulation results show that MC-DP-Fair outperforms all existing algorithms.