Adaptive Workload Management in Mixed-Criticality Systems

Abstract

Due to the efficient resource usage of integrating tasks with different criticality onto a shared platform, the integration with mixed-criticality tasks is becoming an increasingly important trend in the design of real-time systems. One challenge in such a mixed-criticality system is to maximize the service for low-critical tasks, while meeting the timing constraints of high-critical tasks. In this article, we investigate how to adaptively manage the low-critical workload during runtime to meet both goals, that is, providing the service for low-critical tasks as much as possible and guaranteeing the hard real-time requirements for high-critical tasks. Unlike previous methods, which enforce an offline bound towards the low-critical workload, runtime adaptation approaches are proposed in which the incoming workload of low-critical tasks is adaptively regulated by considering the actual demand of high-critical tasks. This actual demand of the high-critical tasks, in turn, is adaptively updated using their historical arrival information. Based on this adaptation scheme, two scheduling policies—the priority-adjustment policy and the workload-shaping policy—are proposed to do the workload management. In order to reduce online management overhead, a lightweight scheme with O ( n · log ( n )) complexity is developed. Extensive simulation results are presented to demonstrate the effectiveness of our proposed workload management approaches.