Adaptive Real-Time Scheduling Algorithms for Embedded Systems

Abstract

Embedded systems are becoming more in demand to work in dynamic and uncertain environments, and being confined to the strong requirements of real-time. Conventional static scheduling models usually cannot cope with runtime modification in workload, resource availability, or system updates. This brief survey covers the area of feedback-based control (e.g., Feedback Control Scheduling) and interdependence between tasks (e.g., Symbiotic Scheduling of Periodic Tasks) models. It also borders on predictive methods and power management, combining methods based on Dynamic Voltage and Frequency Scaling (DVFS). In this paper, key mechanisms are briefly summarized, influencing trade-offs relating to adaptivity/predictability, typical metrics of evaluation, and ongoing problems, especially in situations where safety is a critical factor, giving a succinct and easy-to-understand introduction to researchers and practitioners who have to cope with the changing environment of adaptive real-time systems.