Energy-Resilient Real-Time Scheduling

Abstract

Embedded nodes in future cyber-physical systems are mostly self-powered, scavenging their required energy from the environment. The environmental sources of energy are usually variable, so that some prediction methods are employed to proactively adapt to the variable harvesting energy. However, prediction errors may surprise the system with some unpredicted changes, needing appropriate reactions. We consider an energy-harvesting real-time system with periodic tasks of multiple performance levels. An energy-resilient scheduler is proposed for the system to react to the unpredicted changes such that the system is survivable, recovers from such a change in a timely manner, and appropriately controls its performance degradation. After the recovery, however, the energy-resilient scheduler preserves the system survivability and maximizes its performance in a prediction time horizon, while it will be ready for another surprise. We provide some theoretical properties and a feasibility test which are used in the design of the energy-resilient scheduler. Our simulations show that the proposed resilient scheduler outperforms well-known performance maximization methods, effectively approximates the optimal solution, and reacts appropriately against surprises of high severity.