The Optimality of PFPasap Algorithm for Fixed-Priority Energy-Harvesting Real-Time Systems

The paper addresses the real-time fixed-priority scheduling problem for battery-powered embedded systems whose energy storage unit is replenished by an environmental energy source. In this context, a task may meet its deadline only if its cost of energy can be satisfied early enough. Hence, a scheduling policy for such a system should account for properties of the source of energy, capacity of the energy storage unit and tasks cost of energy. Classical fixed-priority schedulers are no more suitable for this model. Based on these motivations, we propose PFP ASAP an optimal scheduling algorithm that handles both energy and timing constraints. Furthermore, we state the worst case scenario for non concrete task sets a non concrete task set is a set of real-time tasks whose offsets are known only at run-time scheduled with this algorithm and build a necessary and sufficient feasibility condition for non concrete task sets. Moreover, a minimal bound of the storage unit capacity that keeps a task set schedulable with PFP ASAP is also proposed. Finally, we validate the proposed theory with large scale simulations and compare our algorithm with other existing ones.