An Energy-Efficient Periodic Resource Model for Bounded Delay-Tolerant Real-Time Systems

In the past few years, the energy consumption of real-time systems has become an essential topic of research, especially with the rise of the internet of things. In hard real-time systems, sensitivity to timing is the focal feature of system behaviors. Real-time systems must consider time constraints (mainly deadlines) to deliver the proper results. Therefore, typical real-time systems are inefficient in resource reservation, where resource supply is always higher than the workload demand and thus consumes more power than required. In firm and soft real-time systems, some deadline violations can be tolerated in a specific time interval. The worst-case execution time can be delayed to abound range. Therefore, instead of over-provisioning resources when considering the demand cannot be higher than supply, we propose a new algorithm that can efficiently estimate the resource reservation and reduce power consumption. The proposed algorithm is theoretically studied, and results show that while still satisfying the delay requirements of control systems, significant energy savings (up to 78.5%) could be achieved.