Low-Power Scheduling Algorithms for Sporadic Task with Shared Resources in Hard Real-Time Systems

Dynamic voltage scaling (DVS), which adjusts the clock frequency and supply voltage based on the workload of systems, has been adopted in many computing systems to reduce the energy consumption of the processor. In this paper, we address the problem of minimizing overall energy consumption of a sporadic task in real-time system with a shared set of serially reusable, single unit resources, considering a generalized power model. We first present a static scheduling algorithm, called SSTSASR, which minimizes the system energy consumption of a given set of sporadic tasks and takes the off-chip workload into consideration, assuming that each task releases with the worst-case workload. In addition, we present dynamic slack reclamation techniques, called DSTSASR, which reclaims the slack time from the early completion task to reduce the energy consumption and takes the speed transition overhead into consideration. The simulation results show that the proposed DSTSASR algorithm could achieve a significant amount of energy savings compared with other DVS algorithms. It can reduce the energy consumption by 7.37–31.99% over the DVSSR and by 0.1–5.90% over the SSTSASR.