Energy-Constrained Performance Optimizations For Real-Time Operating Systems

In energy-constrained settings, most real-time operating systems take the approach of minimizing the energy consumption while meeting all the task deadlines. However, it is possible that the available energy budget is not sufficient to meet all deadlines and some deadlines will inevitably have to be missed. In this paper, we present a framework through which the operating system can select jobs for execution in order to achieve two alternative performance objectives: 1) maximizing the number of deadlines met , and 2) maximizing the total reward (utility) of jobs that meet their deadlines during the operation. We present an optimal algorithm that achieves the first objective. We prove that achieving the latter objective is NP-Hard and propose some fast heuristics for this problem. We evaluate the performance of the heuristics through simulation studies.