LAUSR

While energy consumption and Quality of Service (QoS) are primary concerns for the design of embedded systems, reliability requirement has become increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for reducing the energy consumption for (m, k)-hard embedded real-time systems, which requires that at least m out of any k consecutive jobs of a real-time task meet their deadlines. In order to ensure the (m, k)-hard requirement while preserving the system reliability, we propose to reserve recovery space for real-time jobs in an adaptive way based on the mandatory/optional job partitioning strategy. Moreover, efficient off-line/online scheduling techniques are proposed to reduce the overall energy consumption while satisfying the reliability requirement. Through extensive simulations, our experiment results demonstrate that the proposed techniques significantly outperformed the previous research in reducing energy consumption for (m, k)-hard embedded real-time systems while preserving the system reliability.