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Fault tolerance, energy management, and quality of service (QoS) are essential aspects for the design of real-time embedded systems. In this work, we focus on exploring methods that can simultaneously address the above three critical issues under standby-sparing. The standby-sparing mechanism adopts a dual-processor architecture in which each processor plays the role of the backup for the other one dynamically. In this way, it can provide fault tolerance subject to both permanent and transient faults. Due to its duplicate executions of the real-time jobs/tasks, the energy consumption of a standby-sparing system could be quite high. With the purpose of reducing energy under standby-sparing, we proposed three novel scheduling schemes: The first one is for (1, 1)-constrained tasks, and the second one and the third one (which can be combined into an integrated approach to maximize the overall energy reduction) are for general (m,k)-constrained tasks that require that among anykconsecutive jobs of a task no more than (k-m) out of them could miss their deadlines. Through extensive evaluations and performance analysis, our results demonstrate that compared with the existing research, the proposed techniques can reduce energy by up to 11% for (1, 1)-constrained tasks and 25% for general (m,k)-constrained tasks while assuring (m,k)-constraints and fault tolerance as well as providing better user perceived QoS levels under standby-sparing. 

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and aperiodic tasks in a weakly hard real-time environment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. For such kind of systems, we firstly propose several novel standby-sparing schemes for the periodic tasks which can ensure the system feasibility under tighter energy budget constraint than the traditional ones. Then based on them integrated approachs for both periodic and aperiodic tasks are proposed to minimize the aperiodic response time whilst achieving better energy and QoS performance under the given energy budget constraint. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state of the art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under the given energy budget constraint. 

For real-time computing systems, energy efficiency, Quality of Service, and fault tolerance are among the major design concerns. In this work, we study the problem of reliable and energy-aware fixed-priority (m,k)-deadlines enforcement with standby-sparing. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to reduce energy consumption for such kind of systems, we proposed a novel scheduling scheme under the QoS constraint of (m,k)- deadlines. The evaluation results demonstrate that our proposed approach significantly outperformed the previous research in energy conservation while assuring (m,k)-deadlines and fault tolerance for real-time systems. 

For real-time computing systems, energy efficiency, Quality of Service, and fault tolerance are among the major design concerns. In this work, we study the problem of reliable and energy-aware fixed-priority (m,k)-deadlines enforcement with standby-sparing. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to reduce energy consumption for such kind of systems, we proposed a novel scheduling scheme under the QoS constraint of (m,k)- deadlines. The evaluation results demonstrate that our proposed approach significantly outperformed the previous research in energy conservation while assuring (m,k)-deadlines and fault tolerance for real-time systems.