Reliability-aware Power Management Research Articles

Energy-Efficient Fault-Tolerant Mapping and Scheduling on Heterogeneous Multiprocessor Real-Time Systems

Energy saving and system reliability are two crucial issues for designing modern multiprocessor systems. There has been reliability-aware power management with dynamic voltage–frequency scaling (DVFS) schemes in recent studies. However, they are limited to optimization under the impact of DVFS on energy and reliability and have not considered reducing the non-negligible leakage energy consumption. In this paper, we focus on co-management of system reliability and total energy for applications with precedence constrained tasks on heterogeneous multiprocessor real-time systems. We first investigate the impact of energy management techniques on both reliability and energy of the systems using task recovery for fault tolerance and then propose an Energy-efficient Fault-tolerant Scheduling (EFS) scheme integrated with power mode management, which can mitigate the negative impact of DVFS on system reliability. To obtain the optimal energy-efficient reliability-guaranteed scheduling for pre-mapped applications on systems considering various realistic issues, we build mixed integer linear programing formulations with the proposed EFS scheme. To address mapping and scheduling for energy-efficiency and fault-tolerance, we finally develop a framework implemented by a List-based Binary Particle Swarm Optimization algorithm. The extensive comparative evaluations for synthetic and realistic benchmarks show that our approaches outperform several related studies in terms of energy consumption and system reliability.

A Comparative Study of Reliability-Ignorant and Reliability-Aware Energy Management Schemes Using UPPAAL-SMC

Energy saving and high reliability are two key concerns in the design of real time systems. However, high reliability and low energy consumption are conflicting objects, and they are generally contrasted with temporal correctness. In this paper, we propose comparing reliability-ignorant and reliability-aware power management schemes with statistical model checking approach using UPPAAL-SMC. The power management schemes and the relevant components are modeled in the form of stochastic timed automata. And the analysis objectives are expressed as verification queries. With the model and queries as inputs, UPPAAL-SMC returns the probability of system failure and the expected value of energy consumption. In this analysis, we have considered three reliability-ignorant power management schemes and two reliability-aware power management schemes. Based on the comparative study, we provided guidelines for choosing the suitable scheme for a given system. One thing that should be emphasized is that our methodology is not limited to the schemes involved in this paper. The modeling and evaluating procedure can be applied to analyse other energy management schemes in practical application.

Joint optimization of energy efficiency and system reliability for precedence constrained tasks in heterogeneous systems

Voltage scaling is a fundamental technique in the energy efficient computing field. Recent studies tackling this topic show degraded system reliability as frequency scales. To address this conflict, the subject of reliability aware power management (RAPM) has been extensively explored and is still under investigation. Heterogeneous Computing Systems (HCS) provide high performance potential which attracts researchers to consider these systems. Unfortunately, the existing scheduling algorithms for precedence constrained tasks with shared deadline in HCS do not adequately consider reliability conservation. In this study, we design joint optimization schemes of energy efficiency and system reliability for directed acyclic graph (DAG) by adopting the shared recovery technique, which can achieve high system reliability and noticeable energy preservation. To the best of our knowledge, this is the first time to address the problem in HCS. The extensive comparative evaluation studies for both randomly generated and some real-world applications graphs show that our scheduling algorithms are compelling in terms of enhancement of both system reliability and energy saving.

Global scheduling based reliability-aware power management for multiprocessor real-time systems

Reliability-aware power management (RAPM) has been a recent research focus due to the negative effects of the popular power management technique dynamic voltage and frequency scaling (DVFS) on system reliability. As a result, several RAPM schemes have been studied for uniprocessor real-time systems. In this paper, for a set of frame-based independent real-time tasks running on multiprocessor systems, we study global scheduling based RAPM (G-RAPM) schemes. Depending on how recovery blocks are scheduled and utilized, both individual-recovery and shared-recovery based G-RAPM schemes are investigated. An important dimension of the G-RAPM problem is how to select the appropriate subset of tasks for energy and reliability management (i.e., scale down their executions while ensuring that they can be recovered from transient faults). We show that making such decision optimally (i.e., the static G-RAPM problem) is NP-hard. Then, for the individual-recovery based approach, we study two efficient heuristics, which rely on local and global task selections, respectively. For the shared-recovery based approach, a linear search based scheme is proposed. The schemes are shown to guarantee the timing constraints. Moreover, to reclaim the dynamic slack generated at runtime from early completion of tasks and unused recoveries, we also propose online G-RAPM schemes which exploit the slack-sharing idea studied in previous work. The proposed schemes are evaluated through extensive simulations. The results show the effectiveness of the proposed schemes in yielding energy savings while simultaneously preserving system reliability and timing constraints. For the static version of the problem, the shared-recovery based scheme is shown to provide better energy savings compared to the individual-recovery based scheme, in virtue of its ability to leave more slack for DVFS. Moreover, by reclaiming the dynamic slack generated at runtime, online G-RAPM schemes are shown to yield better energy savings.