While Dynamic Voltage Scaling (DVS) remains as a popular energy management technique for modern computing systems, recent research has identified significant and negative impacts of voltage scaling on system reliability. To preserve system reliability under DVS settings, a number of reliability-aware power management (RA-PM) schemes have been recently studied. However, the existing RA-PM schemes normally schedule a separate recovery for each task whose execution is scaled down and are rather conservative. To overcome such conservativeness, we study in this article novel RA-PM schemes based on the shared recovery (SHR) technique. Specifically, we consider a set of frame-based real-time tasks with individual deadlines and a common period where the precedence constraints are represented by a directed acyclic graph (DAG). We first show that the earliest deadline first (EDF) algorithm can always yield a schedule where all timing and precedence constraints are met by considering the effective deadlines of tasks derived from as late as possible (ALAP) policy, provided that the task set is feasible. Then, we propose a shared recovery based frequency assignment technique (namely SHR-DAG) and prove its optimality to minimize energy consumption while preserving the system reliability. To exploit additional slack that arises from early completion of tasks, we also study a dynamic extension for SHR-DAG to improve energy efficiency and system reliability at runtime. The results from our extensive simulations show that, compared to the existing RA-PM schemes, SHR-DAG can achieve up to 35% energy savings, which is very close to the maximum achievable energy savings. More interestingly, our extensive evaluation also indicates that the new schemes offer non-trivial improvements on system reliability over the existing RA-PM schemes as well.

The negative impact of the popular energy management technique Dynamic Voltage and Frequency Scaling (DVFS) on the reliability of real-time embedded systems, in terms of increased transient fault rates, has been recently identified. As a result, recent research literature includes a number of solutions within the so-called Reliability-Aware Power Management (RA-PM) framework, where the aim is to preserve the system's original reliability. In this research effort, we propose a more general framework where the aim is to achieve arbitrary reliability levels that may vary for each periodic task. A critical component of our solution is the use of dynamically allocated recoveries: we show that providing a relatively modest recovery allowance to a given periodic task helps to achieve surprisingly high reliability levels as long as these allowances can be reclaimed on-demand during the hyper period. We propose a pseudo-polynomial time feasibility test, as well as static and dynamic algorithms to determine the recovery allowance and frequency assignments to minimize energy consumption while satisfying timing and reliability constraints. Our experimental evaluation points to the significant gain potential of the new framework in terms of both energy and reliability figures.

DVFS remains an important energy management technique for embedded systems. However, its negative impact on transient fault rates has been recently shown. In this paper, we propose the Generalized Shared Recovery (GSHR) technique to optimally use the DVFS technique in order to achieve a given reliability goal for real-time embedded applications. Our technique determines the optimal number of recoveries to deploy as well as task-level processing frequencies to minimize the energy consumption while achieving the reliability goal and meeting the timing constraints. The recoveries may be shared among tasks, improving the prospects of DVFS compared to existing reliability-aware power management frameworks. The experimental evaluation points to the close-to-optimal energy savings of our proposed technique.

Mixed-criticality real-time systems, where tasks may be associated with different criticality and assurance levels, have attracted much attention in the recent past. In this paper, we consider partitioning-based multiprocessor scheduling of mixed- criticality real-time task sets. Guaranteeing feasibility in this setting is shown to be NP-Hard. With a focus on fixed-priority preemptive scheduling on each processor, we identify the two main aspects of the problem, namely the task allocation and priority assignment dimensions. For the task allocation dimension, we propose and compare bin-packing-inspired heuristics, based on offline task ordering according to utilization and criticality. For the priority assignment dimension, we compare the well- known Rate Monotonic priority assignment policy with Audsley's priority assignment algorithm. Through simulations, we also assess and discuss the relative importance of these two primary dimensions on the overall mixed-criticality feasibility problem for multiprocessor platforms.

The dynamic voltage and frequency scaling (DVFS) technique is the basis of numerous state-of-the-art energy management schemes proposed for real-time embedded systems. However, recent research has illustrated the alarmingly negative impact of DVFS on task and system reliability. In this paper, we consider the problem of assigning processing frequencies to a set of real-time tasks in order to maximize the overall reliability, under given time and energy constraints. First, under the frame-based task model, we formulate the problem as a nonlinear optimization problem and show how to obtain the static optimal solution. Then, we propose online (dynamic) algorithms that detect early completions and adjust the task frequencies at runtime, to improve overall reliability. Furthermore, we extend these solutions to the periodic task model, with both static and dynamic solutions. All our solutions ensure that all timing constraints are met while the cumulative energy consumption of tasks does not exceed the given energy budget. Our simulation results indicate that our algorithms perform comparably to a clairvoyant optimal scheduler that knows the exact workload in advance.

While Dynamic Voltage Scaling (DVS) and Dynamic Power Management (DPM) techniques are widely used in real-time embedded applications, their complex interaction is not fully understood. In this research effort, we consider the problem of minimizing the expected energy consumption on settings where the workload is known only probabilistically. By adopting a system-level power model, we formally show how the optimal processing frequency can be computed efficiently for a real-time embedded application that can use multiple devices during its execution, while still meeting the timing constraints. Our evaluations indicate that the new technique provides clear (up to 35%) energy gains over the existing solutions that are proposed for deterministic workloads. Moreover, in a non-negligible part of the parameter spectrum, the algorithm's performance is shown to be close to that of a clairvoyant algorithm that can minimize the energy consumption with the advance knowledge about the exact workload.

While Dynamic Voltage Scaling (DVS) remains as a popular energy management technique for real-time embedded applications, recent research has identified significant and negative impact of voltage scaling on system reliability. For this reason, a number of reliability-aware power management (RA-PM) schemes were recently proposed to preserve the system reliability when DVS is used. In this paper, we propose a new approach, called the shared recovery (SHR) technique, to minimize the system-level energy consumption while still preserving the system’s original reliability. The main idea of the SHR technique is to avoid the offline allocation of separate recovery tasks to the scaled tasks by assigning a global/shared recovery block that can be used by any task at run-time. Our simulation results show that, compared to the existing RA-PM schemes, our scheme can achieve up to 35 % energy savings. Further, this performance is shown to be comparable to the maximum energy savings thatcanbeachievedbyanyalgorithm. Interestingly,ourextensive evaluation indicates that SHR offers also non-trivial gains over the previous algorithms on the reliability side. Further, a dynamic extension is proposed to improve energy and reliability management at run-time by reducing the size of the recovery block and re-using the slack that arises from early completions.

The dynamic voltage scaling (DVS) technique is the basis of numerous state-of-the-art energy management schemes proposed for real-time embedded systems. However, recent research has illustrated the alarmingly negative impact of DVS on task and system reliability. In this paper, we consider the problem of processing frequency assignment to a set of real-time tasks in order to maximize the overall reliability, under given time and energy constraints. First, we formulate the problem as a non-linear optimization problem and show how to obtain the static optimal solution. Then, we propose on-line (dynamic) algorithms that detect early completions and adjust the task frequencies at run-time, to improve overall reliability. Our simulation results indicate that our algorithms perform comparably to a clairvoyant optimal scheduler that knows the exact workload in advance.