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 any k consecutive 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.

Time-sensitive networking (TSN) is an industry-standard networking protocol that is widely deployed in safety-critical industrial and automotive networks thanks to its quality-of-service (QoS) mechanisms, esp. deterministic transmission and bounded end-to-end delay for time-triggered (TT) flows. In this article, we focus on TT flows and address the issue of fault tolerance against permanent and transient faults with both spatial and temporal redundancy. We present an efficient heuristic algorithm for online incremental rerouting and rescheduling of disrupted flows, assuming the paths and schedules of existing flows stay fixed. It is complementary to and can be combined with offline routing and scheduling algorithms for achieving fault tolerance based on frame replication and elimination for reliability (FRER) (IEEE 802.1CB). Performance evaluation shows that our approach is able to better recover the system’s degree of redundancy (DoR) and has a higher acceptance rate than related work.

For real-time computing systems, energy efficiency, quality of service (QoS), and fault tolerance are among the major design concerns. In this work, we study the problem of reliable and energy-aware$(m,k)$-deadlines enforcement using standby sparing under the fixed-priority assignment. 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 kinds of systems, we proposed two novel scheduling schemes under the QoS constraint of$(m,k)$-deadlines: one for task sets partitioned with deeply red pattern and one for task sets partitioned with evenly distributed pattern. The evaluation results demonstrate that our proposed approaches significantly outperformed the previous research in energy conservation while assuring$(m,k)$-deadlines and fault tolerance for real-time systems.

While energy consumption is the primary concern for the design of real-time embedded systems, fault-tolerance and quality of service (QoS) are becoming increasingly important in the development of today's pervasive computing systems. In this work, we study the problem of energy-aware standby-sparing for weakly hard real-time embedded systems. 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 two novel scheduling schemes: one for (1,1)-hard tasks and one for general (m,k)-hard tasks which require that at least m out of any k consecutive jobs of a task meet their deadlines. Through extensive evaluations, our results demonstrate that the proposed techniques significantly outperform the previous research in reducing energy consumption for both (1,1)-hard task sets and general (m,k)-hard task sets while assuring fault tolerance through standby-sparing.

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 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 weakly hard real-time systems. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. With the purpose of ensuring the feasibility of such kind of systems, we firstly present two novel scheduling schemes for the standby-sparing systems with tighter energy budget constraint than the traditional ones. Then based on them a hybrid approach is proposed to achieve better performance. 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 given energy budget constraint.

For real-time computing systems, energy efficiency and reliability are two primary design concerns. In this research work, we study the problem of enhanced energy-aware standby-sparing for fixed-priority (FP) hard real-time systems under reliability requirement. The standby-sparing system adopts a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to keep the energy consumption for such kind of systems under control, we explore enhanced fixed-priority scheduling schemes to minimize the overlapped concurrent executions of the workloads on the primary processor and on the spare processor, enabling energy savings. Moreover, efficient online scheduling techniques are under development to boost the energy savings during runtime while preserving the system reliability.

The uncertainty due to performance variations of IC chips and resource sharing on multi-core platforms have significantly degraded the predictability of real-time systems. Traditional deterministic approaches based on the worst-case assumptions become extremely pessimistic and thus unpractical. In this article, we address the problem of scheduling a set of fixed-priority periodic real-time tasks on multi-core platforms in a probabilistic manner. Specifically, we consider task execution time as a probabilistic distribution and study how to schedule these tasks on multi-core platforms with guaranteed Quality of Service (QoS) requirements in terms of deadline-missing probabilities. Moreover, it is a well-known fact that the relationship among task periods, if exploited appropriately, can significantly improve the processor utilization. To this end, we present a novel approach to partition real-time tasks that can take both task execution time distributions and their period relationships into consideration. From our extensive experiment results, our proposed methods can greatly improve the schedulability of real-time tasks when compared with existing approaches.

In this paper, we explore reducing the rate for missing the Quality of Service (QoS) constraint for overloaded real-time systems. The QoS constraint is modeled using (m, k)-constraints, which require that at least m out of any k consecutive jobs of a task meet their deadlines. We proposed a dynamic scheduling scheme to satisfy the (m, k)-constraints for real-time tasks. The simulation results demonstrate that our proposed techniques have great potential in reducing the (m, k)-missing rate for overloaded real-time systems.

For real-time embedded systems, schedulability and energy efficiency are two highly co-related important design issues. In this paper, we explore how to improve the schedulability and energy efficiency for real-time systems with weakly hard temporal constraints. The weakly hard temporal constraints are modeled by the (m, k)-constraints, which require that at least m out of any k consecutive jobs of a task meet their deadlines. Two off-line approaches are proposed for managing the appropriate mandatory/optional job partition for a given task set with (m, k)-constraints. Through extensive experiments, the results demonstrate that our proposed techniques significantly outperform previous approaches in both schedulability and energy performance for weakly hard real-time embedded systems.

In this paper, we present a system level dynamic scheduling algorithm to minimize the energy consumption by the DVS processor and multiple non-DVS peripheral devices in a hard real-time system. We show that the previous work which adopts the critical speed as the lower bound for scaling might not be most energy efficient when the energy overhead of shutting-down/waking-up is not negligible. Moreover, the widely used statically defined break even idle time might not be overall energy efficient due to its independence of job execution situations. In our approach, we first present an approach to enhance the computation of break even idle time dynamically. Then a dynamic scheduling approach is proposed in the management of speed determination and task preemption to reduce the energy consumption of the processor and devices. Compared with existing research, our approach can effectively reduce the system-level energy consumption for both CPU and peripheral devices.

In this paper, we present system-wide dynamic scheduling algorithms to reduce the energy consumption by both the core DVS processor and multiple non-DVS peripheral devices for hard real-time systems scheduled with rate-monotonic scheduling (RMS) scheme. In our research, we first present an approach to leverage the use of the critical speed strategy and the traditional DVS strategy based on the job workload to be finished within certain interval. Then dynamic scheduling approaches are proposed in the management of speed determination and device shut-down to reduce the energy at the system level. Compared with existing research, our approach can effectively reduce the overall energy consumption for both CPU and peripheral devices.

In this paper, we study the problem of reducing the energy consumption for hard real-time systems based on fixed-priority (FP) scheme. To balance the static and dynamic energy consumption, the concept of critical speed was proposed. Moreover, when combined with the processor shutdown strategy, the critical speed was widely used as the lower bound for voltage scaling in literature. In this paper, we show that the critical speed strategy might not always be more energy efficient than the traditional DVS strategy and there is a tradeoff between these two strategies. Based on it, we propose an off-line approach to set up the energy efficient static speed schedule for real-time tasks. The simulation results demonstrate that our proposed techniques can effectively reduce the energy consumption for hard realtime systems.

While the dynamic voltage scaling (DVS) techniques are efficient in reducing the dynamic energy consumption for the processor, varying voltage alone becomes less effective for the overall energy reduction as the static power is growing rapidly. On the other hand, Quality of Service (QoS) is also a primary concern in the development of today's pervasive computing systems. In this paper, we propose a dynamic approach to minimize the overall energy consumption for soft real-time systems while ensuring the QoS-guarantee. The QoS requirements are deterministically quantified with the window-constraints, which require that at least m out of each non-overlapped window of k consecutive jobs of a task meet their deadlines. Necessary and sufficient conditions for checking the feasibility of task sets with arbitrary service times and periods are developed to ensure that the window-constraints can be guaranteed in the worst case. And efficient scheduling techniques based on pattern variation and dynamic slack reclaiming extensions are proposed to combine the task procrastination and dynamic slowdown to minimize the energy consumption. In contrast to the previous leakage-aware slack reclaiming work which never scales the job speed below the critical speed, we will show that it can be more energy efficient to reclaim the slack with speed lower than the critical speed when necessary. Through extensive simulations, our experiment results demonstrate that the proposed techniques significantly outperformed the previous research in both overall and idle energy reduction.

In this paper, we present a dynamic scheduling algorithm to minimize the energy consumption by both the DVS processor and peripheral devices in a weakly hard real-time system. In our approach, we first use a new static approach to partition real-time jobs into mandatory and optional part to meet the weakly hard real-time constraints. We then adopt an on-line approach that can effectively exploit the run-time variations and reduce the preemption impacts to leverage the energy saving performance. Extensive simulation studies demonstrate that our approach can effectively reduce the system-wide energy consumption while guaranteeing the weakly hard constraints

Energy reduction is critical to increase the mobility and battery life for today's pervasive portable computing systems. At the same time, energy reduction must be subject to the real-time constraints and quality of service (QoS) requirements for multimedia applications running on many of these systems. This paper presents a novel run-time scheduling approach to reduce the system-wide energy consumption for such systems. In this paper, the multimedia applications are modeled using a popular weakly hard realtime model, i.e., the (m,k)-model. Our experimental results show that, by judiciously scheduling the real-time tasks and shutting down the processor and/or peripheral devices, our approach can lead to significant energy savings while guaranteeing the (m,k) firm deadlines at the same time

Energy consumption and quality of service (QoS) are two primary concerns in the development of today's pervasive computing systems. While most of the current research in energy-aware real-time scheduling has been focused on hard real-time systems, a large number of practical applications and systems exhibit more soft real-time nature. In this paper, we study the problem of minimizing energy for soft real-time systems while providing a QoS guarantee. The QoS requirements are deterministically quantified with the (m,k)-constraints, which require that at least m out of any k consecutive jobs of a task meet their deadlines. In this paper, we propose a hybrid approach to achieve the dual goals of QoS guarantee and energy minimization. We first present the necessary and sufficient schedulability conditions for the static mandatory/optional workload partitioning. Then, we propose to dynamically vary the statically defined mandatory/optional partitions to accommodate dynamic run-time variations while minimizing the energy consumption. The experimental results demonstrate that our proposed techniques outperform previous work significantly in terms of both the energy savings and achieved QoS.

Energy reduction is critical to increase the mobility and to extend the mission period in the development of today's pervasive computing systems. On the other hand, however, energy reduction must be subject to the requirements not to compromise the quality of service (QoS) that these systems need to provide. While most of the current research in energy-aware real-time scheduling has been focused on hard real-time systems, a large number of practical applications and systems exhibit more soft real-time nature. In this paper, we study the problem of minimizing energy for soft real-time systems with the requirements of QoS-guarantee. The QoS requirements are deterministically quantified with the (m, k)-constraints, which require that at least m out of any k consecutive jobs of a task meet their deadlines. To deal with the dynamic characteristics of such applications and systems, we propose a hybrid static/dynamic scheduling approach that can efficiently reduce the energy consumption while guaranteeing the (m, k)-constraints. The experimental results demonstrate that our proposed techniques outperform previous research significantly in terms of both the energy savings and QoS that can be achieved.

While the dynamic voltage scaling (DVS) techniques are efficient in reducing the dynamic energy consumption for the processor, varying voltage alone becomes less effective for the overall power reduction as the leakage power is growing rapidly, i.e., five times per technical generation as predicted. In this paper, we study the problem of reducing both the static and dynamic power consumption at the same time for the hard real-time system scheduled by the earliest deadline first (EDF) strategy. To balance the dynamic and leakage energy consumption, higher-than-necessary processor speeds may be required when executing real-time tasks, which can result in a large number of idle intervals. To effectively reduce the energy consumption during these idle intervals, we propose a technique that can effectively merge these scattered intervals into larger ones without causing any deadline miss. Simulation studies demonstrate the effectiveness of our approach. Specifically, our experiments show that the proposed technique can lead up to more than 80% idle energy savings than that by the previous ones.

While dynamic voltage scaling (DVS) is an efficient technique in reducing the dynamic energy consumption of a CMOS processor, methods that employ DVS without considering leakage current are quickly becoming less efficient when considering the processor's overall energy consumption. A leakage conscious DVS voltage schedule may require the processor to run at a higher-than-necessary speed to execute a given set of real-time tasks, which can result in a large number of idle intervals. To effectively reduce the energy consumption during these idle intervals, and therefore the overall energy consumption, the DVS schedule must judiciously allow the processor to enter and leave the power down state during these idle intervals, while considering the time and energy cost of doing so. In this paper, we present a scheduling technique that can effectively reduce the overall energy consumption for hard real-time systems scheduled according to a fixed priority (FP) scheme. Experimental results demonstrate that a processor using our strategy consumes as less as 15% of the idle energy of a processor employing the conventional strategy.