CMOS scaling has greatly increased concerns for both lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the user's concern is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. This paper introduces a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we tackle the problem of maximizing availability for multicore real-time systems with consideration of permanent and transient faults. A framework is proposed to solve the system availability maximization problem. Experimental results on a hardware board and simulation results of synthetic tasks show that our scheme significantly improves system MTTF (and hence availability) compared with existing techniques.

In recent years, the Internet of Things (IoT) has promoted many battery-powered emerging applications, such as smart home, environmental monitoring, and human healthcare monitoring, where energy management is of particular importance. Meanwhile, there is an accelerated tendency toward mobility of IoT devices, either being transported by humans or being mobile by itself. Existing energy management mechanisms for battery-powered IoT fail to consider the two significant characteristics of IoT: 1) the approximate real-time computation and 2) the mobility of IoT devices, resulting in unnecessary energy waste and network lifetime decay. In this paper, we explore mobility-aware network lifetime maximization for battery-powered IoT applications that perform approximate real-time computation under the quality-of-service (QoS) constraint. The proposed scheme is composed of offline and online stages. At offline stage, an optimal mobility-aware task schedule that maximizes network lifetime is derived by using mixed-integer linear programming technique. Redundant executions due to mobility-incurred overlapping of a single task on different IoT devices are avoided for energy savings. At online stage, a performance-guaranteed and time-efficient QoS-adaptive heuristic based on cross-entropy method is developed to adapt task execution to the fluctuating QoS requirements. Extensive simulations based on synthetic applications and real-life benchmarks have been implemented to validate the effectiveness of our proposed scheme. Experimental results demonstrate that the proposed technique can achieve up to 169.52% network lifetime improvement compared to benchmarking solutions.

Federated learning (FL), an emerging distributed machine learning (ML) technique, allows massive embedded devices and a server to work together for training a global ML model without collecting user data on a server. Most existing approaches adopt the traditional centralized FL paradigm with a single server: one is the cloud-centric FL paradigm and the other is the edge-centric FL paradigm. The cloud-centric FL paradigm is able to manage a large-scale FL system across massive user devices with high communication cost, whereas the edge-centric FL paradigm is capable of coordinating a small-scale FL system benefiting from the low communication delay over wireless networks. To fully exploit the advantages of both, in this article, we develop a distinctive hierarchical FL framework for the promising mobile-edge cloud computing (MECC) system, called HELCHFL, to achieve high-efficiency and low-cost hierarchical FL training. In particular, we formulate the corresponding theoretical foundation for our HELCHFL to ensure hierarchical training performance. Furthermore, to address the inherent communication and user heterogeneity issues of FL training, our HELCHFL develops a utility-driven and heterogeneity-aware heuristic user selection strategy to enhance training performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCHFL introduces a device operating frequency determination approach to reduce training energy cost. Experiments demonstrate that our HELCHFL can enhance the highest accuracy by up to 52.93%, gain the training speedup of up to 483.74%, and obtain up to 45.59% training energy savings compared to state-of-the-art baselines.

Cyber-physical cloud systems (CPCS) are integrations of cyber-physical systems (CPS) and cloud computing infrastructures. Integrating CPS into cloud computing infrastructures could improve the performance in many aspects. However, new reliability and security challenges are also introduced. This fact highlights the need to develop novel methodologies to tackle these challenges in CPCS. To this end, this study is oriented toward enhancing the soft-error reliability of real-time workflows on CPCS while satisfying the lifetime reliability, security, and real-time constraints. In this study, we propose a dependable algorithm for scheduling workflow applications on CPCS. The proposed algorithm uses slack to recover failed tasks and allows all tasks to share the available slack in the system. To improve soft-error reliability, the algorithm first determines the priority of tasks, then assigns the maximum frequency to each task, and finally assigns the recoveries to tasks dynamically. Slack also can be used to utilize security services for satisfying system security requirements. The lifetime reliability constraint is met by dynamically scaling down the operating frequency of low-priority tasks. Extensive experiments on real-world workflow benchmarks demonstrate that the proposed scheme reduces the probability of failure by up to 52.1% and improves the scheduling feasibility by up to 83.5% compared to a number of representative approaches.