With the advancement of technology size and the integration of multiple cores on a single chip, the probability of fault occurrence has increased. These faults can be transient or permanent, requiring techniques to manage both types. Hybrid fault tolerance techniques have emerged as effective solutions to handle both types. In this paper, we propose a power-aware hybrid fault tolerance (called Pilot). Our approach utilizes checkpointing with rollback-recovery and primary/backup techniques, tolerating two kinds of faults. Moreover, in real-time embedded systems, power consumption is a critical constraint that must be managed. To do this, we exploit the Thermal Safe Power (TSP) constraint for each processing core. Based on this constraint and the utilization of each core, tasks are mapped and scheduled, while guaranteeing the timing constraints. Our experimental results demonstrate that our proposed methods can meet the reliability target by tolerating the optimal number of fault occurrences in each task while reducing power consumption. Our proposed methods are compared to state-of-the-art techniques in terms of schedulability, power consumption, Quality of Service (QoS), energy consumption, and reliability. The peak power and energy consumption are reduced on average by 34.2% and 15.9%, respectively, the QoS is improved on average to 28.7%, and the schedulability is improved on average to 14.6% while satisfying the system reliability target.

Energy efficiency is a critical design constraint in multicore Cyber-Physical Systems (CPS). Using energy management methods can violate timing constraints; hence, designing scheduling policies that can adapt to the dynamic and unpredictable nature of aperiodic real-time tasks remains a significant challenge. This paper introduces LEETMIC, a novel deep reinforcement learning framework for energy-aware real-time scheduling. To manage a variable number of active jobs, LEETMIC utilizes a learned policy network to determine the scheduling priority and Dynamic Voltage and Frequency Scaling (DVFS) level for each task individually, based on a combination of the task's local attributes and a summary of the global system state. The policy is trained using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to co-optimize for both task schedulability and energy consumption. The trained policy neural network is a compact multi-layer perceptron (MLP), making it suitable for online deployment in embedded systems. Experimental results demonstrate that, compared to the Global Earliest Deadline First (GEDF) scheduler, LEETMIC achieves a similar success ratio while significantly reducing energy consumption by 41.5% on average (up to 70%).

In multicore Internet of Things (IoT) devices, high reliability, low power/energy design, and real-time computing are three important design requirements. The multiple cores in these systems enable us to exploit different fault-tolerance techniques for improving system reliability. However, these techniques impose power/energy and timing overheads on the system. This paper proposes DReaM, a power- and reliability-aware approach that utilizes Deep Reinforcement Learning (DRL) to minimize power consumption in multicore systems while maintaining the desired reliability level and meeting timing constraints. DReaM controls the power consumption by adjusting the voltage and frequency levels for each task and choosing the most suitable fault-tolerant techniques between Primary/Backup (P/B), Triple Modular Redundancy (TMR), and N-Modular Redundancy (NMR). Evaluations using an ARM-based processor and applications from the MiBench benchmark suite were conducted under different fault injection scenarios. The experimental results show that DReaM reduces power consumption on average by 19.86% (up to 64.1%), with improved Quality of Service (QoS) by an average of 11.02% (up to 77%) while maintaining the required system reliability.

With the emergence of the mobile-edge computing (MEC) paradigm, many challenges related to the cloud computing paradigm were addressed, including bandwidth consumption, high latency, nonreal-time computing, etc. This makes the MEC paradigm a promising technology for real-time applications, such as healthcare, automotive vehicles, smart cities, virtual reality, etc. Since MEC environments operate on wireless networks, one of the open problems in the MEC paradigm is task allocation strategies. Reliable task allocation in such environments directly impacts the network’s Quality of Service (QoS). This article proposes RASOUL, a learning-based task allocation strategy to improve the overall task success ratio and energy consumption in MEC environments. We have formulated the reliability-aware task allocation problem to improve task success ratio and reduce energy consumption and conducted experiments on the proposed method to evaluate its performance compared to state-of-the-art methods. Experimental results demonstrate that RASOUL can achieve an average improvement in QoS of 10.1% (up to 16.3%) and 18.7% (up to 22.1%) in task success ratio while reducing energy consumption by an average of 38.1% compared to other task allocation strategies.