As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system’s SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods. Note to Practitioners—This article is motivated by the safety-critical industrial cyber-physical system (ICPS) applications necessitating reliable execution and long lifespan, which could be realized by increasing reliability and controlling operating temperature. Our goal is to improve the system reliability of CPU- and GPU-integrated multiprocessor systems-on-chip (MPSoCs) deployed in ICPS under the temperature constraint. Most of the existing papers target either reliability or temperature. A few recent papers have focused on reliability and temperature optimization simultaneously. However, they are not designed for ICPS and do not consider the widely accepted CPU- and GPU-integrated MPSoC platforms. This article proposes a machine learning-based approach that trains an artificial neural network (ANN) to facilitate the online estimation of system SER and temperature. Compared to the offline estimation using simulation tools, the online approach is more applicable to real-time ICPS applications. This article also designs a feedback control-based approach for improving system reliability and reducing peak temperature of the CPU- and GPU-integrated MPSoCs by determining the number of tasks to be admitted and the number of replicas for tasks.

In-situ(InS) server systems are typically deployed in special environments to handleInSworkloads which are generated from environmentally sensitive areas or remote places lacking modern power supply infrastructure. This special operating environment ofInSservers urges such systems to be powered by renewable energy. In addition, theInSsystems are vulnerable to soft errors due to the harsh environments they deploy. This article tackles the problem of allocating harvested energy to renewable powered servers and assigning theInSworkloads to these servers for optimizing throughput of both the overall system and individual servers under energy and reliability constraints. We perform the energy allocation based on system state. In particular, for systems in low energy state, we propose a game theoretic approach that models the energy allocation as a cooperative game among multiple servers and derives a Nash bargaining solution. To meet the reliability constraint, we analyze the reliability optimality of assigning tasks to multiple servers and design a reliability-aware task assignment heuristic based on the analysis. Experimental results show that with a small time overhead, the proposed energy allocation approach achieves a high throughput from perspectives of both the overall system and individual servers, and the proposed task assignment approach ensures an increased system reliability.

Heterogeneous multiprocessor system-on-chips (MPSoCs) are suitable platforms for real-time embedded applications that require powerful parallel processing capability as well as low power consumption. For such applications, soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are both key concerns. There have been several efforts in the literature oriented toward related reliability problems. However, most existing techniques only concentrate on improving one of the two reliability metrics, which are not suitable for embedded systems deployed in critical applications in need of a long lifetime as well as a reliable execution. This article develops a novel heterogeneous earliest-finish-time (HEFT)-based algorithm to maximize SER and LTR simultaneously under the real-time constraint for dependent tasks executing on heterogeneous MPSoC systems. More specifically, a new deadline-constrained reliability-aware HEFT algorithm, namely DRHEFT, is proposed, which seeks for the best SER–LTR tradeoff solutions through using fuzzy dominance to evaluate the relative fitness values of candidate solutions. The extensive experiments on real-life benchmarks as well as synthetic applications demonstrate that DRHEFT is capable of achieving better SER–LTR tradeoff solutions with higher hypervolume and less computation cost when compared with the state-of-the-art approaches.

Internet of Things (IoT) devices, such as intelligent road side units and video-based detectors, are being deployed in emerging applications like sustainable and intelligent transportation systems. The primary obstacles against the development of these IoT devices are various security threats and huge energy consumption. In this article, we study the problem of scheduling tasks onto a heterogeneous multiprocessor system on a chip (MPSoC) deployed in IoT for optimizing quality of security under energy, real-time, and task precedence constraints. We first provide a mixed-integer linear programming (MILP) formulation for allocating and scheduling dependent tasks with energy and real-time constraints on a heterogeneous MPSoC system to maximize system quality of security. In order to efficiently solve the formulated MILP, we then propose an analysis-based two-stage scheme that determines the allocation, operating frequency, and security service of tasks to maximize system quality of security while satisfying the design constraints. We finally carry out extensive simulation experiments to validate our proposed two-stage scheme and MILP approach. Simulation results demonstrate that the proposed two-stage scheme outperforms a number of representative existing approaches in saving energy and improving system quality of security. The results also show that the proposed MILP approach can achieve the best performance and the proposed two-stage scheme has a close performance to the MILP approach.

Multiprocessor system-on-chip (MPSoC) has been widely used in many real-time embedded systems where both soft-error reliability (SER) and lifetime reliability (LTR) are key concerns. Many existing works have investigated them, but they focus either on handling one of the two reliability concerns or on improving one type of reliability under the constraint of the other. These techniques are thus not applicable to maximize SER and LTR simultaneously, which is highly desired in some real-world applications. In this paper, we study the joint optimization of SER and LTR for real-time MPSoCs. We propose a novel static task scheduling algorithm to simultaneously maximize SER and LTR for real-time homogeneous MPSoC systems under the constraints of deadline, energy budget, and task precedence. Specifically, we develop a new solution representation scheme and two evolutionary operators that are closely integrated with two popular multiobjective evolutionary optimization frameworks, namely NSGAII and SPEA2. Extensive experimental results on standard benchmarks and synthetic applications show the efficacy of our scheme. More specifically, our scheme can achieve significantly better solutions (i.e., LTR-SER tradeoff fronts) with remarkably higher hypervolume and can be dozens or even hundreds of times faster than the state-of-the-art algorithms. The results also demonstrate that our scheme can be applied to heterogeneous MPSoC systems and is effective in improving reliability for heterogeneous MPSoC systems.