This article investigates the reliability of a special cyber-physical system (CPS) with an unreliable service and a complex boundary behavior. A flat semi-dormant multicontroller (FSDMC) model is constructed on a special CPS named arbitrated networked control system (ANCS) with dual channels. In this study, the dual-channel ANCS is considered as a Markov repairable system, which integrates the binary state of physical device failure and the multistate of information flow. The FSDMC is modeled as anN/(d,c)-M/M/c/K/SMWVqueuing system with an unreliable service. A dual rate matrix method is proposed to solve the stationary distribution of the queuing system and obtain the closed-form matrix solution of the distribution. Based on the queuing model, an optimization model is established to minimize the proposed cost performance rate function. A particle swarm optimization algorithm is used to solve the optimization model and obtain the optimal values of the system parameters under stable conditions. The closed-form expression of the instantaneous availability of the FSDMC on the physical failure rate and repair rate of the controller is yielded iteratively. The linear relationship between system instantaneous failure rate and task instantaneous failure rate is expressed. The sensitivity of task failure rate to system parameters is analyzed. Several reliability metrics are used to evaluate system reliability and task reliability. Experiments are conducted in real application scenarios to compare the task reliability using redundancy technology and real parallel applications. Experiments show that the proposed reliability model can more effectively guarantee system reliability goals compared with its counterparts.

Generalized sparse nonnegative matrix factorization (SNMF) has been proven useful in extracting information and representing sparse data with various types of probabilistic distributions from industrial applications, e.g., recommender systems and social networks.However, current solution approaches for generalized SNMF are based on the manipulation of whole sparse matrices and factor matrices, which will result in large-scale intermediate data.Thus, these approaches cannot describe the high-dimensional and sparse matrices in mainstream industrial and big data platforms, e.g., graphics processing unit (GPU) and multi-GPU, in an online and scalable manner. To overcome these issues, an online, scalable, and single-thread-based SNMF for CUDA parallelization on GPU (CUSNMF) and multi-GPU (MCUSNMF) is proposed in this article. First, theoretical derivation is conducted, which demonstrates that the CUSNMF depends only on the products and sums of the involved feature tuples. Next, the compactness, which can follow the sparsity pattern of sparse matrices, endows the CUSNMF with online learning capability and the fine granularity gives it high parallelization potential on GPU and multi-GPU. Finally, the performance results on several real industrial datasets demonstrate the linear scalability of the time overhead and the space requirement and the validity of the extension to online learning. Moreover, CUSNMF obtains speedup of 7X on a P100 GPU compared to that of the state-of-the-art parallel approaches on a shared memory platform.

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches—that are independent of, or weakly dependent on DVFS—for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

Optimisation of timing performance and power consumption on heterogeneous multi-core architectures is gaining increasing attention. Systems and devices may have varying demands on timing and power, which motivates more flexible optimisation. Along this line, we consider a heterogeneous computing architecture with multiple cores, where each core runs a mixed stream of general and dedicated tasks with a certain scheduling strategy. Employing the queuing model, we first propose a load balancing algorithm, which minimises the average response time of the general tasks whilst guaranteeing the timing requirements of the dedicated tasks. Built upon the above, we propose a bi-directional optimisation algorithm that is able to improve the timing performance under the constraint of power consumption, and reduces the power consumption for the given timing requirement. Extensive numerical experiments illustrate the significance of the proposed algorithms. Implementation on a real platform validates the consistency between the theoretical analysis and the practical results.

The development of the complexity and connectivity of modern automobiles has caused a massive rise in the security risks of in-vehicle networks (IVNs). Nevertheless, existing IVN designs (e.g., controller area network) lack cybersecurity consideration. Intrusion detection, an effective method for defending against cyberattacks on IVNs while providing functional safety and real-time communication guarantees, aims to address this issue. Therefore, the necessity of its research has risen. In this paper, an IVN environment is introduced, and the constraints and characteristics of an intrusion detection system (IDS) design for IVNs are presented. A survey of the proposed IDS designs for the IVNs is conducted, and the corresponding drawbacks are highlighted. Various optimization objectives are considered and comprehensively compared. Lastly, the trend, open issues, and emerging research directions are described.

This paper investigates resource provisioning in cyber-physical systems (CPSs) by developing a new definition of anelasticity. A flat semi-dormant multicontroller (FSDMC) model is established on a special type of CPS platform named arbitrated networked control system with dual communication channels. A novel, quantitative, and formal definition of anelasticity for the FSDMC is proposed. A new finite capacity M/M/c queuing system with N-policy and asynchronous multiple working vacations of partial servers is established, and the FSDMC is modeled as a quasi-birth-and-death process to obtain the stationary probability distribution of the system. Based on the queueing model, we quantify various performance indices of the system to build a nonlinear cost-performance ratio (CPR) function. An optimization model is presented to minimize the CPR. A particle swarm optimization (PSO) algorithm is used to find the optimum solution of the optimization model and obtain the optimal configuration values of the system parameters under stability condition. By changing the system parameters, the sensitivity of the system performance indices and the CPR are analyzed, respectively. The unexpected workload varies randomly over time. Thus, an M/M/1/K queue is constructed in a Markovian environment by employing a three-state, irreducible Markov process. In this queue, the conditional average queue length and the probabilities of the three-state process are calculated. Then, the anelasticity value of the system is precisely determined. When the average arrival rate exceeds the average service rate in the queueing system, an optimal CPR unchanged adaptive algorithm based on PSO is designed to dynamically adjust the controller service rate. Extensive numerical results show the usefulness and effectiveness of the proposed techniques and exhibit that the system can maintain elastic invariance in adaptive adjustment parameters.

Automotive functional safety standard ISO 26262 aims to avoid unreasonable risks due to systematic failures and random hardware failures caused by malfunctioning behavior. Automotive functions involve distributed end-to-end computation in automotive cyber-physical systems (ACPSs). The automotive industry is highly cost-sensitive to the mass market. This study presents a resource-cost-aware fault-tolerant design methodology for end-to-end functional safety computation on ACPSs. The proposed design methodology involves early functional safety requirement verification and late resource cost design optimization. We first propose the functional safety requirement verification (FSRV) method to verify the functional safety requirement consisting of reliability and response time requirements of the distributed automotive function during the early design phase. We then propose the resource-cost-aware fault-tolerant optimization (RCFO) method to reduce the resource cost while satisfying the functional safety requirement of the function during the late design phase. Finally, we perform experiments with real-life automotive and synthetic automotive functions. Findings reveal that the proposed RCFO and VFSR methods demonstrate satisfactory resource cost reduction compared with other methods while satisfying the functional safety requirement.

Given a high-order, large-scale and sparse data from big data and industrial applications, how can we acquire useful patterns in a real-time and low memory overhead manner? Sparse Non-negative tensor factorization (SNTF) possesses high-order representation, non-negativity and dimension reduction inherence. Thus, SNTF has become a useful tool to represent and analyze the sparse data, which has been incorporated with extra contextual information, i.e., time and location, etc, more than the matrix, which can only model the 2 ways data. However, current SNTF techniques suffer from a) non-linear time and space overhead, b) intermediate data explosion, and c) inability on GPU and multi-GPU. To address these issues, a single-thread-based SNTF is proposed, which involves the feature elements rather than on the whole factor matrices, and can avoid the forming of large-scale intermediate matrices. Then, a CUDA parallelizing single-thread-based SNTF (CUSNTF) model is proposed for industrial applications on GPU and multi-GPU (MCUSNTF). Thus, CUSNTF has linear computing and space complexity, and linear communication cost on multi-GPU. We implement CUSNTF and MCUSNTF on 8 P100 GPUs, and compare it with state-of-the-art parallel and distributed methods. Experimental results from several industrial datasets demonstrate that the linear scalability and efficiency of CUSNTF.

Real-time accurate recommendation of large-scale recommender systems is a challenging task. Matrix factorization (MF), as one of the most accurate and scalable techniques to predict missing ratings, has become popular in the collaborative filtering (CF) community. Currently, stochastic gradient descent (SGD) is one of the most famous approaches for MF. However, it is non-trivial to parallelize SGD for large-scale CF MF problems due to the dependence on the user and item pair, which can cause parallelization over-writing. To remove the dependence on the user and item pair, we propose a multi-stream SGD (MSGD) approach, for which the update process is theoretically convergent. On that basis, we propose a Compute Unified Device Architecture (CUDA) parallelization MSGD (CUMSGD) approach. CUMSGD can obtain high parallelism and scalability on Graphic Processing Units (GPUs). On Tesla K20m and K40c GPUs, the experimental results show that CUMSGD outperforms prior works that accelerated MF on shared memory systems, e.g., DSGD, FPSGD, Hogwild!, and CCD++. For large-scale CF problems, we propose multiple GPUs (multi-GPU) CUMSGD (MCUMSGD). The experimental results show that MCUMSGD can improve MSGD performance further. With a K20m GPU card, CUMSGD can be 5-10 times as fast compared with the state-of-the-art approaches on shared memory platform.

In this paper, the joint optimization problem with energy efficiency and effective resource utilization is investigated for heterogeneous and distributed multi-core embedded systems. The system model is considered to be fully a heterogeneous model, that is, all nodes have different maximum speeds and power consumption levels from the perspective of hardware while they can employ different scheduling strategies from the perspective of applications. Since the concerned problem by nature is a multi-constrained and multi-variable optimization problem in which a closed-form solution cannot be obtained, our aim is to propose a power allocation and load balancing strategy based on Lagrange theory. Furthermore, when the problem cannot be fully solved by Lagrange approach, a data fitting method is employed to obtain core speed first, and then load balancing schedule is solved by Lagrange method. Several numerical examples are given to show the effectiveness of the proposed method and to demonstrate the impact of each factor to the present optimization system. Finally, simulation and practical evaluations show that the theoretical results are consistent with the practical results. To the best of our knowledge, this is the first work that combines load balancing, energy efficiency, hardware heterogeneity and application heterogeneity in heterogeneous and distributed embedded systems.

Recently, the modeling and design of distributed controllers in cyber-physical systems (CPSs), which suffer from messages lost, delay variation, and jitter, has gained lots of research attentions. A special CPS, arbitrated networked control system (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, we propose a novel ANCS with dual communication channels. The proposed ANCS uses a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy that is based on the event trigger protocol. A flat semi-dormant multicontrollers (FSDMC) model is developed for the proposed ANCS. We then model the FSDMC as an N/(d,c)-M/M/c/K/SMWV queue, and obtain various performance indices. Based on the model, a multiobjective optimization problem is then formulated to minimize the nonlinear energy consumption function and the nominal delay function presented in this study. To resolve the multiobjective optimization problem, a scheduling algorithm based on the multiobjective particle swarm optimization algorithm is proposed to generate the Pareto front and the corresponding nondominated vector sets. An optimal stopping algorithm is also designed to obtain the optimal value of the number of semi-dormant controllers. The optimal values of various parameters of the control system are obtained by using the above nondominated vector sets, and are applied to the proposed ANCS. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and the effects of the control system parameters on the optimal policy.

This paper focuses on the fuzzy-H∞-filter-design problem for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Two cases of the time-varying delays are considered: 1) The delays are differentiable and have both the lower and upper bounds of the delay derivatives, and 2) the delays are bounded but not necessary to be differentiable. Since we employ a new fuzzy Lyapunov-Krasovskii functional (LKF) and estimate a tighter upper bound of its derivative, the proposed delay-derivative-dependent bound-real-lemma (BRL) condition has advantages over some previous results in that it enlarges the application scope but has less conservatism, which is established theoretically. The BRL condition that depends on both the upper and lower bounds of the delay derivatives is derived. Then, based on the aforesaid BRL, a new fuzzy H∞filter scheme is proposed, and a sufficient condition for the existence of such a filter is established in terms of linear-matrix inequalities (LMIs). Finally, some numerical examples and an application to the truck-trailer system are utilized to demonstrate the effectiveness and reduced conservatism of our results.