The recovery language approach for software-implemented fault tolerance

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.

This paper presents a new approach to provide fault tolerance in MPLS multicast networks. MPLS has been used to provide faster forwarding; combining with multicasting it further supports applications with improved service. Fault tolerance is therefore important for such networks. Our approach for fault tolerance is to divide and encode the traffic into multiple disjoint trees using a modified (k, n) Threshold Sharing scheme. Our approach can reconstruct the original traffic from any k out of n trees available. Therefore, in the event of node/link failure(s) of any (n-k) trees, our approach provides fault tolerance without introducing any recovery delay and packet loss. Moreover, our objective is also to minimize bandwidth utilization for protection purposes.

Brain-inspired deep neural networks such as Convolutional Neural Network (CNN) have shown great potential in solving difficult cognitive problems such as object recognition and classification. However, such architectures have high computational energy demand and sensitivity to variation effects, making them inapplicable for energy-constrained embedded learning platforms. To address this issue, we propose a learning and mapping approach that utilizes approximate computing during early design phases for a layer-wise pruning and fault tolerant weight mapping scheme of reliable and energy-efficient CNNs. In the proposed approach, approximate CNN is prepared first by layer-wise pruning of approximable neurons, which have high error tolerance margins using a two-level approximate learning methodology. Then, the pruned network is retrained to improve its accuracy by fine-tuning the weight values. Finally, a fault-tolerant layer-wise neural weight mapping scheme is adopted to aggressively reduce memory operating voltage when loading the weights of error resilient layers for energy-efficiency. Thus, the combination of approximate learning and fault tolerance aware memory operating voltage downscaling techniques enable us to implement robust and energy-efficient approximate inference engine for CNN applications. Simulation results show that the proposed fault tolerant and approximate learning approach can improve the energy-efficiency of CNN inference engines by more than 50% with less than 5% reduction in classification accuracy. Additionally, more than 26% energy-saving is achieved by using the proposed layer-wise mapping-based cache memory operating voltage down-scaling.

Failures are normal rather than exceptional in cloud computing environments, high fault tolerance issue is one of the major obstacles for opening up a new era of high serviceability cloud computing as fault tolerance plays a key role in ensuring cloud serviceability. Fault tolerant service is an essential part of Service Level Objectives (SLOs) in clouds. To achieve high level of cloud serviceability and to meet high level of cloud SLOs, a foolproof fault tolerance strategy is needed. In this paper, the definitions of fault, error, and failure in a cloud are given, and the principles for high fault tolerance objectives are systematically analyzed by referring to the fault tolerance theories suitable for large-scale distributed computing environments. Based on the principles and semantics of cloud fault tolerance, a dynamic adaptive fault tolerance strategy DAFT is put forward. It includes: (i) analyzing the mathematical relationship between different failure rates and two different fault tolerance strategies, which are checkpointing fault tolerance strategy and data replication fault tolerance strategy; (ii) building a dynamic adaptive checkpointing fault tolerance model and a dynamic adaptive replication fault tolerance model by combining the two fault tolerance models together to maximize the serviceability and meet the SLOs; and (iii) evaluating the dynamic adaptive fault tolerance strategy under various conditions in large-scale cloud data centers and consider different system centric parameters, such as fault tolerance degree, fault tolerance overhead, response time, etc. Theoretical as well as experimental results conclusively demonstrate that the dynamic adaptive fault tolerance strategy DAFT has high potential as it provides efficient fault tolerance enhancements, significant cloud serviceability improvement, and great SLOs satisfaction. It efficiently and effectively achieves a trade-off for fault tolerance objectives in cloud computing environments.

Track switches are essential in order to enable railway vehicles to change routes however they are also the largest single cause of failure on the railway network and remain a single point of failure. This thesis focusses on modelling and fault tolerant control of a new type of electromechanical railway track switch. Unlike conventional track switch machines, this switch needs closed loop feedback control. Firstly, a detailed simulation model of the switch is developed and validated against experimental results. Two model-based control designs are then developed and tested: a classical cascaded P/PI controller and a modern state feedback LQR controller. The two controllers are compared and it is found that, whilst there are some performance differences, both meet the requirements for use in a redundantly actuated track switch. Following this, a fault tolerant control scheme for sensor faults is developed and validated using fault detection, identification and accommodation methods applied to a detailed non-linear simulation model and then validated on the experimental test rig. The results demonstrate that using a reliable fault tolerant control configuration could increase the availability and reliability of the switch in the presence of sensor faults. Finally, a fault tolerant control approach to actuator faults on the switch with triplex redundancy is developed and validated. In order to achieve this, a dynamic model of a railway track is developed in simulation as the basis for load application to the system. Three parallel actuators with their associated controllers are used to actuate the switch showing good performance in meeting the position control requirements. Different actuator faults are applied and their control performance results are shown. A summary of the control performance is also presented highlighting the differences in performance with different permutations of actuator faults. Overall, it is shown that redundancy in actuation with a suitable control scheme provides benefits in achieving post-fault availability of a redundantly actuated electromechanical railway track switch in the presence of actuator faults.

Fault tolerance is an important requirement for long-running parallel programs. This paper presents a different approach to fault-tolerance support in message-passing parallel programs based on their structural and behavioral characteristics, commonly known as patterns. A classification of these patterns and their applicable fault-tolerance strategies is aimed to facilitate an application developer to incorporate appropriate fault-tolerance strategies to an application. Fault-tolerance strategies for two of the patterns are discussed, and one specific strategy is elaborated and analyzed. The presented strategies have been incorporated into a fault-tolerance support framework called FT-PAS. One objective of the framework is to separate the fault tolerance related details from an application developer's main objectives (separation-of-concerns). The paper presents the additional key features of the framework, and concludes with a discussion on current and future research directions.

Objectives: This aim of this paper is to analyse different approaches for transient fault tolerance like PBS, RM-FT, FSP, BCE and MWFD. Methods: Transient faults are emerging as a critical issue in the reliability of real time systems. Besides multiprocessors being an apparent part of various booming technologies now-a-days, it becomes evident to make these systems susceptible, reliable and consistent to transient faults. Thus, to address the increasing susceptibility of multiprocessors systems to transient faults, different approaches are analysed to counter transient faults. Findings: To identify the optimal method for various constraints like slack time, reduction in time space and reconfiguration etc. Applications: This comparative analysis will help for us to identify the optimal approaches for tolerating the transient fault in critical real time systems.

Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.

This paper develops a modulation based fault-tolerant (FT) strategy for restoring the operation of three-phase modular multilevel converters (MMCs) with faulty switches. This FT strategy is based on a proposed modified space vector modulation (SVM) technique that generates balanced line-to-line (line) voltages even in the case of a fault occurrence. In the postfault operation, the proposed strategy is able to restore the fundamental amplitude of the line to the neutral (load) voltages to that of the normal operation condition with a slightly increased voltage stress over the switches in the faulty phase. In this paper, first a brief background about MMCs and SVM technique is provided. Then, the proposed FT strategy and the modified SVM technique are presented. Finally, several simulation and experimental results are provided to validate operation of the proposed strategy.

Purpose The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system. Design/methodology/approach In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft. Findings The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied. Practical implications For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.

Due to modern technology trends such as decreasing feature sizes and lower voltage levels, fault tolerance (FT) is becoming increasingly important in computing systems. Several schemes have been proposed to enable a user to configure the FT at the application level, thereby enabling the user to trade stronger FT for performance or vice versa. In this paper, we propose supporting instruction-level rather than application-level configurability of FT, since different parts of some applications (e.g., multimedia) can have different reliability requirements. Weak or no FT will be applied to less critical parts, resulting in time and/or resource gains. These gains can be used to apply stronger FT techniques to the more critical parts; hence increasing the overall reliability. The paper shows how some existing FT techniques can be adapted to support instruction-level FT configurability, how a programmer can specify the desired FT level of the instructions, and how the compiler can manage it automatically. A comparison between the existing FT scheme EDDI (which duplicates all instructions) and the proposed approach is performed both at the kernel and at full application levels. The simulation results show that both the performance and the energy consumption are significantly improved (up to 50% at the kernel and up to 16% at full application level), while the fault coverage depends on the application. For the full application (JPEG encoder), our approach is only applied to one kernel in order to avoid increasing the programming effort significantly.

Fault-tolerant frameworks provide highly available services by means of fault detection and fault recovery mechanisms. These frameworks need to meet different constraints related to the fault model strength, performance, and resource consumption. One of the factors that led to this work is the observation that current fault-tolerant frameworks are not always adapted to existing Internet services. In fact, most of the proposed frameworks are not transport-level- or session-level-aware, although the concerned services range from regular services like HTTP and FTP to more recent Internet services such as multimodal conferencing and voice over IP. In this work we give a comprehensive overview of fault tolerance concepts, approaches, and issues. We show how the redundancy of application servers can be invested to ensure efficient failover of Internet services when the legitimate processing server goes down.

High-energy physics experiments require an extraordinary amount of real-time computation, and the computers implementing the online data processing must be very reliable because of the large cost associated with operating the facilities and the potential for loss of irreplaceable data. Conventional redundancy-based fault tolerance and adaptive approaches are not appropriate because of the tremendous system cost (fault tolerance is limited to a maximum of 10% overhead). In this work, we developed a framework for building robust embedded systems, which utilizes an autonomic reflex-healing approach to achieve fault tolerance. Components of the framework implement user-defined failure adaptation strategies within the context of a large-scale embedded environment. The tools embrace a model-based approach combining design specification and code-generation for both simulation and system implementation. In this paper we present the concepts and entities of the reflex and healing framework

The Algorithm-Based Fault Tolerance (ABFT) approach transforms a system that does not tolerate a specific type of faults, called the fault-intolerant system, to a system that provides a specific level of fault tolerance, namely recovery. The ABFT philosophy leads directly to a model from which error correction can be developed. By employing an ABFT scheme with effective convolutional code, the design allows high throughput as well as high fault coverage. The ABFT techniques that detect errors rely on the comparison of parity values computed in two ways. The parallel processing of input parity values produce output parity values comparable with parity values regenerated from the original processed outputs and can apply convolutional codes for the redundancy. This method is a new approach to concurrent error correction in fault-tolerant computing systems. This chapter proposes a novel computing paradigm to provide fault tolerance for numerical algorithms. The authors also present, implement, and evaluate early detection in ABFT.

Currently, using commercial off-the-shelf (COTS) components and constructing parallel system becomes the main way to improve satellite system performance remarkably. System reliability relying on its structure rather than devices makes system fault-tolerant architecture and strategy increasingly important. As centralized fault-tolerant solution has the inherent drawback of single point of failure (SPOF), it can not guarantee system reliability well. Distributed fault- tolerant method, due to its complexity and large overhead, has not been maturely used. In this paper, we propose a new distributed fault-tolerant scheme with high reliability and flexibility, in which an equal symmetric distributed fault- tolerant (ESDFt) architecture is constructed. Based on the structure, functional module framework of system nodes and autonomous fault-tolerant strategy are designed. The ESDFt scheme creates favorable conditions for flexible implementation of system fault tolerance. It utilizes the master election mechanism, which eliminates the risks of SPOF and chooses master node intelligently based on user's configuration, making fault-tolerant complexity and processing overhead flexibly adjustable. The verification and evaluation of ESDFt scheme has been performed on a prototype system. The experiment results prove that ESDFt scheme not only ensures system reliability well, but also enhances system flexibility and practicality at the same time.