Fault-tolerant Model Research Articles

Simplified fault tolerant finite control set model predictive control of a five-phase inverter supplying BLDC motor in electric vehicle drive

Multiphase brushless direct current (BLDC) motors can meet the increasing demand for higher reliability in motor drives applicable in electric vehicles by integrating fault diagnosis to a fault-tolerant (FT) control method. To achieve this goal, a modified FT finite control set model predictive control (FCS–MPC) is proposed in this paper. The dead beat control is used to predict the reference voltage applied by the inverter. A sensitivity analysis is done to show the effect of model uncertainty on the controller performance. In addition, a simple, fast and general open switch and short circuit fault detection (FD) method in voltage source inverter (VSI) is presented. The FD method is capable of detecting open switch, open phase, and short circuit faults without any auxiliary variable. Moreover, it is robust to both speed and load transients in a motor drive. To validate the presented theory, experimental results are conducted on a five-phase BLDC motor drive with outer rotor in wheel structure.

Fault-Tolerant Scheduling for Real-Time Tasks on Multiple Earth-Observation Satellites

Fault-tolerance plays an important role in improving the reliability of multiple earth-observing satellites, especially in emergent scenarios such as obtaining photographs on battlefields or earthquake areas. Fault tolerance can be implemented through scheduling approaches. Unfortunately, little attention has been paid to fault-tolerant scheduling on satellites. To address this issue, we propose a novel dynamic fault-tolerant scheduling model for real-time tasks running on multiple observation satellites. In this model, the primary-backup policy is employed to tolerate one satellite’s permanent failure at one time instant. In the light of the fault-tolerant model, we develop a novel f ault- t olerant s atellite s cheduling algorithm named FTSS. To improve the resource utilization, we apply the overlapping technology that includes primary-backup copy overlapping (i.e., PB overlapping) and backup-backup copy overlapping (i.e., BB overlapping). According to the satellites characterized with time windows for observations, we extensively analyze the overlapping mechanism on satellites. We integrate the overlapping mechanism with FTSS, which employs the task merging strategies including primary-backup copy merging (i.e., PB merging), backup-backup copy merging (i.e., BB merging) and primary-primary copy merging (i.e., PP merging). These merging strategies are used to decrease the number of tasks required to be executed, thereby enhancing system schedulability. To demonstrate the superiority of our FTSS, we conduct extensive experiments using the real-world satellite parameters supplied from the satellite tool kit or STK; we compare FTSS with the three baseline algorithms, namely, NMFTSS, NOFTSS, and NMNOFTSS. The experimental results indicate that FTSS efficiently improves the scheduling quality of others and is suitable for fault-tolerant satellite scheduling.

A decentralized fault tolerance model based on level of performance for grid environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, computer resources and network failur...

Fault tolerance at system level based on RADIC architecture

The increasing failure rate in High Performance Computing encourages the investigation of fault tolerance mechanisms to guarantee the execution of an application in spite of node faults. This paper presents an automatic and scalable fault tolerant model designed to be transparent for applications and for message passing libraries. The model consists of detecting failures in the communication socket caused by a faulty node. In those cases, the affected processes are recovered in a healthy node and the connections are reestablished without losing data. The Redundant Array of Distributed Independent Controllers architecture proposes a decentralized model for all the tasks required in a fault tolerance system: protection, detection, recovery and masking. Decentralized algorithms allow the application to scale, which is a key property for current HPC system. Three different rollback recovery protocols are defined and discussed with the aim of offering alternatives to reduce overhead when multicore systems are used. A prototype has been implemented to carry out an exhaustive experimental evaluation through Master/Worker and Single Program Multiple Data execution models. Multiple workloads and an increasing number of processes have been taken into account to compare the above mentioned protocols. The executions take place in two multicore Linux clusters with different socket communications libraries.

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.

Review on Fault Tolerance Techniques in Cloud Computing

With the immense growth of internet and its users, Cloud computing, with its incredible possibilities in ease, Quality of service and on-interest administrations, has turned into a guaranteeing figuring stage for both business and nonbusiness computation customers. It is an adoptable technology as it provides integration of software and resources which are dynamically scalable. The dynamic environment of cloud results in various unexpected faults and failures. The ability of a system to react gracefully to an unexpected equipment or programming malfunction is known as fault tolerance. In order to achieve robustness and dependability in cloud computing, failure should be assessed and handled effectively. Various fault detection methods and architectural models have been proposed to increase fault tolerance ability of cloud. The objective of this paper is to propose an algorithm using Artificial Neural Network for fault detection which will overcome the gaps of previously implemented algorithms and provide a fault tolerant model.

Research of Sensor Fault-Tolerant Control Based on CSA-SVR Algorithm for EMB Actuator System

The electro mechanical brake (EMB) system is a efficient pure electric vehicle braking system in where technologies in the fields of electronics, mechanics, and vehicle communication network were considered at the same time. Because that offered information to the electronic control of the EMB system is taken from several detectors, during developments of the pure electric vehicle, unsolved difficulties in EMB system are the accurate fault detection as well as the timely process of a fault tolerant control. In this paper, the fault detection and the fault tolerant control on the mathematical models of a current, a speed, and a pressure detector loaded on the EMB actuator system will be studied. Based on the three-loop control architecture model of the EMB actuator, these models of detectors are constructed by the method of the dynamics analysis of the actuating agency respectively. On the purpose of improving the accuracy of fault detection, the clonal selection - support vector regression algorithm (CSA-SVR) is proposed, which is a combination of CSA and SVR calculation methods. Through CSA-SVR algorithm, optimized parameters of support vector machine (SVM) and improved accuracy of fault detection are obtained. The adaptive fault tolerant control architectural model mentioned which is designed using CSA-SVR algorithm in this paper shows effective function in fault detection, isolation from fault, and fault estimation.

Schedulability of Fault Tolerant Real Time System Based on Local Optimum Checkpoint Under Priority Mixed Strategy

Against the defect of the real time system fault tolerant model that can only tolerate one fault, the Local optimum checkpoint (LOC) algorithm was proposed. Then according to the schedulability analysis, the worst case response time formula under the fault tolerant priority mixed strategy based on the local optimum checkpoint was deduced. Finally the Fault tolerant priority configuration search algorithm under Mixed strategy based on LOC (FTPCS-MS-LOC) was proposed. The FTPCS-MS-LOC can toleratemultiple transient faults. The algorithm can effectively reduce the search space compared to the enumeration method. The simulation shows that the FTPCS-MSLOC can significantly improve the system fault resilience than the fault tolerant priority inheritance, promotion and demotion strategy, and also the mixed strategy based on the checkpoint interval in the condition of single fault occurred.

Multihybrid job scheduling for fault-tolerant distributed computing in policy-constrained resource networks

Unpredictable fluctuations in resource availability often lead to rescheduling decisions that sacrifice a success rate of job completion in batch job scheduling. To overcome this limitation, we consider the problem of assigning a set of sequential batch jobs with demands to a set of resources with constraints such as heterogeneous rescheduling policies and capabilities. The ultimate goal is to find an optimal allocation such that performance benefits in terms of makespan and utilization are maximized according to the principle of Pareto optimality, while maintaining the job failure rate close to an acceptably low bound. To this end, we formulate a multihybrid policy decision problem (MPDP) on the primary-backup fault tolerance model and theoretically show its NP-completeness. The main contribution is to prove that our multihybrid job scheduling (MJS) scheme confidently guarantees the fault-tolerant performance by adaptively combining jobs and resources with different rescheduling policies in MPDP. Furthermore, we demonstrate that the proposed MJS scheme outperforms the five rescheduling heuristics in solution quality, searching adaptability and time efficiency by conducting a set of extensive simulations under various scheduling conditions.

Reliability and Mission Cost of 1-Out-of-<formula formulatype="inline"><tex Notation="TeX">$N$</tex></formula>:G Systems With State-Dependent Standby Mode Transfers

The paper proposes a new fault tolerant system design model, in particular a 1-out-of- $N$ :G hybrid redundant system with standby elements subject to state-dependent standby mode transfers. Specifically, in such systems, one standby element always resides in the hot standby mode, and thus is ready to replace the failed online element at any time to make the system dependable. If the online operating element or the hot standby element fails, one of the warm standby elements is immediately transferred to the hot standby mode. A numerical algorithm is first suggested for evaluating the reliability and the expected mission cost of the considered system. The algorithm is based on a discrete approximation of element time-to-failure distributions, and can work with any type of distribution. Furthermore, based on the suggested algorithm, the problem of optimal sequencing of standby elements initiation is formulated and solved. The objective of this optimization problem is to minimize the expected mission cost associated with elements' standby and operation expenses, as well as the mode transfer expenses, while meeting a certain system reliability constraint. Illustrative examples are provided.

Modeling dynamic recovery strategy for composite web services execution

During the execution of Composite Web Services (CWS), a component Web Service (WS) can fail and can be repaired with strategies such WS retry, substitution, compensation, roll-back, replication, or checkpointing. Each strategy behaves differently on different scenarios, impacting the CWS QoS. We propose a non intrusive dynamic fault tolerant model that analyses several levels of information: environment state, execution state, and QoS criteria, to dynamically decide the best recovery strategy when a failure occurs. We present an experimental study to evaluate the model and determine the impact on QoS parameters of different recovery strategies; and evaluate the intrusiveness of our strategy during the normal execution of CWSs.

Reliable fault tolerant model for grid computing environments

Grid reliability analysis and modeling are not easy tasks because of the complexity and large scale of the system. For some grid services which have large subtasks requiring time-consuming computation, the reliability of grid service could be rather low. To resolve this problem, RNFR and LNFR are proposed in literature. This paper describes the property of the grid computing systems and analyzes the grid program and service reliability. In this paper, we propose a fault tolerance mechanism, namely LRNFR, based both on RNFR and LNFR in a complementary manner with a periodic migration. We select the most reliable node among the set of Neighboring Collaborators. The migration process is divided by the MDTTR of the failed node until its repair or the migration of all its programs. Experiments demonstrate the performance of the proposed approach.

Modeling and Analysis of Fault Detection and Fault Tolerance in Wireless Sensor Networks

Technological advancements in communications and embedded systems have led to the proliferation of Wireless Sensor Networks (WSNs) in a wide variety of application domains. These application domains include but are not limited to mission-critical (e.g., security, defense, space, satellite) or safety-related (e.g., health care, active volcano monitoring) systems. One commonality across all WSN application domains is the need to meet application requirements (e.g., lifetime, reliability). Many application domains require that sensor nodes be deployed in harsh environments, such as on the ocean floor or in an active volcano, making these nodes more prone to failures. Sensor node failures can be catastrophic for critical or safety-related systems. This article models and analyzes fault detection and fault tolerance in WSNs. To determine the effectiveness and accuracy of fault detection algorithms, we simulate these algorithms using ns-2. We investigate the synergy between fault detection and fault tolerance and use the fault detection algorithms’ accuracies in our modeling of Fault-Tolerant (FT) WSNs. We develop Markov models for characterizing WSN reliability and Mean Time to Failure (MTTF) to facilitate WSN application-specific design. Results obtained from our FT modeling reveal that an FT WSN composed of duplex sensor nodes can result in as high as a 100% MTTF increase and approximately a 350% improvement in reliability over a Non-Fault-Tolerant (NFT) WSN. The article also highlights future research directions for the design and deployment of reliable and trustworthy WSNs.

Hadoop, MapReduce and HDFS: A Developers Perspective

Abstract The applications running on Hadoop clusters are increasing day by day. This is due to the fact that organizations have found a simple and efficient model that works well in distributed environment. The model is built to work efficiently on thousands of machines and massive data sets using commodity hardware. HDFS and MapReduce is a scalable and fault-tolerant model that hides all the complexities for Big Data analytics. Since Hadoop is becoming increasingly popular, understanding technical details becomes essential. This fact inspired us to explore Hadoop and its components in-depth. The process of analysing, examining and processing huge amount of unstructured data to extract required information has been a challenge. In this paper we discuss Hadoop and its components in detail which comprise of MapReduce and Hadoop Distributed File System (HDFS). MapReduce engine uses JobTracker and TaskTracker that handle monitoring and execution of job. HDFS a distributed file-system which comprise of NameNode, DataNode and Secondary NameNode for efficient handling of distributed storage purpose. The details provided can be used for developing large scale distributed applications that can exploit computational power of multiple nodes for data and compute intensive applications.

A Novel Approach for Replica Synchronization in Hadoop Distributed File Systems

Abstract The Map Reduce framework provides a scalable model for large scale data intensive computing and fault tolerance. In this paper, we propose an algorithm to improve the I/O performance of the Hadoop distributed file system. The results prove that the proposed algorithm show better I/O performance with comparatively less synchronization

Rotational Wireless Video Sensor Networks with Obstacle Avoidance Capability for Improving Disaster Area Coverage

Wireless Video Sensor Networks (WVSNs) have become a leading solution in many important applications, such as disaster recovery. By using WVSNs in disaster scenarios, the main goal is achieving a successful immediate response including search, location, and rescue operations. The achievement of such an objective in the presence of obstacles and the risk of sensor damage being caused by disasters is a challenging task. In this paper, we propose a fault tolerance model of WVSN for efficient post-disaster management in order to assist rescue and preparedness operations. To get an overview of the monitored area, we used video sensors with a rotation capability that enables them to switch to the best direction for getting better multimedia coverage of the disaster area, while minimizing the effect of occlusions. By constructing different cover sets based on the field of view redundancy, we can provide a robust fault tolerance to the network. We demonstrate by simulating the benefits of our proposal in terms of reliability and high coverage.

FIM-SIM: Fault Injection Module for CloudSim Based on Statistical Distributions

The evolution of ICT systems in the way data is accessed and used is very fast nowadays. Cloud computing is an innovative way of using and providing computing resources to businesses and individuals and it has gained a faster popularity in the last years. In this context, the user’s expectations are increasing and cloud providers are facing huge challenges. One of these challenges is fault tolerance and both researchers and companies have focused on finding and developing strong fault tolerance models. To validate these models, cloud simulation tools are used as an easy, flexible and fast solution. This paper proposes a Fault Injector Module for CloudSim tool (FIM-SIM) for helping the cloud developers to test and validate their infrastructure. FIM-SIM follows the event-driven model and inserts faults in CloudSim based on statistical distributions. The authors have tested and validated it by conducting several experiments designed to highlight the statistical distribution influence on the failures generated and to observe the CloudSim behavior in its current state and implementation.

Efficient Resource Management Mechanism with Fault Tolerant Model for Computational Grids

Grid computing provides a framework and deployment environment that enables resource sharing, accessing, aggregation and management. It allows resource and coordinated use of various resources in dynamic, distributed virtual organization. The grid scheduling is responsible for resource discovery, resource selection and job assignment over a decentralized heterogeneous system. In the existing system, primary-backup approach is used for fault tolerance in a single environment. In this approach, each task has a primary copy and backup copy on two different processors. For dependent tasks, precedence constraint among tasks must be considered when scheduling backup copies and overloading backups. Then, two algorithms have been developed to schedule backups of dependent and independent tasks. The proposed work is to manage the resource failures in grid job scheduling. In this method, data source and resource are integrated from different geographical environment. Faulttolerant scheduling with primary backup approach is used to handle job failures in grid environment. Impact of communication protocols is considered. Communication protocols such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP) which are used to distribute the message of each task to grid resources.

A Diskless Checkpointing Algorithm for Cluster Architectures Applied to Geospatial Raster Data Processing

In recent years, due to the increasing calculation demands for the massive spatial data analysis, the parallel computing based on high-performance computers has become an inevitable trend of geospatial raster data processing, such as digital terrain analysis (DTA for short), remote sensing interpretation and digital soil mapping. A key problem is how to design a fault-tolerant software to enhance the stability and robustness of scientific application. This paper presents an approach of failure recovery for distributed memory parallel computing. Furthermore, we adopt the master/slave programming model and present a framework of redundant master mode, which the failure occurring on the master node could not lead to a breakdown of the whole system. This approach schedules the failing task by dividing all the failing data into several partitions according to the calculating scale of failure. By means of the Fault-Tolerant Granularity Model, the scheduling algorithm can assign the failing task dynamically. Finally, taking example of digital terrain analysis, two experiments are discussed that based on the data size and the number of failures. Simulation results indicate that the proposed scheduling algorithm based on Fault-Tolerant Granularity Model achieves lower fault tolerance overhead than the rollback recovery scheme when several processors fail simultaneously.

Fault-tolerant quantum computation with constant overhead

What is the minimum number of extra qubits needed to perform a large fault-tolerant quantum circuit? Working in a common model of fault-tolerance, I show that in the asymptotic limit of large circu...