Enhancing transient fault tolerance in embedded systems through an OS task level redundancy approach

Asghari, Seyyed Amir; Marvasti, Mohammadreza Binesh; Rahmani, Amir M.

doi:10.1016/j.future.2018.04.049

Cited by 19 publications

(8 citation statements)

References 20 publications

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“…It provides more physical copies of components, i.e., processors and memories, to improve their reliability [31]. Different forms of redundancy were defined in software systems like information redundancy (code redundancy), functional redundancy, and time redundancy [22,32,33]:…”

Section: Software Redundancymentioning

confidence: 99%

“…(i) Information redundancy indicates the excess of information expressed in Shannon bits used to represent the state of a program [34]. (ii) Functional redundancy consists on using the same program specification to generate different algorithm or program versions performing the same functionality [33]. (iii) Time redundancy represented by the time was used to repeat the execution of the failed process [24,31].…”

Section: Software Redundancymentioning

confidence: 99%

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An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

Amara

Fatnassi

Rabai

2021

Scientific Programming

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Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.

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Section: Software Redundancymentioning

confidence: 99%

Section: Software Redundancymentioning

confidence: 99%

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

Amara

Fatnassi

Rabai

2021

Scientific Programming

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“…Distributed systems have been widely used in data fusion and comparative computing, finance, traffic management, dispatch management and other fields [1][2]. Therefore, the research of error control technology of distributed system has become an important and beneficial subject in the research of modern control system.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Control of Distributed System Based on Neural Network

2020

DPS

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Neural networks are very promising in the design and control of distributed systems due to their powerful computing power, learning and adaptive capabilities, and distributed capabilities. The purpose of this work is to study fault-tolerant control of distributed systems based on neural networks. For nonlinear systems with actuator failures, fault-tolerant control based on a small portion of the network is required. By combining error tracking and forecasting methods to build performance metrics and reduced fault-tolerant systems, this translates into a control problem. The PSO algorithm is introduced to train the judgment neural network, which avoids the selection of the initial vector and improves the training success rate to a certain extent. To mitigate the effects of actuator errors, a neural network error observer is designed to estimate and compensate for errors. Finally, the effectiveness of the proposed fault-tolerant supervisory control method is proved by an example simulation.

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“…The idea of fault tolerance is to ensure that these component failures remain as faults and do not compromise the entire system [37]. Many techniques in literature [85][86][87][88][89] use redundant hardware to improve fault tolerance, however with the use of peripheral hardware, there is increased cost due to infrastructure and reconfiguration, therefore, the trend nowadays is to ensure that FT techniques are more software dependent than hardware [90]. The use of hardware redundancy is inevitable in most CPS, for example, within sensor networks, hardware-based FT techniques still play an important role because sensors are relatively cheap and can be replicated without incurring high infrastructure costs.…”

Section: Fault Tolerance (Ft)mentioning

confidence: 99%

“…The use of hardware redundancy is inevitable in most CPS, for example, within sensor networks, hardware-based FT techniques still play an important role because sensors are relatively cheap and can be replicated without incurring high infrastructure costs. Hence, novel FT techniques tend to find a balance between hardware and software in a bid to enhance CPS reliability [90].…”

Section: Fault Tolerance (Ft)mentioning

confidence: 99%

A Robust Discrete Event Method for the Design of Cyber-Physical Systems

Boi-Ukeme¹

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The technological advancement in Cyber-Physical Systems (CPS) has seen more sophisticated hardware, leading to systems that are complex, interconnected, and require automation. This trend has made modern CPS fragile and susceptible to faults. Traditional methods for fault detection and diagnosis are unable to adequately scale up to handle the faults that occur in CPS because of the tight interconnectivity between the physical and cyber parts of CPS. Also, real-time requirements present new challenges that are not sufficiently addressed by traditional fault-tolerant design approaches, therefore more intelligent methods are now needed to deal with these faults. To address these issues, we propose an approach to formally define faults, detect, and diagnose faults, and accommodate faults for fault tolerance.For fault detection and diagnosis, we propose a generic fault detection and diagnosis (FDD) scheme capable of diagnosing CPS faults in real-time. The scheme is developed to accommodate different modeling methods but for clarity of explanation, we adapt it to the DEVS formalism. To test the scheme, we implemented a library to store fault codes in a data structure and developed intelligent logic to ensure faults are correctly detected and isolated. We also propose a purely data-driven approach to detect faults in CPS where we have no control over the design of the control system. Our data-driven methods thrive on setting rigorous processes and workflows to ensure that representative data is collected for CPS to ensure that FDD techniques perform optimally.

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Enhancing transient fault tolerance in embedded systems through an OS task level redundancy approach

Cited by 19 publications

References 20 publications

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

Fault Tolerant Control of Distributed System Based on Neural Network

A Robust Discrete Event Method for the Design of Cyber-Physical Systems

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