Redundancy Techniques Research Articles

A novel redundancy technique to enhance the security of cloud computing

A novel redundancy technique to enhance the security of cloud computing

Modeling the Failure Rate of a Standby Multi-Component System and Improving Reliability

Evaluating the reliability of a system requires knowledge of the failure modes to which it is subject. Complex topology systems generally require a high level of availability, which is a function of the arrangement of elements (components) in the system. To avoid serious failures for such complex systems, recourse can be had to the redundancy techniques available in the literature. These techniques help improve system reliability, without affecting the reliability of system components. This paper is interested in the proposal of a model for evaluating the failure rate of a standby multi-component system and in improving the reliability of mechanical systems, arranged in a topology (series, parallel, or mixed).

Evaluation of Dynamic Triple Modular Redundancy in an Interleaved-Multi-Threading RISC-V Core

Functional safety is a key requirement in several application domains in which microprocessors are an essential part. A number of redundancy techniques have been developed with the common purpose of protecting circuits against single event upset (SEU) faults. In microprocessors, functional redundancy may be achieved through multi-core or simultaneous-multi-threading architectures, with techniques that are broadly classifiable as Double Modular Redundancy (DMR) and Triple Modular Redundancy (TMR), involving the duplication or triplication of architecture units, respectively. RISC-V plays an interesting role in this context for its inherent extendability and the availability of open-source microarchitecture designs. In this work, we present a novel way to exploit the advantages of both DMR and TMR techniques in an Interleaved-Multi-Threading (IMT) microprocessor architecture, leveraging its replicated threads for redundancy, and obtaining a system that can dynamically switch from DMR to TMR in the case of faults. We demonstrated the approach for a specific family of RISC-V cores, modifying the microarchitecture and proving its effectiveness with an extensive RTL fault-injection simulation campaign.

Classification of Valvular Regurgitation Using Echocardiography

Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carried out after keyframe extraction using the Level Set (LS) with Fuzzy C-means (FCM) approach. Performances are evaluated in terms of accuracy, precision, recall, and F1-score and compared for both reference frame and redundant frame extraction techniques. K-fold cross-validation is used to examine the performance of the model. The performance result shows that our proposed approach outperforms other state-of-art machine learning approaches in terms of accuracy, precision, recall, and F1-score.

Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients.

Although glioblastoma (GBM) is the most common primary brain malignancy, few tools exist to pre-operatively risk-stratify patients by overall survival (OS) or common genetic alterations. We developed an MRI-based radiomics model to identify patients with EGFR amplification, MGMT methylation, GBM subtype, and OS greater than 12months. We retrospectively identified 235 patients with pathologically confirmed GBMs from the Cancer Genome Atlas (88; TCGA) and MD Anderson Cancer Center (147; MDACC). After two neuroradiologists segmented MRI tumor volumes, we extracted first-order and second-order radiomic features (gray-level co-occurrence matrices). We used the Maximum Relevance Minimum Redundancy technique to identify the 100 most relevant features and validated models using leave-one-out-cross-validation and validation on external datasets (i.e., TCGA). Our results were reported as the area under the curve (AUC). The MDACC patient cohort had significantly higher OS (22months) than the TCGA dataset (14months). On both LOOCV and external validation, our radiomics models were able to identify EGFR amplification (all AUCs > 0.83), MGMT methylation (all AUCs > 0.85), GBM subtype (all AUCs > 0.92), and OS (AUC > 0.91 on LOOCV and 0.71 for TCGA validation). Our robust radiomics pipeline has the potential to pre-operatively discriminate common genetic alterations and identify patients with favorable survival.

Reliability provisioning for Fog Nodes in Smart Farming IoT-Fog-Cloud continuum

Reliability is essential in Smart Farming supported by the IoT-Fog-Cloud continuum. Smart Farms’ unprotection may cause significant economic losses and low yields of production. This paper introduces an optimization model for providing reliability and, consequently, service continuity to the IoT-Fog-Cloud continuum-based smart farms. The proposed model allows Smart Farming stakeholders to find the optimal number of Fog Nodes needed to deploy farming services considering the heterogeneity in the fog capabilities, resource demands, redundancy techniques, and reliability requirements. The model was solved using linear programming and evaluated with different demands and protection schemes. Results show that protection schemes guarantee high reliability and reveal that a shared redundancy scheme reduces deployment cost and yet provides reliability. Results also indicate that deployment costs and resources depend on the type of fog-based smart farm services to serve. Moreover, they show that deploying more low-resource hardware can be less expensive for low-reliability demands than deploying with a few high-resource hardware.

Lung Cancer Diagnosis System Based on Volatile Organic Compounds (VOCs) Profile Measured in Exhaled Breath

Lung cancer is one of the world’s lethal diseases and detecting it at an early stage is crucial and difficult. This paper proposes a computer-aided lung cancer diagnosis system using volatile organic compounds (VOCs) data. A silicon microreactor, which consists of thousands of micropillars coated with an ammonium aminooxy salt, is used to capture the volatile organic compounds (VOCs) in the patients’ exhaled breath by means of oximation reactions. The proposed system ranks the features using the Pearson correlation coefficient and maximum relevance–minimum redundancy (mRMR) techniques. The selected features are fed to nine different classifiers to determine if the lung nodule is malignant or benign. The system is validated using a locally acquired dataset that has 504 patients’ data. The dataset is balanced and has 27 features of volatile organic compounds (VOCs). Multiple experiments were completed, and the best accuracy result is 87%, which was achieved using random forest (RF) either by using all 27 features without selection or by using the first 17 features obtained using maximum relevance–minimum redundancy (mRMR) while using an 80–20 train-test split. The correlation coefficient, maximum relevance–minimum redundancy (mRMR), and random forest (RF) importance agreed that C4H8O (2-Butanone) ranks as the best feature. Using only C4H8O (2-Butanone) for training, the accuracy results using the support vector machine, logistic regression, bagging and neural network classifiers are 86%, which approaches the best result. This shows the potential for these volatile organic compounds (VOCs) to serve as a significant screening tests for the diagnosis of lung cancer.

A comprehensive review of fault diagnosis and fault-tolerant control techniques for modular multi-level converters.

Many faults occur in the modular multi-level converters (MMCs), including unbalancing capacitor voltage, lower and upper arm unbalancing, the line to line voltage unbalancing, sensors and actuators fault, system fault, and sub-modules fault in high as well as medium voltage applications. Several fault-tolerant approaches are presented to overcome these problems, such as active fault-tolerant control system (AFTCS), passive fault-tolerant control system (PFTCS), hybrid fault-tolerant control system (HFTCS), redundant system technique, special power circuit with the controller, and zero sequence voltage methods, which we will explain extensively in this article. This review emphasizes the types of faults in the MMCs and discusses the protection methods under failure conditions. The MMC is more popular in high voltage applications because it not only improves the quality of the grid but also has good harmonic performance in high power transmission. There is no need for any isolated dc sources to operate it. When faults are removed, the efficiency and reliability of the system will be increased. This extensive explanation of the current literature on MMC fault diagnosis and control techniques will conclude which methods provide a more valuable solution. Finally, this paper discusses the best approach to reduce MMC faults and provides a future research direction to the readers.

Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.

Balanced Nonadaptive Redundancy Scheduling

Distributed computing systems implement redundancy to reduce the job completion time and variability. Despite a large body of work about computing redundancy, the analytical performance evaluation of redundancy techniques in queuing systems is still an open problem. In this work, we take one step forward to analyze the performance of scheduling policies in systems with redundancy. In particular, we study the pattern of shared servers among replicas of different jobs. To this end, we employ combinatorics and graph theory and define and derive performance indicators using the statistics of the overlaps. We consider two classical nonadaptive scheduling policies: random and round-robin. We then propose a scheduling policy based on combinatorial block designs. Compared with conventional scheduling, the proposed scheduling improves the performance indicators. We study the expansion property of the graphs associated with round-robin and block design-based policies. It turns out the superior performance of the block design-based policy results from better expansion properties of its associated graph. As indicated by the performance indicators, the simulation results show that the block design-based policy outperforms random and round-robin scheduling in different scenarios. Specifically, it reduces the average waiting time in the queue to up to 25% compared to the random policy and up to 100% compared to the round-robin policy.

Process-Resilient Fault-Tolerant Delay-Locked Loop Using TMR With Dynamic Timing Correction

A delay-locked loop (DLL) circuit is often useful for the clock synchronization in a chip incorporating multiple functional dies. In this work, we present a process-resilient fault-tolerant DLL design. We will first show that a naïve triple-module redundancy (TMR) technique cannot work well unless with a simple yet powerful static timing correction scheme to nullify the adversary effect caused by the voter circuit’s delay. Furthermore, we enhance it with a dynamic scheme so that the overall performance is immune to process variation. Through the proposed schemes, the maximum phase error over 1000 clock cycles between the DLL’s input and output clock signals after locking, which is often considered as the most important performance metric, can be reduced tremendously from 130 ps using only naïve TMR to 20 ps using static timing correction, and then further down to 11 ps using the dynamic timing correction.

Reliability Analysis of Industrial Model Using Redundancy Technique and Geometric Distribution

The present paper is an initiative towards analyzing the maintenance process of an industrial model using regenerative techniques. With an increase in demands to analyze the reliability of any industrial models following continuous or discrete distribution, the major concern to be considered inspect the possibility and level of failure and proceeding the failed unit for further repairing process. The system consists of two parallel units following the passive standby redundancy and with different failure mechanism. The proposed analytical methodology in the paper made it possible to assess the reliability of the whole system in the event of failure of its components. The stochastic analysis of reliability characteristics of the considered system to the types of repair time distributions was also studied using geometric distribution. The numerical equations and results being calculated for reliability parameters like mean time to system failure, availability of system in operative form, down period of the system following repair mechanism, using regenerative techniques, and geometric distribution. Graphical and analytical analysis were presented.

A Low-cost Fault Tolerance Method for ARM and RISC-V Microprocessor-based Systems using Temporal Redundancy and Approximate Computing through Simplified Iterations

Approximate Computing techniques have been successfully used to reduce the overhead associated with redundancy in fault-tolerant system designs. This paper presents a fault tolerance method to reduce the execution time overhead of the well-known Time Redundancy technique by means of an improvement proposed for the Approximate Computing software-based technique known as loop perforation. Time Redundancy is a software-based fault tolerance technique that involves executing replicas of a task at different times. We propose to approximate the tasks to be executed using a new approximate computing technique based on loop perforation, i.e., simplified iterations. The novelty of this method is the combined use of the fault tolerance technique, temporal redundancy, jointly with the new proposed Approximate Computing technique, simplified iterations. The proposal is validated through simulation-based fault injection campaigns on several test programs for the ARM and RISC-V microprocessor architectures. Experimental results verified not only the applicability of the proposal in different architectures, but also its effectiveness, showing a good trade-off between reliability, error and overhead. Results showed that using the proposed method, a normalized mean work to failure (MWTF) up to 5.28× was obtained with approximation errors lower than those obtained using the traditional loop perforation technique.

Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals.

The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure’s power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital’s computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN’s operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks’ dependability in quality of service to cope with overloading medical services in world-wide virus pandemics.

Survey of Software-Implemented Soft Error Protection

As soft errors are important design concerns in embedded systems, several schemes have been presented to protect embedded systems against them. Embedded systems can be protected by hardware redundancy; however, hardware-based protections cannot provide flexible protection due to hardware-only protection modifications. Further, they incur significant overheads in terms of area, performance, and power consumption. Therefore, hardware redundancy techniques are not appropriate for resource-constrained embedded systems. On the other hand, software-based protection techniques can be an attractive alternative to protect embedded systems, especially specific-purpose architectures. This manuscript categorizes and compares software-based redundancy techniques for general-purpose and specific-purpose processors, such as VLIW (Very Long Instruction Word) and CGRA (Coarse-Grained Reconfigurable Architectures).

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-the-art methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

Testing a Fault Tolerant Mixed-Signal Design Under TID and Heavy Ions

This work presents results of three distinct radiation tests performed upon a fault-tolerant data acquisition system comprising a design diversity redundancy technique. The first and second experiments are Total Ionizing Dose (TID) essays, comprising gamma and X-ray irradiations. The last experiment considers single event effects, in which two heavy ion irradiation campaigns are carried out. The case study system comprises three analog-to-digital converters and two software-based voters, besides additional software and hardware resources used for controlling, monitoring and memory management. The applied Diversity Triple Modular Redundancy (DTMR) technique, comprises different levels of diversity (temporal and architectural). The circuit was designed in a programmable System-on-Chip (PSoC), fabricated in a 130nm CMOS technology process. Results show that the technique may increase the lifetime of the system under TID if comparing with a non-redundant implementation. Considering the heavy ions experiments the system was proved effective to tolerate 100% of the observed errors originated in the converters, while errors in the processing unit present a higher criticality. Critical errors occurring in one of the voters were also observed. A second heavy-ion campaign was then carried out to investigate the voters reliability, comparing the dynamic cross-section of three different software-based voter schemes implemented in the considered PSoC.

Robust neural network‐based backstepping landing control of quadrotor on moving platform with stochastic noise

AbstractThis article introduces a novel nonlinear control approach for quadrotor landing on a moving ship. The dynamics of the relative position and attitude dynamics with uncertainties, unmodeled dynamics, external disturbances, and control input saturation is derived. Then, a novel robust adaptive constrained backstepping control law for the propeller and torques of the quadrotor is designed. To deal with uncertainties and un‐modeled dynamics, radial basis function is employed. The bounded‐in‐probability stability is used to alleviate the destructive influence of the stochastic Wiener process on the system positions and attitudes. Whereas the quadrotor is under‐actuated, a redundant control input technique, which affects the attitude commands, is presented. The actual control input saturation limits are turned into unknown saturation limits of the redundant control inputs. Also, since two phases for the landing problem are considered, a smooth switching mechanism between the control inputs is developed. Simulation results show the merits and applicability of the developed robust adaptive nonlinear controller.

Defending N-Version Programming Service Components against Co-Resident Attacks in IoT Cloud Systems

The real innovation of Internet of Things (IoT) can be spurred only when being combined with cloud computing, a paradigm that allows numerous users to simultaneously access configurable resources and services. However, serious vulnerability concerns have arisen from the virtual machine co-resident architecture of the IoT cloud. Specifically, co-resident attacks can be launched, where an attacker can access and corrupt a user's sensitive data/software by co-locating their virtual machines on the same physical server. Various solutions have been suggested in literature to mitigate negative effects of the co-resident attacks in the cloud environment. However, to the best of our knowledge no work has been performed for studying co-resident attacks in cloud systems with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -version programming (NVP), a popular redundancy technique for enhancing survivability of critical cloud service components. This paper makes original contributions by modeling IoT cloud system services implementing the NVP component redundancy, and evaluating the corruption probability of the NVP service component. Further, users’ policies on choosing the optimal number of service component versions are investigated through formulating and solving a new set of optimization problems with the objective to minimize the expected cost of losses of a cloud service provider. As demonstrated through examples, these policies can effectively help defend the NVP service component against the co-resident attacks in the cloud system.

Reliability evaluation of demand-based warm standby systems with capacity storage

Warm standby is an energy-saving redundancy technique that consumes less energy than a conventional hot standby method. It can be naturally integrated with an energy storage technique to enhance system reliability. However, the integration approach of both techniques and the advantage it affords to system reliability have not been reported in literature. This study resolves this limitation by formulating a novel reliability model for demand-based warm standby systems with capacity storage. In this model, the chronological characteristics of warm standby components are explicitly explored before and after their activation. Moreover, different utilization sequences of warm standby components and storage components are embedded to analyze the effects of these sequences on system reliability. A multi-valued decision diagram is developed to evaluate system reliability considering successful activation probabilities of warm standby components. This diagram is applicable to arbitrary component lifetime distributions and warm standby systems. Numerical examples are presented to verify the application of the proposed methodology.