High Degree Of Fault Tolerance Research Articles

Optimizing fault tolerance of RAM cell through MUX based modeling and design using symmetries of QCA cells

Extensive research is now being conducted on the design and construction of logic circuits utilizing quantum-dot cellular automata (QCA) technology. This area of study is of great interest due to the inherent advantages it offers, such as its compact size, high speed, low power dissipation, and enhanced switching frequency in the nanoscale domain. This work presents a design of a highly efficient RAM cell in QCA, utilizing a combination of a 3-input and 5-input Majority Voter (MV) gate, together with a 2 × 1 Multiplexer (MUX). The proposed design is also investigated for various faults such as single cell deletion, single cell addition and single cell displacement or misalignment defects. The circuit under consideration has a high degree of fault tolerance. The functionality of the suggested design is showcased and verified through the utilization of the QCADesigner tool. Based on the observed performance correlation, it is evident that the proposed design demonstrates effectiveness in terms of cell count, area, and latency. Furthermore, it achieves a notable improvement of up to 76.72% compared to the present configuration in terms of quantum cost. The analysis of energy dissipation, conducted using the QCAPro tool, is also shown for various scenarios. It is seen that this design exhibits the lowest energy dispersion, hence enabling the development of ultra-low power designs for diverse microprocessors and microcontrollers.

An online stochastic MPC-based fault-tolerant optimization for microgrids

Due to the current energy dependence of society, the availability and correct functioning of microgrids are strategic issues to be dealt with. Currently, energy management systems are very focused on achieving systems that present a remarkable optimization of demand as well as a high degree of fault tolerance. This paper presents a novel Control Reconfiguration framework to manage faults based on Model Predictive Control (MPC). The proposal comprises fault detection, isolation and reconfiguration. The Fault Detection and Isolation method identifies the faults based on parity equations, structured residuals and stochastic thresholds, while the reconfiguration is focused on adapting the control law to the faulty scenario. Therefore, two different model predictive controllers are involved: MPC-1 drives the microgrid to the correct values and MPC-2 carries out the control reconfiguration, optimizing a multi-criteria objective function where outputs are the values of criteria. The new decision variables of the fault reconfiguration are the selection of mitigation actions to be performed to reduce the effects of faults. A novel formulation of this problem is provided. Experiments have been carried out on a real microgrid located in the laboratory to show the benefits of the method.

Hadoop Technology

'Big Data 'describes and technologies to store, distribute, manage and analyze large-sized datasets with high-velocity. Big data can be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. Data is generated from various different sources and can arrive in the system at various rates. In order to process these large amounts of data in an inexpensive and efficient way, parallelism is used. Hadoop is the core platform for structuring Big Data, and solves the problem of making it useful for analytics purposes. Hadoop is an open-source software project that enables the distributed processing of large data sets with a very high degree of fault tolerance.

A Review Paper on Big Data and Hadoop is solution for Big Data

The amount of data produced and communicated over Internet is significantly increased. The formation of this massive amount of data through divergent source is massive. Now days a Big Data application where data collection has grown very fast and traditional software tools are unable to capture, manage, and process it. In this paper we are highlighting Big Data its sources and types such as structured, unstructured and semi structured. Data is generated from various different sources and can arrive in the system at various rates. In order to process these large amounts of data in an inexpensive and efficient way, parallelism is used. Big Data is a data whose scale, diversity, and complexity require new architecture to manage it and extract value and hidden knowledge from it. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance.

1MW-Class Solid Oxide Electrolyser System Prototype for Low-Cost Green Hydrogen

Ceres has developed a novel and highly differentiated SteelCell™ solid oxide fuel cell technology based upon the use of thick-film ceramics deposited upon a ferritic stainless-steel substrate, using doped ceria as the predominant oxygen-ion conducting ceramic within the cell. The use of ceria allows operation of the SteelCell at lower temperatures (500 - 620 °C) than conventional SOFCs, and the use of a metal support allows much greater mechanical robustness and lower production cost than is typically the case with a planar ceramic SOFC, whilst maintaining the high volumetric power density typical of planar SOFCs.Ceres Power are now qualifying their SteelCell™ technology for use in steam electrolysis operation with encouraging results. Tests suggest that functional electrochemical and physical processes of the SteelCell are equally efficient in SOFC and SOEC mode meaning there is no major difference in the area specific resistance and degradation within the nominal operating space.As a next step, the development of a 1MW-class Solid Oxide Electrolysis (SOEL) system is proposed that offers a path to green hydrogen production costs of <$2/kgH2 with equally compelling capital equipment costs enabled by the SteelCell technology. One of the clear advantages of solid oxide electrolysis systems is the ability to thermally integrate with industrial processes such as steel, ammonia and e-fuels production. Integration with existing low pressure “waste” steam feeds available at industrial plants combined with available high grade heat streams may enable system efficiencies of greater than 90% to be achieved. A study of predicted system efficiencies and associated techno-economics of green hydrogen production under various industrial use cases is presented.The first-of-a-kind 1MW-class Solid Oxide Electrolysis system under consideration will be packaged within a 40ft ISO shipping container and is expected to produce approximately 600 to 800kg of green hydrogen per day. The system will be made up of a number of Electrolysis Cell Modules (ECM). Each ECM will contain an array of Ceres’ standard Gen1B stacks along with hot balance of plant components such as heat exchangers and heaters. This modular approach allows each module to be constructed and tested prior to installation at the demonstration site. It also allows for a high degree of fault tolerance and redundancy that is particularly valuable for prototype systems. Furthermore, it also enables a distributed supply chain to be developed for various elements up and down the system structure.Finally, a roadmap to the first commercial SOEL systems is presented setting out predicted system CapEx costs, hydrogen production costs and possible arrangements for systems in the 100+ MW range.

Quantitative Shape Measurement of an Inflatable Rubber Dam Using an Array of Inertial Measurement Units

Shape measurement plays an important role in the condition monitoring and operation control of inflatable rubber dams. This article presents a method to measure the cross-sectional shape of a rubber dam using an array of inertial measurement units (IMUs) placed on the circumference of the dam. Accelerometer and gyroscope measurements are combined using an adaptive complementary filter to determine the tangent angles of the dam circumference. The adaptive complementary filter adjusts the weights of the accelerometer and gyroscope measurements dynamically in order to reduce the uncertainty in orientation estimation due to external acceleration under dynamic conditions. A natural cubic spline that interpolates the measured tangent angles at discrete locations is used to represent the tangent angles along the dam circumference as a continuous function of the arc length. Finally, the cross-sectional shape is reconstructed by integrating the continuous tangent angle function along the circumference of the dam. Experimental assessment of the measurement system was performed on a purpose-built test rig using a digital camera as a reference measuring device. Results under a typical static condition show that the measured and reference shapes agree well with each other, with a similarity index being 3.74%, a mismatch distance of the last IMU node being 12.3 mm, and a relative error of height measurement being −2.44%. Under dynamic conditions, the measurement results deteriorate due to external acceleration, but considerable improvement is achieved in comparison with an accelerometer-only approach. In addition, the elimination of faulty nodes from shape reconstruction has negligible influence on the results, suggesting that the measurement system enjoys a high degree of fault tolerance.

An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud

With the increasing popularity and acceptance of cloud computing, it is being applied in services like executing large-scale applications, where cloud environment is selected by the scientific associations to easily execute the computation intensive workflows. However, cloud computing can have higher failure rates due to the larger number of servers and components filled with the intensive workloads. These failures may lead to the unavailability of virtual machines (VMs) for computation. Hence, this issue of fault occurrences can be tolerated by adopting an effective and efficient fault tolerant strategy. The goal of our research in this paper is to develop an adaptive fault detector strategy based on Improved Differential Evolution (IDE) algorithm in cloud computing that can minimize the energy consumption, the makespan, the total cost and, at the same time, tolerate up faults when scheduling scientific workflows. This proposed work applies an adaptive network-based fuzzy inference system (ANFIS) prediction model to proactively control resource load fluctuation that increases the failure prediction accuracy before fault/failure occurrence. In addition, it applies a reactive fault tolerance technique for when a processor fails and the scheduler must allocate a new VM to execute the workflow tasks. The experimental results show that compared with existing techniques, the proposed approach significantly improves the overall scheduling performance, achieves a higher degree of fault tolerance with high HyperVolume (HV) compared with the ICFWS, IDE, and ACO algorithms, minimizes the makespan, the energy consumption and task fault ratio, and reduces the total cost.

Lower bounds for dilation, wirelength, and edge congestion of embedding graphs into hypercubes

Interconnection networks provide an effective mechanism for exchanging data between processors in a parallel computing system. One of the most efficient interconnection networks is the hypercube due to its structural regularity, potential for parallel computation of various algorithms, and the high degree of fault tolerance. Thus it becomes the first choice of topological structure of parallel processing and computing systems. In this paper, lower bounds for the dilation, wirelength, and edge congestion of an embedding of a graph into a hypercube are proved. Two of these bounds are expressed in terms of the bisection width. Applying these results, the dilation and wirelength of embedding of certain complete multipartite graphs, folded hypercubes, wheels, and specific Cartesian products are computed.

Privacy preserving distributed training of neural networks

Learnae is a system aiming to achieve a fully distributed way of neural network training. It follows a “Vires in Numeris” approach, combining the resources of commodity personal computers. It has a full peer-to-peer model of operation; all participating nodes share the exact same privileges and obligations. Another significant feature of Learnae is its high degree of fault tolerance. All training data and metadata are propagated through the network using resilient gossip protocols. This robust approach is essential in environments with unreliable connections and frequently changing set of nodes. It is based on a versatile working scheme and supports different roles, depending on processing power and training data availability of each peer. In this way, it allows an expanded application scope, ranging from powerful workstations to online sensors. To maintain a decentralized architecture, all underlying tech should be fully distributed too. Learnae’s coordinating algorithm is platform agnostic, but for the purpose of this research two novel projects have been used: (1) IPFS, a decentralized filesystem, as a means to distribute data in a permissionless environment and (2) IOTA, a decentralized network targeting the world of low energy “Internet of Things” devices. In our previous work, a first approach was attempted on the feasibility of using distributed ledger technology to collaboratively train a neural network. Now, our research is extended by applying Learnae to a fully deployed computer network and drawing the first experimental results. This article focuses on use cases that require data privacy; thus, there is only exchanging of model weights and not training data.

Dynamic analysis and performance of a repoint track switch

ABSTRACTRepoint is an alternative concept for the design of track switches developed at Loughborough University. The concept, based around a stub switch, offers several improvements over current designs. Through a novel locking arrangement, it allows parallel, multi-channel actuation and passive locking functions, providing a high degree of fault tolerance. The aim of the work presented in this paper is to evaluate the dynamic interaction forces due to the passage of rolling stock over the switch and, particularly, the area of the stub rail ends, in comparison to a conventional switch. Specific behaviour and load transfer conditions from one rail to the other at the joint are analysed, as well as long-term wear conditions of the rails. These evaluations are undertaken by means of multi-body dynamic simulations, leading to design refinement of the stub rail ends and the identification of further research and development requirements in their design.

Fault tolerant adaptive parallel and distributed simulation through functional replication

This paper presents FT-GAIA, a software-based fault-tolerant parallel and distributed simulation middleware. FT-GAIA has being designed to reliably handle Parallel And Distributed Simulation (PADS) models, which are needed to properly simulate and analyze complex systems arising in any kind of scientific or engineering field. PADS takes advantage of multiple execution units run in multicore processors, cluster of workstations or HPC systems. However, large computing systems, such as HPC systems that include hundreds of thousands of computing nodes, have to handle frequent failures of some components. To cope with this issue, FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some protection against Byzantine failures, since interaction messages among the simulated entities are replicated as well, so that the receiving entity can identify and discard corrupted messages. Results from an analytical model and from an experimental evaluation show that FT-GAIA provides a high degree of fault tolerance, at the cost of a moderate increase in the computational load of the execution units.

Energy-Aware Fault-Tolerant Dynamic Task Scheduling Scheme for Virtualized Cloud Data Centers

As clouds have been implemented and widely used in various fields, both the size and the number of cloud data centers (CDCs) are growing rapidly. Serious problems have been raised, such as the inefficient use of resources, high energy consumption, and failure of heterogeneous task execution. The existing studies have aimed to solve these challenging problems separately, but it is difficult to optimize resources and energy efficiency while simultaneously providing fault-tolerance. In this study, a dynamic task assignment and scheduling scheme, namely, the energy-aware fault-tolerant dynamic scheduling scheme (EFDTS), is developed to coordinately optimize resource utilization and energy consumption with a fault tolerant mechanism. In the task assignment scheme, a task classification method is developed to partition the coming tasks into different classes and then allocate them to the most suitable virtual machines based on their classes to reduce the mean response time while considering energy consumption. Replication is used for the fault tolerance to minimize the task rejection ratio caused by machine failure and delay. An elastic resource provisioning mechanism is designed in the context of fault-tolerance to improve resource utilization and energy efficiency. Furthermore, a migration policy is developed that can simultaneously improve resource utilization and energy efficiency. The experimental results show that compared with existing techniques, EFDTS significantly improves the overall scheduling performance, achieves a higher degree of fault tolerance with high CDC resource utilization, minimizes the mean response time and task rejection ratio, and reduces energy consumption.

The Fundamental Primitives with Fault-Tolerance in Quantum-Dot Cellular Automata

Since conventional CMOS technology has met its development bottleneck, an alternative technology, quantum-dot cellular automata (QCA), attracted researchers’ attention and was studied extensively. The manufacturing process of QCA, however, is immature for commercial production because of the high defect rate. Seeking for designs that display excellent performance shows significant potentials for practical realizations. In the paper we propose a 5 × 5 module, which not only can implement three-input majority gate but also can realize five-input majority gate by adding another two inputs. A comprehensive analysis is made in terms of area, number of cells, energy dissipation and fault tolerance against single-cell omission defects. In order to testify the superiority of the proposed designs, preexisting related designs are tested and compared. Weighing up above four kinds of factors and technical feasibility, proposed majority gates perform fairly well. Further, we take full adders and multi-bit adders as illustrations to display the practical application of proposed majority gates. The detailed comparisons with previous adders reveal that proposed 5 × 5 module behaves well in circuits, especially the high degree of fault tolerance and the relatively small area, complexity and QCA cost, thereby making it more suitable for practical realizations in large circuit designs.

Communication Technologies and Network Protocols of Automotive Systems

Automotive industry has gone through rapid changes in the past few years. The usage of electronics and electronic control units (ECUs) have increased manifold, and this has also affected the way different subsystems communicate. Communication technologies and protocols are required to fulfill demands of fault-tolerance, dependability, bandwidth and determinism of demanding and safety-critical applications. This paper presents a survey of state-of-the-art and the most commonly employed communication technologies and protocols; both wired and wireless for in-vehicle and vehicle to vehicle (V2V) communication in the automotive systems. The technologies such as LIN (Local Interconnect Network), CAN (Controller Area Network), MOST (Media Oriented Systems Transport), and Flexray are compared in terms of the performance, reliability, cost and protocol characteristics. The study shows that Flexray is an excellent network topology for in-vehicle communication that has higher degree of fault tolerance, and is suitable for hard real time systems with high bandwidth. Moreover, wireless technologies i.e. Bluetooth, ZigBee, Wi-Fi and UWB are discussed that satisfy different requirements of diagnostics and multimedia communication for in-vehicle and vehicle to vehicle communication and can be used for advanced autonomous driving systems. The paper also presented issues that need to be addressed to fully realize the potential of these communication technologies and other advancements in automotive industry.

Analysis and Review the Data Using Big Data Hadoop

Big information is pool of huge and complicated information sets so it becomes tough to method information exploitation management tools. The term ‘Big Data’ illustrates innovative method and knowledge to capture, store, distribute, handle and evaluate petabyte or larger-sized datasets with high-speed and totally different structures. Huge knowledge may be structured, unstructured or semi-structured, leading to incapability of standard knowledge management ways. With the quick evolution of information, information storage and networking assortment capability, massive information area unit quickly growing altogether science and engineering domains. Knowledge is generated from numerous totally different sources and might arrive within the system at numerous rates. So as to method these giant amounts of information in a cheap and economical approach, similarity are employed. Huge knowledge may be knowledge whose scale, diversity, and quality need new design, techniques, algorithms, and analytics to manage it and extract price and hidden information from it. The analysis of huge information typically tough because it often involves assortment of mixed information supported completely different patterns or rules. The challenges embrace capture, storage, search, sharing, analysis, and visualization. The trend to massive information sets is owing to the additional info drawn from analysis of one large set of connected information, compared to separate smaller sets with constant total quantity of information. Massive data processing is that the ability of extracting helpful info from streams of information or datasets, that owing to its rate, variability and volume. This paper argues applications of huge processing model and conjointly massive data processing. Hadoop is that the core platform for structuring huge knowledge, and solves the matter of constructing it helpful for analytics functions. Hadoop is Associate in nursing open supply software system project that permits the distributed process of huge knowledge sets across clusters of goods servers. It’s designed to rescale from one server to thousands of machines, with a awfully high degree of fault tolerance.

Embedding of Hypercube into Extended Rooted Theta Mesh

The hypercube is one of the most popular interconnection networks due to its structural regularity, potential for parallel computation of various algorithms, and the high degree of fault tolerance. In this paper, we introduce a graph called extended rooted theta mesh and we compute the exact wirelength of embedding r -dimensional hypercube into r -dimensional extended rooted theta mesh, r≥ 2

OdNEAT: An Algorithm for Decentralised Online Evolution of Robotic Controllers.

Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-(μ + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM-(μ + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.

Axial-Flux Hybrid-Excitation Synchronous Machine: Analysis, Design, and Experimental Evaluation

This paper presents an axial-flux machine with hybrid excitation (HE), in which the flux linkage can be regulated in an extremely wide range by using a rotating excitation winding. This machine shows a theoretically infinite constant power speed range and also exhibits a high degree of fault tolerance against uncontrolled generator operation. The machine has a single-stator double-rotor topology, with one rotor mounting permanent magnets and the other supporting the excitation winding. After a review of the state of the art of HE machines, this paper examines the principle of operation of this machine. Subsequently, a prototype is designed, analyzed with finite-element analysis, and built. Finally, experimental results are reported to confirm the theoretical predictions and the benefits and drawbacks of this novel topology.

Embedding of Hypercubes into l-Sibling Trees

The hypercube is one of the most popular interconnection networks due to its structural regularity, potential for parallel computation of various algorithms, and the high degree of fault tolerance. In this paper, we introduce a tree called l-sibling trees and the main results obtained in this paper are: (1) For r ≥ 1, the minimum wirelength of embedding r-dimensional hypercube into r-dimensional l-sibling tree. (2) For r ≥ 1, embedding of r-dimensional extended l-sibling tree into caterpillar with minimum dilation. (3) Based on the proof of (1), we provide an O(r)-linear time algorithm to compute the minimum wirelength of embedding r-dimensional hypercube into r-dimensional l-sibling tree.

A survey of checker architectures

Reliability is quickly becoming a primary design constraint for high-end processors because of the inherent limits of manufacturability, extreme miniaturization of transistors, and the growing complexity of large multicore chips. To achieve a high degree of fault tolerance, we need to detect faults quickly and try to rectify them. In this article, we focus on the former aspect. We present a survey of different kinds of fault detection mechanisms for processors at circuit, architecture, and software level. We collectively refer to such mechanisms as checker architectures . First, we propose a novel two-level taxonomy for different classes of checkers based on their structure and functionality. Subsequently, for each class we present the ideas in some of the seminal papers that have defined the direction of the area along with important extensions published in later work.