Fault Tolerance Mechanisms Research Articles

Towards Fault Tolerance of Reservoir Computing in Time Series Prediction

During the deployment of practical applications, reservoir computing (RC) is highly susceptible to radiation effects, temperature changes, and other factors. Normal reservoirs are difficult to vouch for. To solve this problem, this paper proposed a random adaptive fault tolerance mechanism for an echo state network, i.e., RAFT-ESN, to handle the crash or Byzantine faults of reservoir neurons. In our consideration, the faulty neurons were automatically detected and located based on the abnormalities of reservoir state output. The synapses connected to them were adaptively disconnected and withdrawn from the current computational task. On the widely used time series with different sources and features, the experimental results show that our proposal can achieve an effective performance recovery in the case of reservoir neuron faults, including prediction accuracy and short-term memory capacity (MC). Additionally, its utility was validated by statistical distributions.

A formal semantics for agent distribution and fault tolerance in Jason

This article provides a formal specification of the distribution and fault-tolerance mechanisms of eJason. The eJason programming language is an extension to the agent-oriented programming language Jason that introduces native support for the transparent distribution of agents as well as fault-tolerance mechanisms. This formal semantics is presented from a multiagent system perspective. It unambiguously describes both the possible evolution of the distributed multiagent system over time and the different instruments for fault detection and fault recovery, hence exposing their strengths. This specification may serve as a reference for researchers interested in the inclusion of similar mechanisms in agent-oriented programming languages. The formal semantics has been mechanized through an (open-source) implementation written in Prolog, which implements both the standard Jason operational semantics, along with the new rules for distribution and fault-tolerance introduced in this article.

Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows

Distributed computing services in cloud environments are easily accessible to end users. These services are delivered to end users via a subscription-based model. The “infrastructure as a service” (IaaS) cloud model is one of the best cloud environment models for running data- and computing-intensive applications. Real-world scientific applications are the best examples of data and computing intensiveness. For their implementation, scientific workflow applications need high-performance computational resources and a large volume of storage. The workflow tasks are linked based on computational and data interdependence. Considering the high volume and variety of scientific workflows (SWs), the resources of the IaaS cloud model require managing energy efficiently and without failure or loss. Therefore, in order to address the issues of power consumption and task failure for real-world SWs, this research work proposes a replication-based dynamic energy-aware resource provisioning (R-DEAR) strategy for SWs in an IaaS cloud environment. The proposed strategy, R-DEAR, is a resource- and service-provisioning strategy that implements a replication-based fault-tolerant and load-balancing mechanism. The proposed R-DEAR strategy schedules the tasks of a scientific workflow with a replication-based fault-tolerant mechanism. The proposed R-DEAR strategy also manages the power consumption of IaaS cloud resources dynamically through a load-sharing process. Simulation results show that the proposed R-DEAR strategy reduces energy consumption, execution cost, and execution time by 9%, 15%, and 18%, respectively, as compared with the existing state-of-the-art strategy.

Fault tolerance in fog-based Social Internet of Things

The Social Internet of Things (SIoT) is a glimpse into the future for self-establishment of inter-things social relationships that guarantee object-to-object transactions. This scenario adopted an intelligent strategy by the trust characteristics and typology of leveraging objects to eradicate the interference of defective nodes. In addition, SIoT networks are error-prone due to heterogeneity and potential resource constraints on the devices, which necessitates the inclusion of a mechanism to enhance the lifetime and dependability of devices. Therefore, fault tolerance mechanisms are indispensable to ensure optimal performance through fault detection and recovery. Although fog architecture and SIoT are two stand-alone paradigms, we extend fog competence into SIoT to empower resource-poor objects for handling intensive tasks. Hence a fault-tolerant model based on a clustering algorithm for fault detection and retrieval is presented. In the proposed scheme, the Markov chains model all faults diagnosed by the fog node. In the recovery stage, the status of the nodes is determined based on the type of faults and the assigned threshold to make a substitution by hot standby nodes. The four performance criteria, namely the reliability, availability, probability density function, the average time to failure, and their mathematical equations were developed on the Markov model. Furthermore, the achievements were demonstrated through either theoretical analysis or simulation results. Then, the proposed scheme increases the number of live nodes, improves the detection accuracy in forwarded requests, lowers the number of invalid requests, and enhances availability and reliability compared to networks with analogous properties.

Block Chain for IOT Security Using Consensus Algorithms

The first distributed recordkeeping system with a built-in trust structure is the block chain. It creates a dependable architecture for decentralized control through information redundancy across multiple nodes. Based on this, this study suggests a minimal block chain-based IoT information exchange security framework. The framework uses a double-chain approach that combines the data block chain and the transaction block chain. Distributed storage and tamper-proof data are implemented in the data block chain, and the consensus process is improved using the improved practical Byzantine fault-tolerant (PBFT) mechanism. Data registration efficiency, resource and data transfers, and privacy protection are all enhanced by better partial blind signature-based algorithms in the transaction block chain. This article focuses on how well the consensus algorithms employed in a block chain system for the Internet of Things perform (IoT). Such systems' time requirements to accomplish. Consensus ought to be minimal. The three most popular consensus algorithms—modified proof of work, realistic byzantine fault tolerance, and binary consensus—are assessed under various conditions, including mote type, number of participating nodes, and radio propagation model. To enable an IoT node to switch between different consensus algorithms, a comprehensive solution is put forward. The Contiki IoT operating system simulations display strong performance (time to achieve consensus less than seconds)

DRONE: An Efficient Distributed Subgraph-Centric Framework for Processing Large-Scale Power-law Graphs

Nowadays, the ever-increasing volume of graph-structured data such as social networks, graph databases and knowledge graphs requires to be processed efficiently and scalably. These natural graphs commonly found in the real world have highly skewed power-law degree distribution and are called power-law graphs. The subgraph-centric programming model is a promising approach applied in many state-of-the-art distributed graph computing frameworks. However, the performance of subgraph-centric frameworks is limited when processing large-scale power-law graphs. When deployed to the subgraph-centric framework, existing graph partitioning algorithms are not suitable for power-law graphs. In this paper, we present a novel distributed graph computing framework, DRONE (Distributed gRaph cOmputiNg Engine), which leverages the subgraph-centric model and the vertex-cut graph partitioning strategy. DRONE also supports the fault tolerance mechanism to accommodate the increasing scale of machines with negligible overhead (6.48% on average). We further study the execution workflow of DRONE and propose an efficient and balanced graph partition algorithm (EBV) for DRONE. Experiments show that DRONE reduces the running time on real-world graphs by 25.6%, on average, compared to the state-of-the-art distributed graph computing frameworks. In addition, the EBV graph partition algorithm reduces the replication factor by at least 21.8% than other self-based partition algorithms. Our results indicate that DRONE has excellent potential in processing large-scale power-law graphs.

Negotiation of the global grid inspection UAV with random delay uncertainty in an information communication network based on a robust fault tolerance mechanism

To accurately simulate the interference mechanism of information communication between unmanned aerial vehicles (UAVs) in the future global grid system, a type of control based on dynamic simulation of the satellite communication network and robust fault tolerance with a stochastic delay uncertain network system is proposed. Based on the imaginary future of the global energy Internet, with unknown information and communication interference, we established a UAV model from sensor to actuator network delay using a robust, fault-tolerant control algorithm and a satellite communication network model that combined the controller’s mathematical model. The simulation results showed improved power transmission capability and communication coverage ability of UAVs by using the network fault-tolerant control mechanism with uncertain network delay and information communication interference. The stability and anti-interference performance was also significantly improved. This algorithm provides a strategy for the future development of global energy Internet.

Software Aging Effects on Kubernetes in Container Orchestration Systems for Digital Twin Cloud Infrastructures of Urban Air Mobility

It is necessary to develop a vehicle digital twin (DT) for urban air mobility (UAM) that uses an accurate, physics-based emulator to model the statics and dynamics of a vehicle. This is because the use of digital twins in the operation and control of UAM vehicles is essential for the UAM operational digital twin infrastructure (UAM-ODT). There are several issues that need to be addressed in this process: (i) the lack of digital twin engines for the digitalization (twinization) of the dynamics and control of UAM vehicles at the core of UAM-ODT systems; (ii) the lack of back-end system engineering in the development of UAM vehicle DTs; and (iii) the lack of fault-tolerant mechanisms for the DT cloud back-end system to run uninterrupted operations 24/7. On the other hand, software aging and rejuvenation are becoming increasingly important in a variety of computing scenarios as the demand for reliable and available services increases. With the increasing use of containerized systems, there is also a need for an orchestrator to support easy management and reduce operational costs. In this paper, an operational digital twin (ODT) of a typical urban air mobility (UAM) infrastructure is developed on a private cloud system based on Kubernetes using a proposed cloud-in-the-loop simulation approach. To ensure the ODT can provide uninterrupted operational control and services in UAM around the clock, we propose a methodology for investigating software aging in Kubernetes-based containerized clouds. We evaluate the behavior of Kubernetes software using the Nginx and K3S tools while they manage pods in an accelerated lifetime experiment. We continuously execute operations for creating and terminating pods, allowing us to observe the utilization of computing resources (e.g., CPU, memory, and I/O), the performance of the Nginx and K3S environments, and the response time of an application hosted in those environments. In some conditions and for specific metrics, such as virtual memory usage, we observed the effects of software aging, including a memory leak that is not fully cleared when the cluster is stopped. These issues could lead to system performance degradation and eventually compromise the reliability and availability of the system when it crashes due to memory space exhaustion or full utilization of swap space on the hard disk. This study helps with the deployment and maintenance of virtualized environments from the standpoint of system dependability in digital twin computing infrastructures where a large number of services are running under strict continuity requirements.

Current Development, Challenges, and Future Trends in Cloud Computing: A Survey

Cloud computing is a new paradigm in information and communication technologies (ICTs) that provides the ability to access shared pools of different computing resources that are related to many cloud users within a pay-per-use or on-demand approach. It has transformed the delivery model of ICT from a product to a service. This provides several different advantages for institutions, companies and users based on savings and reduced capital expenditure through lower operating expenses. This paper provides a comprehensive survey of cloud computing. It first develops an understanding of cloud computing in general and discusses its advantages, current development, challenges and future trends. Subsequently, a detailed discussion on the cloud computing architectures, services models, fault tolerance mechanisms, services selection methods, adoption by industry, and scheduling of cloud-based resources is also presented. Nonetheless, cloud computing has many obstacles which expose it to a number of limitations. Some of these challenges include security of data, fault tolerance, and load balancing. A number of techniques in literature are proposed to cope with these challenges which are discussed and analyzed. Experimental data and usage drift validates the popularity of cloud computing and its adoption in recent years. Future trends in cloud computing support the use of intelligent machine learning (ML) techniques and new technologies to cope with some of the challenges and making cloud computing more efficient, secure and commercially viable to be widely accepted.

A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

The reliability and availability of cloud systems have become major concerns of service providers, brokers, and end-users. Therefore, studying fault-tolerance mechanisms in cloud computing attracts intense attention in industry and academia. The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems. A task-scheduling mechanism distributes tasks to a group of instances to be executed. Much work has been undertaken in this direction to improve the overall outcome of cloud computing, such as improving service quality and reducing power consumption. However, little work on task scheduling has studied the problem of lost tasks from the broker’s perspective. Task loss can happen due to virtual machine failures, server crashes, connection interruption, etc. The broker-based concept means that the backup task can be allocated by the broker on the same cloud service provider (CSP) or a different CSP to reduce costs, for example. This paper proposes a novel fault-tolerant mechanism that employs the primary backup (PB) model of task scheduling to address this issue. The proposed mechanism minimizes the impact of failure events by reducing the number of lost tasks. The mechanism is further improved to shorten the makespan time of submitted tasks in cloud systems. The experiments demonstrated that the proposed mechanism decreased the number of lost tasks by about 13%–15% compared with other mechanisms in the literature.

An Optimal Cluster Head and Gateway Node Selection with Fault Tolerance

In Mobile Ad Hoc Networks (MANET), Quality of Service (QoS) is an important factor that must be analysed for the showing the better performance. The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection (NQCAFFFOCHGS) has the best network performance because it uses the Improved Weighted Clustering Algorithm (IWCA) to cluster the network and the FFO algorithm, which uses fuzzy-based network metrics to select the best CH and entryway. However, the major drawback of the fuzzy system was to appropriately select the membership functions. Also, the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway. When learning fuzzy sets, this algorithm employs a new Continuous Action-set Learning Automata (CALA) approach that correctly modifies and chooses the fuzzy membership functions. Despite the fact that it extends the network’s lifespan, it does not assist in the detection of defective nodes in the routing route. Because of this, a new Fault Tolerance (NQCAEFFFOCHGS-FT) mechanism based on the Distributed Connectivity Restoration (DCR) mechanism is proposed, which allows the network to self-heal as a consequence of the algorithm’s self-healing capacity. Because of the way this method is designed, node failures may be utilised to rebuild the network topology via the use of cascaded node moves. Founded on the fractional network information and topologic overhead related with each node, the DCR is suggested as an alternative to the DCR. When compared to the NQCAFFFOCHGS algorithm, the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay, energy consumption, Packet Loss Ratio (PLR), Normalized Routing Overhead (NRO), and Balanced Load Index (BLI).

Blockchain Assisted Federated Learning for Enabling Network Edge Intelligence

The recently emerging federated learning (FL) exploits massive data stored at multiple user nodes to train a global optimal learning model without leaking the privacy of user data. However, it is still inadequate to learn the global model safely at the centralized aggregator, which is an essential part for the traditional FL architecture. Specifically, when using FL in radio access networks to enable edge intelligence, it is difficult for a central server, which belongs to a third party, to guarantee its credibility. Moreover, because the central server may cause a single point of failure, its reliability is also difficult to guarantee. Besides, a malicious participating node of FL may send ill parameters for model aggregation. In this article, we develop a blockchain assisted federated learning (BC-FL) framework, with aim to overcome the single point of failure caused by central server. Meanwhile, we propose to use blockchain to implement auditing of individual involved nodes to ensure the reliability of learning process. To avoid privacy leakage during the audit process to the greatest extent, we design a matching audit mechanism to realize efficient random matching audit process. A cryptocurrency free delegated byzantine fault tolerant (CF-DBFT) consensus mechanism is also designed to realize the low-latency distributed consensus of all nodes in the FL proces. We apply the proposed BC-FL framework to resolve the computing resource allocation problem at the edger servers in MEC network. Simulation results demonstrate the effectiveness and performance superiority of the proposed BC-FL framework. Compared with legacy FL algorithm, the serving time of MEC servers and utilization of computing resource are increased by 35% and 48% respectively under our proposed BC-FL algorithm.

Online Music Teaching Resource Sharing Algorithm Based on Blockchain Technology

Blockchain technology provides technical support for cloud computing data sharing in data storage and data access, and ensures the safe and effective transmission of data sharing. The online music teaching resource platform based on blockchain technology faces the problems of low data throughput, large delay and unstable performance. Therefore, a blockchain-based sharing algorithm (Practical Byzantine Fault Tolerance, PBFT), PBFT algorithm is proposed. Consensus allows 1/3 of cheating points in the blockchain, and authenticated transactions are confirmed through a confirmation mechanism to ensure data security. However, the traditional PBFT algorithm lacks an efficient node joining and exiting mechanism, and has problems such as low data throughput. Therefore, on the basis of the traditional PBFT algorithm, a blockchain sharing algorithm (WBFT) that improves the weighted Byzantine fault tolerance mechanism is proposed. A weighting mechanism is added in the consensus process. After the consensus, the node behavior will dynamically adjust the weights to reduce the adverse impact of malicious nodes on the data and improve the overall data sharing effect. Experimental performance tests show that the improved WBFT algorithm performs best in multi-algorithm delay performance tests. In the data average throughput test, the performance of the improved WBFT algorithm is 20.2% higher than that of the traditional PBFT algorithm. In the security test, the improved WBFT algorithm has the best performance. The proposed improved WBFT algorithm can well meet the needs of building an online music teaching resource sharing platform. The research content has important reference value for the application of blockchain technology in the education industry, and is conducive to promoting the development of modern education.

Lightweight Multidimensional Encrypted Data Aggregation Scheme With Fault Tolerance for Fog-Assisted Smart Grids

Encrypted data aggregation is an effective approach to preserve power consumption data confidentiality as well as reduce communication overhead in smart grids. As the core component of smart grids, smart meters periodically report encrypted multidimensional data to an aggregator gateway. However, heavy computation and communication overhead may hinder the wide applications of smart meters. Besides, once the secret key is leaked, encrypted data will be broken, thereby violating users’ privacy. In this article, we present a lightweight multidimensional encryption data aggregation (MD-EDA) scheme, enabling a grid control center to retrieve fine-grain aggregated data in a confidential way. In particular, each fog node is installed at the edge of networks, playing the role of aggregator gateway for data verification and aggregation in the same grid area. MD-EDA prevents adversaries from extracting valuable data even if the secret key is compromised. MD-EDA exploits Shamir secret-sharing technique to achieve transmission fault tolerance mechanism from smart meters to a corresponding fog node. The performance evaluation demonstrates the lightweight computation advantages of MD-EDA in the deployment of fog-assisted smart grids.

Online Pressure Map Reconstruction in a Multitouch Soft Optical Waveguide Skin

In this article, we present a reconstruction method for mapping a wide range of pressure stimuli in a multitouch soft optical waveguide skin (MSOWS). Distributed sensing information is typically obtained from wired soft sensing units. This approach hardly adapts to different shapes and has limited durability against stresses and shocks. In our MSOWS, the spatial tactile transduction relies on a continuum sensitive area of graded-stiffness polydimethylsiloxane, with a virtual grid free from electronics and connections. The sensing range of each cell is up to 234 kPa, and the spatial resolution is 5 mm. We use a time-delay neural network to reduce the hysteresis of the soft material to less than 1%. In addition, a fault-tolerant mechanism makes the entire system robust to component failure. These results may open the way to a new generation of distributed soft skins that are versatile due to material design and processing.

Cost-Effective Spot Instances Provisioning Using Features of Cloud Markets

Cloud computing offers a variable-cost payment scheme that allows cloud customers to specify the price they are willing to pay for renting spot instances at much lower costs than fixed payment schemes, and depending on the varying demand from cloud customers, cloud platforms could revoke spot instances at any time. To alleviate the effect of spot instance revocations, applications often employ different fault-tolerance mechanisms to minimize or even eliminate the lost work for each spot instance revocation. However, these fault-tolerance mechanisms incur additional overhead related to application completion time and deployment cost. This article proposes a novel cloud market-based approach for provisioning spot instances using features of cloud markets to reduce the deployment cost and completion time of applications. The simulation results show that the approach reduces the deployment cost and completion time compared to approaches based on fault-tolerance mechanisms.

PRBP: A prioritized replica balancing policy for HDFS balancer

AbstractData replication is the main fault tolerance mechanism implemented by the Apache Hadoop Distributed File System (HDFS). The placement of the data across the cluster directly affects replica balancing and data locality. The HDFS Balancer is the native solution to rebalance the data distribution by moving the blocks from over‐utilized to under‐utilized nodes. Nevertheless, its current balancing policy does not address the characteristics and specific needs of the applications during data rearrangement. In this work, we present the PRBP, a customized replica balancing policy for the HDFS Balancer. The PRBP is based on a system of priorities, which can be adapted and configured according to different demands of use, either these are related to heterogeneous environments or focused on improving data reliability and availability. The priorities define whether system metrics or aspects of the cluster topology should be considered during the execution of the HDFS Balancer, thus making the process of replica balancing in HDFS more flexible. Based on the priority system, we determine association rules that allow the use of multiple priorities simultaneously. Along with these rules, we present guidelines for using the PRBP as a specialized solution in scenarios that can benefit from reactive replica balancing. In addition, we conducted a practical experimentation to highlight the behavior and the applicability of the guidelines of the PRBP to prioritize replica rearrangement in the file system.

A Fuzzy Comprehensive Evaluation Study on the Performance of Age-Friendly Digital Retrofit Based on User Experience: Take the “Elder Mode” App as an Example

To study the digital needs, activities, and experiences of elderly users, evaluate the performance of age-friendly digital retrofit, and provide a path to bridge the digital divide among elderly users. Based on the user experience perspective, the age-friendly evaluation model of the “elder mode” APP was constructed, the index weights were determined based on the entropy weight method, and WeChat, Alipay, ZOA, and ZARA of the “elder mode” were selected as the research objects. We use questionnaires and in-depth interviews to collect user data and use a fuzzy comprehensive evaluation method for empirical evaluation. The overall experience of elderly users on the four APPs is at the upper level of “generally satisfied,” among which the age-friendly level of “visual experience” and “behavioral experience” is high, while the level of “interaction experience” and “value experience” needs to be improved. In particular, attention needs to be paid to the retrofit of the fault-tolerant mechanism, toughness, and error correction of digital products. The paper proposes countermeasures in terms of enhancing the compatibility of interface elements of digital products, optimizing embedded interaction procedures, improving human-computer interaction options, enhancing fault tolerance of operations, and improving age-friendly continuity and thoroughness.

An online intelligent electronic medical record system via speech recognition

Traditional electronic medical record systems in hospitals rely on healthcare workers to manually enter patient information, resulting in healthcare workers having to spend a significant amount of time each day filling out electronic medical records. This inefficient interaction seriously affects the communication between doctors and patients and reduces the speed at which doctors can diagnose patients’ conditions. The rapid development of deep learning–based speech recognition technology promises to improve this situation. In this work, we build an online electronic medical record system based on speech interaction. The system integrates a medical linguistic knowledge base, a specialized language model, a personalized acoustic model, and a fault-tolerance mechanism. Hence, we propose and develop an advanced electronic medical record system approach with multi-accent adaptive technology for avoiding the mistakes caused by accents, and it improves the accuracy of speech recognition obviously. For testing the proposed speech recognition electronic medical record system, we construct medical speech recognition data sets using audio and electronic medical records from real medical environments. On the data sets from real clinical scenarios, our proposed algorithm significantly outperforms other machine learning algorithms. Furthermore, compared to traditional electronic medical record systems that rely on keyboard inputs, our system is much more efficient, and its accuracy rate increases with the increasing online time of the proposed system. Our results show that the proposed electronic medical record system is expected to revolutionize the traditional working approach of clinical departments, and it serves more efficient in clinics with low time consumption compared with traditional electronic medical record systems depending on keyboard inputs, which has less recording mistakes and lows down the time consumption in modification of medical recordings; due to the proposed speech recognition electronic medical record system is built on knowledge database of medical terms, so it has a good generalized application and adaption in the clinical scenarios for hospitals.

Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System

Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System