Designing Reliable Architecture for Stateful Fault Tolerance

Cloud computing alters the way organizations manage and deploy their IT resource. It provides an organization with scalable, inexpensive, and flexible options. The complexity and dynamic nature of cloud environments pose a challenge to maintaining high availability at all times, especially when the system fails or a disaster arises. The legacy techniques of disaster recovery, fault tolerance, and high availability leave behind much to be desired. These techniques are mostly static, slow to respond, and have a dismal ability to adapt to continuously changing conditions in contemporary cloud systems. Such techniques largely depend on manual configurations and predefined policies; resulting in lots of inefficiencies and increases in the risk of service downtime. This research investigates the way Artificial Intelligence (AI) changes the paradigm on cloud resilience to promote adoption of intelligent systems for guaranteed disaster recovery, better fault-tolerant behavior, or transparent high availability. With machine learning algorithms, AI-based cloud services utilize large data volumes to reveal patterns within system logs, performance metrics, and user behavior data; thereby offering real-time anomaly detection and predictive failure analysis. For example, techniques like predictive analytics help cloud providers predict likely system outages, optimize the resources to be used, and automate failover processes (Xu et al., 2021; Lee & Kumar, 2022). AI-aided disaster recovery techniques employ complex algorithms to produce an adaptive backup mechanism, thereby minimizing loss and reducing restoration time. Fault tolerance in AI cloud systems comes from intelligent error correction, automatic isolation of faults, and self-healing features, i.e. repair of faults without the need for human supervision (Chen et al., 2020). Besides that, AI also contributes to high availability through intelligent load balancing, which ensures that at any given time, resources are optimally distributed throughout the network to sustain continuous service even during peak demand or unanticipated failures (Patel & Zhang, 2023). The approach is a comprehensive review of the various existing literature on the topic, empirical analysis of the current AI-driven cloud solutions available in the market, and case studies for comparison analysis on the different AI systems. The study scenario reveals that AI-driven solutions noticeably reduce downtimes, lead to improved recovery times, and contribute to overall system reliability as compared to traditional methods. However, other areas include model bias, data privacy, and continuous training of AI models. This study expands the trends of AI in the field of cloud computing by documenting the significance of intelligent systems in bridging traditional weaknesses of resilience strategies. It further signifies the need for AI into predictive maintenance, automated disaster response, and proactive fault management of rapidly changing dynamic cloud environments. Future studies will focus on AI integration along with edge computing and blockchain technologies for even more robust and secure services in cloud operations.

In modern distributed systems, handling large-scale data efficiently is a key challenge, especially when dealing with structured and unstructured files stored in the Hadoop Distributed File System (HDFS) [1]. This paper presents an API- based solution using the Spring framework to process files in distributed file system, transforming them based on specific busi- ness requirements and storing the results back into distributed storage. The proposed architecture ensures high availability, fault tolerance, and efficient workload distribution through the integration of Apache Zookeeper [2] for consensus management and Docker [6] for containerized execution. We have distributed processing frameworks like Spark [8], which cannot be used in some cases where a certain process requires installing a software which cannot be done in distributed file system for security reasons. Approach discussed in this paper leverages the parallel execution of multiple Spring-based microservices, each deployed as independent Docker [6] containers, allowing for scalable and efficient processing. More instances of the Spring application can run simultaneously, ensuring that files are processed in a distributed manner to maximize throughput. The API facilitates seamless interaction with the HDFS [1] cluster, enabling efficient read, transformation, and write operations. To ensure coordination among instances, Apache Zookeeper [2] is used to manage leader election, task allocation, and synchronization, preventing conflicts and ensuring load balancing across nodes. The parallel processing workflow significantly improves the performance and resilience of the system. By running multiple instances in a containerized environment, our solution dynamically scales based on workload demands. Additionally, Zookeeper [2] ensures that processing tasks are distributed optimally, preventing redundant operations and maintaining system consistency. The paper provides a solution that demonstrates reduced processing time and improved fault tolerance compared to traditional single- instance processing methods. Through this paper, we highlight the benefits of combining Spring Boot [3], HDFS [1], Docker [6], and Zookeeper [2] for scalable and efficient distributed file processing.

In this paper, a framework of Controller Placement Problem (CPP) is implemented for dynamic mapping and switch migration across controllers in SDN distributed network architecture. Then a heuristic multi-level capacitated CPP (MCCPP) is formulated to efficiently assigned each switch to a corresponding controller to improve the reliability and scalability of the SDN system. The proposed method simultaneously identifies the optimum number of controllers, minimizes the delay, and ensures load balancing among controllers. Then a Genetic Algorithm (GA) is integrated with the allocation components to improve the controller locations. The effectiveness of the GA-based MCCPP is compared against heuristics CCPP over different network topologies. The adaptive GA-based yields excellent results in latency and only requires a few iterations to attain convergence.

Wireless Sensor Networks (WSNs) have undergone significant advancements over the past few decades, becoming a crucial component in various applications such as environmental monitoring, healthcare, and industrial automation. Traditionally, WSNs have relied on static routing protocols that lack adaptability to changing network conditions. This rigidity often results in congestion, energy inefficiency, and suboptimal overall network performance. Fixed routing paths frequently cause imbalanced load distribution, ultimately reducing the lifespan and efficiency of sensor nodes. A key limitation of conventional WSNs is their inability to accommodate dynamic network changes, including node failures, energy depletion, or environmental disruptions. These challenges lead to inefficient routing, redundant data retransmissions, and increased energy consumption. Moreover, traditional WSN architectures generally exhibit poor scalability, making it difficult to maintain consistent performance as the network size increases. Single-path routing, commonly used in these systems, further exacerbates the risk of data loss in the event of path failure or congestion. This research addresses the need for a more adaptive and efficient routing mechanism capable of responding to dynamic network conditions while ensuring load balancing and fault tolerance. The primary motivation is to enhance the reliability, energy efficiency, and scalability of WSNs, especially in large-scale deployments where conventional routing techniques fall short. To tackle these challenges, the proposed system integrates Software-Defined Networking (SDN) with WSNs. SDN introduces centralized control and real-time routing adaptation, enabling dynamic load balancing and multipath routing. This integration allows for more flexible and efficient traffic management, improved fault tolerance, and optimal resource utilization. By dynamically adjusting routes and distributing the network load, the system aims to overcome the limitations of traditional WSNs and deliver robust performance across a wide range of applications.

Currently, using commercial off-the-shelf (COTS) components and constructing parallel system becomes the main way to improve satellite system performance remarkably. System reliability relying on its structure rather than devices makes system fault-tolerant architecture and strategy increasingly important. As centralized fault-tolerant solution has the inherent drawback of single point of failure (SPOF), it can not guarantee system reliability well. Distributed fault- tolerant method, due to its complexity and large overhead, has not been maturely used. In this paper, we propose a new distributed fault-tolerant scheme with high reliability and flexibility, in which an equal symmetric distributed fault- tolerant (ESDFt) architecture is constructed. Based on the structure, functional module framework of system nodes and autonomous fault-tolerant strategy are designed. The ESDFt scheme creates favorable conditions for flexible implementation of system fault tolerance. It utilizes the master election mechanism, which eliminates the risks of SPOF and chooses master node intelligently based on user's configuration, making fault-tolerant complexity and processing overhead flexibly adjustable. The verification and evaluation of ESDFt scheme has been performed on a prototype system. The experiment results prove that ESDFt scheme not only ensures system reliability well, but also enhances system flexibility and practicality at the same time.

The concept of Power Electronic Building Blocks (PEBBs) has its origin in the U.S. Navy during the last decade of the past century. As compared to a more conventional or classical design approach, a PEBB-oriented design approach combines various potential advantages such as increased modularity, high availability and simplified serviceability. This relatively new design paradigm for power conversion has progressively matured since then and its underlying philosophy has been clearly and successfully demonstrated in a number of real-world applications. Therefore, this approach has been adopted here to design a Marx-topology modulator for an International Linear Collider (ILC) environment where easy serviceability and high availability are crucial. This paper describes various aspects relating to the design of a 32-cell Marx-topology ILC klystron modulator. The concept of nested droop correction is introduced and illustrated. Several design considerations including cosmic ray withstand, power cycling capability, fault tolerance, etc., are discussed. Details of the design of a Marx cell PEBB are included. I.

Fault tolerance and reliable transmission are hot issues in wireless sensor networks. For the problems that the fault of nodes or links will affect the transmission stability and reliability of the network, a gradient based multipath reliable transmission strategy with fault tolerance is presented for wireless sensor networks. Firstly nonuniform clustering topology is established in nonequal clustering probability based on twice K-means algorithm. Then it calculates the comprehensive measurements (CM) of cluster heads by the quality evaluation function and establishes the contour lines. Finally gradient based multiple disjoint transmission paths are established by the mechanisms of load balance and linear erasure coding. It also establishes the transmission mathematical model to analyze the performance of the network. Simulation shows that this strategy has good performance of fault tolerance. It improves the transmission reliability of the network and energy efficiency.

BIM2RT: BWAS-immune mechanism based multipath reliable transmission with fault tolerance in wireless sensor networks

Considering the Internet of Things (IoT) impact in today’s world, uninterrupted service is essential, and recovery has received more attention than ever before. Fault-tolerance (FT) is an essential aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in systems. The advent of software-defined networking (SDN) in the IoT plays a significant role in providing a reliable communication platform. This paper proposes a data plane fault-tolerant architecture using the concepts of software-defined networks for IoT environments. In this work, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the primary and backup non-overlapping paths between network equipment. In addition to the fault tolerance, service quality was considered in the proposed schemes. Putting the percentage of link bandwidth usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths with the best condition. We compare our two proposed dynamic schemes with the hybrid disjoint paths (Hybrid_DP) method and our previous work. IoT developments, wireless and wired equipment are now used in many industrial and commercial applications, so the proposed hybrid dynamic method supports both wired and wireless devices; furthermore multiple link failures will be supported in the two proposed dynamic schemes. Simulation results indicate that, while reducing the error recovery time, the two proposed dynamic designs lead to improved service quality parameters such as packet loss and delay compared to the Hybrid_DP method. The results show that in case of a link failure in the network, the proposed hybrid dynamic scheme’s recovery time is approximately 12 ms. Furthermore, in the proposed hybrid dynamic scheme, on average, the recovery time, the packet loss, and the delay improved by 22.39%, 8.2%, 5.66%, compared to the Hybrid_DP method, respectively.

In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric data center network topology can ensure the scale of the data center network by recursively increasing the number of network nodes and links. relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric data center network topology, which has more advantages in scalability, fault tolerance and so on. Multicast and broadcast in data center network have more application scenarios and use value. For example, the video conference online, multimedia remote education and other development are inseparable from the application and promotion of network multicast and broadcast. So it is necessary to research the routing algorithms of multicast and broadcast in the network. Based on the deep research of PTNet network, this paper analyzes and researches the network topology, multicast and broadcast routing algorithm.

Wireless sensor networks are a new emerging area where swarm intelligence can be applied with interesting implications. In fact, a strong analogy between unicellular organism colonies and wireless sensor networks can be emphasized: a sensor network can be viewed as a “colony” of simple, scarce resource nodes that, autonomously, are able only to perform simple tasks, but all together can accomplish very complex problems. In this paper we propose a routing protocol for multisink Wireless Sensor Networks with interesting properties: self organization, fault tolerance and environmental adaptation. The proposed protocol was inspired by the well known behavior (in artificial life studies) of “Slime Mold”. Such colony of unicellular organisms organizes itself in clusters by pheromone generation and evaporation mechanisms. In a similar manner our protocol manages the data traffic towards multiple sink nodes using the gradient concept and reaching high levels of autonomy and fault tolerance. We analyze the proposed protocol to examine the performances, the signalling overhead and the adaptation properties to environmental changes or nodes faults using simulation techniques.

The major concern in a Controller Area Network based safety critical system are node failures, its early detection and failure tolerance. Aiming at the improvement of node fault detection timing response and reliability of a CAN based distributed system, this paper mainly focuses on a new fault detection and tolerance algorithm named Distributed Node Fault Detection and Tolerance [DNFDT] Algorithm for Controller Area Network. The purpose of the algorithm is to have an effective fault detection method for (n-1) node faults for n node system by reducing the fault detection cycle timings and by limited checking on fault free nodes. A four node experimental hardware platform was implemented and performance evaluation of the time elapsed for one complete fault detection cycle under varying CAN bus loads have been obtained.

Compressionless Routing (GR) is a new adaptive routing framework which provides a unified framework for efficient deadlock-free adaptive routing and fault-tolerance. CR exploits the tight-coupling between wormhole routers for flow control to detect potential deadlock situations and recover from them. Fault-tolerant Compressionless Routing (FCR) extends Compressionless Routing to support end-to-end fault-tolerant delivery. Detailed routing algorithms, implementation complexity and performance simulation results for CR and FCR are presented.CR has the following advantages: deadlock-free adaptive routing in torus networks with no virtual channels, simple router designs, order-preserving message transmission, applicability to a wide variety of network topologies, and elimination of the need for buffer allocation messages. FCR has the following advantages: tolerates transient faults while maintaining data integrity (nonstop fault-tolerance), tolerates permanent faults, can be applied to a wide variety of network topologies, and eliminates the need for software buffering and retry for reliability. These advantages of CR and FCR not only simplify hardware support for adaptive routing and fault-tolerance, they also can simplify communication software layers.

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme), fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme). These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

In environments that are remote and hazardous, tasks such as repairing and maintaining a robot manipulator can be extremely difficult and costly. Under such conditions, the operational reliability of a robotic system is very important and motivates the problem of designing optimally fault-tolerant manipulators. One solution to increasing the fault tolerance of a robotic system is to use kinematically redundant manipulators, as their extra degree of redundancy could be used to compensate for the failure in joint actuators. However, simply adding kinematic redundancy does not guarantee fault tolerance, rather it should be strategically added to ensure optimally fault-tolerant system. In this article the authors design nominal manipulator Jacobians that are optimally fault-tolerant to multiple joint failures. The special case when the manipulator is equally fault-tolerant to any two simultaneous joint failures is considered. While it is possible to design a six degree-of-freedom (DOF) robot manipulator to be equally fault-tolerant to any two simultaneous joint failures for three-dimensional workspaces, it is shown that no manipulator operating in a 4 or 5 dimensional workspace can be equally fault-tolerant to two failures.