BRA2Cloud ??? A Brand new Resilient Agent-based Architecture to Cloud Computing

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.

Sun, J., 2020. Fault tolerance mechanism of distributed system for marine communication network. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 605–608. Coconut Creek (Florida), ISSN 0749-0208.When the communication network fails or the signal is interrupted, the distributed fault-tolerant system can continue to complete the communication task, which is needed to improve the fault-tolerant performance of the distributed system. Therefore, the key technologies of fault tolerance need to be studied, including the distributed fault-tolerant communication network simulation platform, the communication-network information-storage data structure, the communication protocol between communication network nodes, and the fault-tolerant algorithm of distributed communication network system. In the marine environment, the fleet exchanges data through wireless networks to understand the situation of the fleet-detection area, which can share the processing and calculation tasks. However, the marine environment is complex and changeable, which can cause great interference to the wireless signal, such as sea and cloud-rain clutter. These marine environments will cause the network communication signals to be greatly disturbed, which makes the distributed system unable to guarantee the communication interconnection between each node at any time. Therefore, a data-sharing and distributed-system platform needs to be built, which will design a reasonable system fault-tolerance mechanism. Through the fault-tolerance mechanism of the communication network system, the problem of frequent-node exits can be solved. By optimizing the fault-tolerant strategy of distributed architecture, the system's disaster resistance can be improved. In this research, a fault-tolerance mechanism for a distributed system was designed, which is more suitable for node election.

High availability in cloud computing services is essential for maintaining customer confidence and avoiding revenue losses due to SLA violation penalties. Since the software and hardware components of cloud infrastructures may have limited reliability, fault tolerance mechanisms are a means of achieving the necessary dependability requirements. This paper investigates the benefits of a warm-standy replication mechanism in a Eucalyptus cloud computing environment. A hierarchical heterogeneous modeling approach is used to represent a redundant architecture and compare its availability to that of a non-redundant architecture. Both hardware and software failures are considered in the proposed analytical models. The results show an enhanced dependability for the proposed redundant system, as well as a decrease in the annual downtime. The results also demonstrate that the simple replacement of hardware by more reliable machines would not produce improvements in system availability to the same extent as would the fault tolerant approach.

With the growing scale of high performance computing platforms and the increasing scientific computing demands, fault tolerance in MPI programs has become a major issue. This paper proposed an analytical performance model to help check pointing mechanisms to get better performance. In this paper, after the introduction of different fault tolerant mechanisms, we focus on the segment-level and variable-level check pointing approaches and analyze how the fault tolerant mechanisms affect the performance of current MPI systems, then we introduced the analytical performance model we proposed. Last we evaluated and analyzed the performance of two classic fault tolerant approaches and propose what should be done to advance variable-level check pointing mechanisms.

Fault tolerance engineering has been advocated as one of the main approaches to ensuring the overall system dependability. The 2nd International Workshop on Engineering Fault Tolerant Systems (EFTS 2007) aims to investigate how fault tolerance mechanisms can be taken into account when engineering complex software systems and to improve our understanding of where and how fault-tolerance should be integrated in the software life-cycle. The focus of the workshop is on developing novel models to be applied at different abstraction levels (requirements, architecture and design models for fault tolerance, together with new implementation schemes), innovative technologies (tools and frameworks for implementing distributed fault tolerant systems) and advanced verification environments (to assess the achieved level of fault tolerance and to evaluate the dependability properties of the systems). Recently there has been a growing interest in the areas directly related and overlapping with fault tolerance, such as self-healing, resilience, self-adaptation and self-management. The topics related to engineering of systems with such properties are in the scope of the workshop as the intention is to improve the current understanding of how fault tolerance engineering can benefit from research on these areas.

Rapid growth of mobile usage and on demand applications not only requires huge performance enhancements of cellular network but also requires fault tolerance, cost effectiveness at its access sub network level. This paper proposes cost optimization model along with fault tolerance for cellular access network. The first objective of the problem is minimization of link cost by optimal assignment of base station to base station controller in a cellular access network, incorporating connectivity constraint and capacity constraint. The fault tolerant mechanism is that, the access network will be able to communicate even in case of any network failure. The second objective aims at fault tolerant mechanism to surmount transmission level link failure by using link redundant multi homing strategy. The mathematical mixed integer programming model has been used to formulate the problem. The fault tolerant cost optimization is a NP hard problem. Therefore simple and efficient heuristic algorithm targeting cost optimization along with fault tolerance has been proposed. The result achieves an average residual capacity of ~26% with back up link assignment. Further, load allocation during multi link failure is attained within minimum gap of 1%. The computation time almost remains same for single and multiple link failure. The algorithm performs well in both small and lager access network.

This article examines the functioning of sensor networks as a key component of the Internet of Things (IoT) technology, which facilitates integration between the physical and digital worlds. It analyzes the challenges sensor networks face, including limited resources, node failures, scalability, and security threats. The main components of sensor networks are discussed: sensors, smart elements, gateways, and communication modules that enable data collection and transmission for further analysis. Attention is drawn to the fact that sensor networks are often targets of malicious attacks, such as DDoS, Sinkhole, and routing attacks, which necessitate the development of new protection methods. The paper thoroughly examines threats from illegitimate elements in sensor networks that can disrupt network operations, cause data leaks, and affect network resilience and fault tolerance by exhausting resources. To effectively detect such nodes, it is proposed to apply mechanisms for traffic analysis, energy consumption monitoring, and encryption. The nature of failures in sensor networks and the interrelationship between fault tolerance and security are explored. Probabilities of failure for networks of different sizes are calculated, and mechanisms for improving fault tolerance, including node redundancy, self-healing algorithms, and fault tolerance mechanisms, are proposed. A methodology for detecting malicious nodes based on traffic and energy characteristics analysis is suggested. It was found that nodes exceeding threshold values for the number of transmitted packets or energy consumption may be malicious. It is noted that combining various methods will improve the accuracy of detecting malicious nodes at early stages, significantly enhancing the level of information security in sensor networks. Prospects for further research into the development of new protective mechanisms and improvements in the fault tolerance of sensor networks are highlighted.

: This report provides a synopsis of research performed in fault- tolerant computing, for the first year of grant AFOSR-84-0052. Also included is a list of publications that have resulted from the research supported by this grant. Additionally, this report reviews the future direction for the continuing research under this grant. In the past year, this effort has focussed on the following problems: (1) Investigation of novel fault-tolerant processor array architectures with the potential of a high degree of defect tolerance, but having low processor and interconnect overhead associated with the fault tolerance mechanisms; (2) Development of realistic models to evaluate the yield, redundancy and performance tradeoffs for the designs. Such models would help establish the viability of these architectures, also enabling them to be compared with other designs in the literature; (3) Development of new and efficient testing strategies, and reconfiguration schemes for their structures; (4) Testable design of large size VLSI memory; and (5) Development of novel sorting networks that can be implemented on a single chip or wafer. Three journal articles were supported during the grant period: 'Synthesis of Directed Multicommodity Flow Networks,' Vol. 14, pp. 213-224 (with A. Stai); 'Fault- Tolerant Multiprocessor Link and Bus Network Architectures, IEEE Transactions on Computers, Vol. C-34, No. 1, January 1985, pp. 33-46; and 'Dynamically Restructurable Fault-Tolerant Processor Network Architectures,' IEEE transactions on Computers (to appear).

We describe a novel approach for software-implemented fault tolerance that separates error detection from error recovery and offers a distinct programming and processing context for the latter. This allows the application developer to address separately the non-functional aspects of error recovery from those pertaining to the functional behaviour that the user application is supposed to have in the absence of faults. We conjecture that this way only a limited amount of non-functional code intrusion affects the user application, while the bulk of the strategy to cope with errors is to be expressed by the user in a "recovery script", conceptually as well physically distinct from the functional application layer. Such script is to be written in what we call a "recovery language", i.e. a specialised linguistic framework devoted to the management of the fault tolerance strategies that allows to express scenarios of isolation, reconfiguration, and recovery. These are to be executed on meta-entities of the application with physical or logical counterparts (processing nodes, tasks, or user-defined groups of tasks). The developer is therefore made able to modify the fault tolerance strategy with only a few or no modifications in the application part, or vice-versa, tackling more easily and effectively any of these two fronts. This can result in a better maintainability of the target fault-tolerant application and in support for reaching portability of the service while moving the application to different unfavourable environments. The paper positions and discusses the recovery language approach and a prototypal implementation for embedded applications developed within project TIRAN on a number of distributed platforms.

Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.

This paper presents a new approach to provide fault tolerance in MPLS multicast networks. MPLS has been used to provide faster forwarding; combining with multicasting it further supports applications with improved service. Fault tolerance is therefore important for such networks. Our approach for fault tolerance is to divide and encode the traffic into multiple disjoint trees using a modified (k, n) Threshold Sharing scheme. Our approach can reconstruct the original traffic from any k out of n trees available. Therefore, in the event of node/link failure(s) of any (n-k) trees, our approach provides fault tolerance without introducing any recovery delay and packet loss. Moreover, our objective is also to minimize bandwidth utilization for protection purposes.

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

Fault tolerance, performance, and throughput have been major areas of research and development since the evolution of large-scale networks. Internet-based applications are rapidly growing, including large-scale computations, search engines, high-definition video streaming, e-commerce, and video on demand. In recent years, energy efficiency and fault tolerance have gained significant importance in data center networks and various studies directed the attention towards green computing. Data centers consume a huge amount of energy and various architectures and techniques have been proposed to improve the energy efficiency of data centers. However, there is a tradeoff between energy efficiency and fault tolerance. The objective of this study is to highlight a better tradeoff between the two extremes: (a) high energy efficiency and (b) ensuring high availability through fault tolerance and redundancy. The main objective of the proposed Energy-Aware Fault-Tolerant (EAFT) approach is to keep one level of redundancy for fault tolerance while scheduling resources for energy efficiency. The resultant energy-efficient data center network provides availability as well as fault tolerance at reduced operating cost. The main contributions of this article are: (a) we propose an Energy-Aware Fault-Tolerant (EAFT) data center network scheduler; (b) we compare EAFT with energy efficient resource scheduling techniques to provide analysis of parameters such as, workload distribution, average task per servers, and energy consumption; and (c) we highlight effects of energy efficiency techniques on the network performance of the data center.

This article is the second part of a two-part tutorial article on the design of fault-tolerant digital systems. The first part, published in November, presented the general principles of fault tolerance and discussed the use of redundancy to increase system reliability and availability. The article also commented on the design of self-testing logic circuits. In this second part, we concentrate more on what has been achieved in practice, and the article describes the JPL-Star computer. This is an experimental computer designed to evaluate many of the strategies for enhancing fault-tolerance, and it is already obsolete. This article concludes, therefore, with a brief survey of other fault-tolerant proposals and implementations

Purpose The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system. Design/methodology/approach In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft. Findings The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied. Practical implications For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.