Fault-tolerant Policy Research Articles

Adaptive hybrid arbiter design for real-time traffic-aware scheduling

PurposeThis work focused on a basic building block of an allocation unit that carries out the critical job of deciding between the conflicting requests, i.e. an arbiter unit. The purpose of this work is to implement an improved hybrid arbiter while harnessing the basic advantages of a matrix arbiter.Design/methodology/approachThe basic approach of the design methodology involves the extraction of traffic information from buffer signals of each port. As the traffic arrives in the buffer of respective ports, information from these buffers acts as a source of differentiation between the ports receiving low traffic rates and ports receiving high traffic rates. A logic circuit is devised that enables an arbiter to dynamically assign priorities to different ports based on the information from buffers. For implementation and verification of the proposed design, a two-stage approach was used. Stage I comprises comparing the proposed arbiter with other arbiters in the literature using Vivado integrated design environment platform. Stage II demonstrates the implementation of the proposed design in Cadence design environment for application-specific integrated chip level implementation. By using such a strategy, this study aims to have a special focus on the feasibility of the design for very large-scale integration implementation.FindingsAccording to the simulation results, the proposed hybrid arbiter maintains the advantage of a basic matrix arbiter and also possesses the additional feature of fault-tolerant traffic awareness. These features for a hybrid arbiter are achieved with a 19% increase in throughput, a 1.5% decrease in delay and a 19% area increase in comparison to a conventional matrix arbiter.Originality/valueThis paper proposes a traffic-aware mechanism that increases the throughput of an arbiter unit with some area trade-off. The key feature of this hybrid arbiter is that it can assign priorities to the requesting ports based upon the real-time traffic requirements of each port. As a result of this, the arbiter is dynamically able to make arbitration decisions. Now because buffer information is valuable in winning the priority, the presence of a fault-tolerant policy ensures that none of the priority is assigned falsely to a requesting port. By this, wastage of arbitration cycles is avoided and an increase in throughput is also achieved.

A Practical Fault Tolerance Approach in Cloud Computing Using Support Vector Machine

Fault tolerance is an important issue in the field of cloud computing which is concerned with the techniques or mechanism needed to enable a system to tolerate the faults that may encounter during its functioning. Fault tolerance policy can be categorized into three categories viz. proactive, reactive and adaptive. Providing a systematic solution the loss can be minimized and guarantee the availability and reliability of the critical services. The purpose and scope of this study is to recommend Support Vector Machine, a supervised machine learning algorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strength of machine learning algorithm with cloud computing.

A practical fault-tolerance approach in cloud computingusing support vector machine

Fault tolerance is an important issue in the field of cloud computing which, is concerned with the techniques ormechanisms needed to enable a system to tolerate the faults that it may encounter during its functioning. Faulttolerance policies can be categorized into three categories, viz., proactive, reactive and adaptive. By providing asystematic solution, the loss can be minimized and to ensure the availability and reliability of the critical services.The purpose and scope of this study is to recommend support vector machine, a supervised machine learningalgorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strengthof machine learning algorithms with cloud computing.

Proactive Fault Tolerance Policy for Virtual Machine Management in Private Cloud Environment

Cloud computing being a new delivery model has many research challenges. It opens the door to great amount of innovative methods and practices. Many researchers have come-up with novel and efficient fault tolerant solutions for the cloud. With the rapid advancement in internet technology, usage of smart devices and social networking are giving rise to tremendous amount of data and demands resources like never before. Cloud computing delivery model provides solution to the need of the day. The recent advancement in IoT, Edge computing etc, has expanded the scope of visualizing cloud computing with a different perspective. There is a need for re-provisioning the existing components of the cloud model. Most of the research on fault tolerant algorithms, mechanisms and techniques are focused on datacenters. In this paper we propose fault detection and prevention policies for VM creation lifecycle derived from cloud computing patterns.

Fault tolerant tracking control for a class of linear parameter varying systems using reduced-order simultaneous estimator and optimal preview policy

This paper considers the reduced-order estimator based fault tolerant preview tracking control problem for a class of linear parameter varying (LPV) systems. The main research contents are composed of two aspects. First, a scheduling-parameter-dependent reduced-order simultaneous state/fault estimator is designed based on model transformation method, unknown input observer theory, estimator parameterisation, and robustness optimisation. Second, a novel robust fault tolerant preview tracking control policy is constructed by integrating fault signal compensation, state feedback, preview action of reference signals, and integral regulation of tracking errors. Compared with the relevant studies, the merits can be summarised in two aspects. On one hand, the proposed design method of reduced-order estimator can be applied to more general LPV models because it does not depend on the traditional constraint operations, such as decomposition of system output equation, separated estimations of actuator and sensor faults, or derivative of output equation. On the other hand, the optimal preview policy including preview action and integral regulation is introduced to increase the design degree of freedom, which contributes to the optimisation of fault tolerant tracking performance even under influences of fault estimation errors and disturbances. Finally, the effectiveness of the proposed method is verified by three case studies.

Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems With Actuator Faults via Integral Reinforcement Learning Method

In this paper, a fuzzy reinforcement learning (RL)-based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi–Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the RL technique and fuzzy-augmented model, the new fuzzy integral RL-based fault-tolerant control algorithm is designed, and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proven by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.

Task-Level Re-Execution Framework for Improving Fault Tolerance on Symmetry Multiprocessors

Hard real-time systems are employed in military, aeronautics, and astronautics fields where deployed systems are susceptible to software faults that can result in functional errors. Thus, there is a need to use fault-tolerant (FT) real-time scheduling. Among the various fault-tolerant real-time scheduling techniques, re-execution has been applied widely to existing real-time systems owing to its simplicity and applicability. However, re-execution requires multiple executions of every task, and some tasks miss their deadlines owing to the prolonged execution time; therefore, it has been found to be suitable for only soft real-time systems. In this paper, we propose an FT policy that can be incorporated into most (if not all) existing real-time scheduling algorithms on multiprocessor systems, which improves the reliability of the target system without a tradeoff against schedulability. As a case study, we apply the FT policy to existing fixed-priority scheduling and earliest deadline zero-laxity scheduling, and we demonstrate that it enhances reliability without schedulability loss.

Dynamic surface fault tolerant control for underwater remotely operated vehicles

In this paper, we present a two stages actuator Fault Tolerant Control (FTC) strategy for the trajectory tracking of a Remotely Operated Vehicle (ROV). Dynamic Surface Control (DSC) is used to generate the moment and forces required by the vehicle to perform the desired motion. In the second stage of the control system, a fault tolerant thruster allocation policy is employed to distribute moment and forces among the thrusters. Exhaustive simulations have been carried out in order to compare the performance of the proposed solution with respect to different control techniques (i.e., PID, backstepping and sliding mode approaches). Saturations, actuator dynamics, sensor noises and time discretization are considered, in fault-free and faulty conditions. Furthermore, in order to provide a fair and exhaustive comparison of the control techniques, the same meta-heuristic approach, namely Artificial Bee Colony algorithm (ABC), has been employed to tune the controllers parameters.

Task mapping and scheduling for network-on-chip based multi-core platform with transient faults

Technology scaling has enabled the integration of large number of transistors into a single chip, leading to performance enhancement via incorporation of Processing Elements (PEs), Intellectual Property (IP) cores and Memory Units together on the same platform. On the downside, it has led on-chip components to be more susceptible to faults, both permanent and transient. Permanent faults are predictable in nature and can be dealt with at the time of manufacturing or in field using spares/redundancy. Transient faults also adversely affect the application performance but are unpredictable in nature. Handling transient faults is a challenging task, especially in a real-time system where different applications are executed with various timing constraints. Although significant amount of work has been reported in literature for transient fault management, it lacks addressing the temporal constraint satisfaction of the tasks while restricting the energy expenditure of the system. Existing fault tolerant policies do task replication to ensure higher percentage of deadline satisfaction but at the cost of higher energy consumption. Checkpointing approach can make energy consumption low, however, the number of tasks satisfying their timing constraint also becomes low. Thus a fault tolerant policy which could jointly address the timing and energy constraint in a real time system is desirable. This work proposes an algorithm to intelligently perform a fault-tolerant resource allocation in real-time dynamic scenarios where tasks of applications are not known apriori. The slack times of the incoming tasks have been exploited in the application mapping/scheduling phase of the algorithm, to assign a fault tolerant policy to the corresponding task for mitigating the effect of transient faults. This helps to improve the deadline satisfaction of the task and also reduce the energy consumption. While comparing with existing works, the proposed algorithm achieves 19.8%, 43.5% and 85.8% improvement in deadline satisfaction compared to MXR [1], CPR [2] and TR [3], respectively. On an average, the energy consumption is reduced by 29.1% and 6.7%, compared to AR [4] and MXR [1].

Augmentation Techniques for Mobile Cloud Computing

Despite the rapid growth of hardware capacity and popularity in mobile devices, limited resources in battery and processing capacity still lack the ability to meet increasing mobile users’ demands. Both conventional techniques and emerging approaches are brought together to fill this gap between user demand and mobile devices’ limited capabilities. Recent research has focused on enhancing the performance of mobile devices via augmentation techniques. Augmentation techniques for mobile cloud computing refer to the computing paradigms and solutions to outsource mobile device computation and storage to more powerful computing resources in order to enhance a mobile device’s computing capability and energy efficiency (e.g., code offloading). Adopting augmentation techniques in the heterogeneous and intermittent mobile cloud computing environment creates new challenges for computation management, energy efficiency, and system reliability. In this article, we aim to provide a comprehensive taxonomy and survey of the existing techniques and frameworks for mobile cloud augmentation regarding both computation and storage. Different from the existing taxonomies in this field, we focus on the techniques aspect, following the idea of realizing a complete mobile cloud computing system. The objective of this survey is to provide a guide on what available augmentation techniques can be adopted in mobile cloud computing systems as well as supporting mechanisms such as decision-making and fault tolerance policies for realizing reliable mobile cloud services. We also present a discussion on the open challenges and future research directions in this field.

Fault-Tolerant Rendezvous of Multirobot Systems

In this paper, we propose a distributed control policy to achieve rendezvous by a set of robots even when some robots in the system do not follow the prescribed policy. These nonconforming robots correspond to faults in the multirobot system, and our control policy is thus a fault-tolerant policy. Each robot has a limited sensing range and is able to directly estimate the state of only those robots within that sensing range, which induces a network topology for the multirobot system. We assume that it is not possible for the fault-free robots to identify the faulty robots, and thus our approach is robust even to undetected faults in the system. The main contribution of this paper is a fault-tolerant distributed control algorithm that is guaranteed to converge to consensus under certain reasonable connectivity conditions. We first present a general algorithm that exploits the notion of a Tverberg partition of a point set in $\mathbb {R}^d$ , and give a proof of convergence. We then provide three instantiations of this algorithm, based on three different sensing models. For each case, we analyze performance via extensive simulations. The effectiveness and performance of our algorithms on real platforms are demonstrated through experiments on a multirobot testbed.

Dynamic fault tolerant scheduling policy for workflows in Grid computing

Dynamic fault tolerant scheduling policy for workflows in Grid computing

A new fault-tolerant architecture based on DASH for adaptive streaming video

The standard dynamic adaptive streaming over HTTP (DASH) has been proposed to guarantee a quality of video under varying bandwidth. DASH enables adaptation of the media bit-rate to varying throughput conditions by offering multiple representations of the same content. However, the Moving Picture Experts Group (MPEG) DASH specification only defines the media presentation description (MPD) and the segment formats. In order to take advantages of DASH, we propose a fairness architecture, called dynamic adaptive streaming over HTTP distributed multimedia systems (DASH-DMS). DASH-DMS combines both 2-tiers and 3-tiers architectures in order to guarantee a quality of service (QoS) under changing conditions. Moreover, we propose replication algorithms to enable load balancing video servers and improve the global QoS. Furthermore, in order to improve the availability of our system, we use a fault tolerance policy. To this purpose, we have conduct simulations throughout the paper using the simulator developed by our team.

Hybrid Fault Tolerant Scheme to Manage VM Failure in the Cloud

Objectives: To managing the Virtual Machine and improve the reliability and availability of the resources using Hybrid fault tolerance method. Methods/Statistical Analysis: Basically two main fault tolerant policies namely reactive fault tolerance and proactive fault tolerance. Reactive fault tolerant techniques attempts to reduce the effort required for recovery after a failure, whereas a proactive fault tolerant technique attempts to prevent the occurrence of a fault. The existing method aims to prevent the occurrence of VM failure using a proactive fault tolerant technique which is a static method. But it provides no solution for VM failure, which might occur while the application is still running. The proposed paper ensures reliability of VM by considering both the reactive and proactive techniques in hybrid ways of tolerating the VM failure. In the proposed system, two types of VM failure, insufficient storage and variable capacity change are detected. These failures are tolerated using a self healing mechanism implemented at the host level. Result: The proposed scheme is implemented using Cloudsim simulator, in order to highlight the performance of how VM can be managed. Applications: Virtual Machine failure can be managed and improve the reliability and availability of resources using hybrid fault tolerance scheme.

A Fault-Tolerant Cache System of Automotive Vision Processor Complying With ISO26262

With fluctuating voltage, widening operating temperature, and increasing clock frequency, cache systems are becoming increasingly susceptible to transient errors. Error correction code (ECC) is an attractive approach for transient error detection and correction. However, redundant memory for ECC has a significant impact on cost and increases the transient error rates. This brief presents a fault-tolerant cache system of automotive vision processors. The cache system has the small redundant memory and decreases the transient error rates with the proposed mechanism, which increases the error recovery rate and is considered with data cache characteristics. Also, the cache system is analyzed complying with ISO26262. As a result, thanks to 80% reduction of error traps caused by the cache system at the automotive vision processor’s variable fault-tolerant policy, compared to the cache system of the state-of-art vision processors for automotive.

분산 파일시스템의 소거 코딩 구현 및 성능 비교

최근의 빅데이터, 머신러닝, 클라우드 컴퓨팅 분야의 성장에 따라 대용량의 비정형 데이터를 저장할 수 있는 스토리지의 중요성은 날로 커지고 있다. 이에 따라 MAHA-FS, GlusterFS, Ceph 등의 개방형 하드웨어 기반의 분산 파일시스템 기술이 많은 주목을 받고 있다. 이러한 저비용 분산 파일시스템들은 데이터의 내결함성을 보장하기 위하여 초기에 복제 방식을 사용하였으나, 스토리지의 용량이 커질수록 복제 방식이 가지는 스토리지 공간의 저효율성이 점차 부각되면서 이를 보완하려는 방향으로 연구가 진행되고 있다. 본 논문은 복제방식을 대체하여 스토리지 공간 효율성을 향상시킬 수 있는 소거코딩 기법을 MAHA-FS 분산 파일시스템에 적용하여 스토리지의 효율성을 높이고, 소거코딩 지원에 따라 발생하는 데이터 일관성 문제를 해결하는 효율적인 방식으로 VDelta 기법을 제안하고 적용하였다. 본 논문은 MAHA-FS와 GlusterFS의 소거코딩의 구조적 차이점을 기술하고 두 파일시스템의 성능을 비교하여 MAHA-FS의 소거코딩 성능이 GlusterFS에 비해 우수함을 확인하였다. With the growth of big data, machine learning, and cloud computing, the importance of storage that can store large amounts of unstructured data is growing recently. So the commodity hardware based distributed file systems such as MAHA-FS, GlusterFS, and Ceph file system have received a lot of attention because of their scale-out and low-cost property. For the data fault tolerance, most of these file systems uses replication in the beginning. But as storage size is growing to tens or hundreds of petabytes, the low space efficiency of the replication has been considered as a problem. This paper applied erasure coding data fault tolerance policy to MAHA-FS for high space efficiency and introduces VDelta technique to solve data consistency problem. In this paper, we compares the performance of two file systems, MAHA-FS and GlusterFS. They have different IO processing architecture, the former is server centric and the latter is client centric architecture. We found the erasure coding performance of MAHA-FS is better than GlusterFS.

Input-output based fault estimation for linear systems with partially dynamic uncertainty and actuator faults

Summary This paper is concentrated on the problem of fault estimation for a class of linear systems with partially dynamic uncertainty and actuator faults. A novel input–output-based fault estimation approach is proposed, by which the estimates can asymptotically converge to the magnitudes of the actuator faults, and the asymptotic convergence of the estimation is theoretically proved. Some important properties related to the corresponding fault errors are obtained. The proposed input–output-based fault estimation method can exponentially weaken the effects of the fault derivatives on the fault error dynamics. Based on the online estimates, a corresponding robust fault-tolerant control policy is designed, so that the closed-loop system is asymptotically stable and the control output curves can asymptotically trace to the normal control output curves. Finally, three examples are given to show the effectiveness, merits, and applications of the proposed methods. Copyright © 2016 John Wiley & Sons, Ltd.

Fault Tolerance Policy in Cloud Computing Based on Ant Colony Optimization

Dorigo introduced Ant Colony Optimization in 1990s to solve various optimization problems by analyzing the complex behaviors of social insects Ants. Now days computer researcher and scientists are solving difficult optimization problems by proposing algorithms on ant behavior i.e. Ant Colony Optimization to find shortest paths from source to destination. Ant colonies present highly structured social organizations in distributed manner and studying the ant colonies behavior the difficult optimization and distributed control system problem in real world are solved. In this paper a new algorithm is proposed to analyze and tolerate the fault in cloud computing environment and it also represented when a cloud server is busy and unable to process a task then how to roll back the process and assign the next node by updating checkpoint.

An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach

In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations.

DFTS: A dynamic fault-tolerant scheduling for real-time tasks in multicore processors

This paper presents a dynamic scheduling for real-time tasks in multicore processors to tolerate single and multiple transient faults. The scheduling is performed based on three important issues: (1) current released tasks, (2) current available processor cores, and (3) consideration of the number of faults and their occurrences. Using tasks utilization along with a defined criticality threshold in the proposed scheduling method, current ready tasks are divided into critical- and noncritical ones. Based on whether a task is critical or noncritical, an appropriate fault-tolerance policy is exploited. Moreover, scheduling decisions are made to fulfill two key goals: (1) increasing scheduling feasibility and (2) decreasing the total tasks execution time. Several simulation experiments are carried out to compare the proposed method with two well-known methods, called checkpointing with rollback recovery and hardware replication. Experimental results reveal that in the presence of multiple transient faults, the feasibility rate of the proposed method is considerably higher than the other well-known fault-tolerance methods. Moreover, the average timing overhead of this method is lower than the traditional methods.