One of challenging issues for task allocation problem in wireless sensor networks (WSNs) is distributing sensing tasks rationally among sensor nodes to reduce overall power consumption and ensure these tasks finished before deadlines. In this paper, we propose a soft real-time fault-tolerant task allocation algorithm (FTAOA) for WSNs in using primary/backup (P/B) technique to support fault tolerance mechanism. In the proposed algorithm, the construction process of discrete particle swarm optimization (DPSO) is achieved through adopting a binary matrix encoding form, minimizing tasks execution time, saving node energy cost, balancing network load, and defining a fitness function for improving scheduling effectiveness and system reliability. Furthermore, FTAOA employs passive backup copies overlapping technology and is capable to determinate the mode of backup copies adaptively through scheduling primary copies as early as possible and backup copies as late as possible. To improve resource utilization, we allocate tasks to the nodes with high performance in terms of load, energy consumption, and failure ratio. Analysis and simulation results show the feasibility and effectiveness of FTAOA. FTAOA can strike a good balance between local solution and global exploration and achieve a satisfactory result within a short period of time.

A PSO-Optimized Real-Time Fault-Tolerant Task Allocation Algorithm in Wireless Sensor Networks

With the increasing complexity of industrial application, an embedded control system (ECS) requires processing a number of hard real-time tasks and needs fault-tolerance to assure high reliability. Considering the characteristics of real-time tasks in ECS, an integrated algorithm is proposed to schedule real-time tasks and to guarantee that all real-time tasks are completed before their deadlines even in the presence of faults. Based on the nonpreemptive critical-section protocol (NCSP), this paper analyzes the blocking time introduced by resource conflicts of relevancy tasks in fault-tolerant multiprocessor systems. An extended schedulability condition is presented to check the assignment feasibility of a given task to a processor. A primary/backup approach and on-line replacement of failed processors are used to tolerate processor failures. The analysis reveals that the integrated algorithm bounds the blocking time, requires limited overhead on the number of processors, and still assures good processor utilization. This is also demonstrated by simulation results. Both analysis and simulation show the effectiveness of the proposed algorithm in ECS.

Fault-Tolerant Scheduling for Real-Time Embedded Control Systems

Hadoop is a popular cloud computing software, and its major component MapReduce can efficiently complete parallel computing in homogeneous environment. But in practical application heterogeneous cluster is a common phenomenon. In this case, it's prone to unbalance load. To solve this problem, a model of heterogeneous Hadoop cluster based on dynamic load balancing is proposed in this paper. This model starts from MapReduce and tracks node information in real time by using its monitoring module. A maximum node hit rate priority algorithm (MNHRPA) is designed and implemented in the paper, and it can achieve load balancing by dynamic adjustment of data allocation based on nodes' computing power and load. The experimental results show that the algorithm can effectively reduce tasks' completion time and achieve load balancing of the cluster compared with Hadoop's default algorithm.

An Optimization Algorithm for Heterogeneous Hadoop Clusters Based on Dynamic Load Balancing

Nowadays, industry has successfully used Network Control Systems (NCS) therefore several lines of research have arisen. A NCS is a current application of a Real-Time Distributed Systems (RTDS), composed of a number of nodes capable of developing a complete control process. In these systems several nodes exchange information through a communication network to achieve specific control goals, nevertheless network traffic increases. This affects the overall system performance. Several approaches have been developed to satisfy requirements of both control and communication performance. Particularly, some methodologies focus on saving bandwidth, one of such methodologies is network scheduling. The objective of this methodology is the accurately use of the computing resources. NCS research is categorized into two main parts [5]: 1. Control of network: Study and research on communications and networks to make them suitable for real-time NCS, e.g. routing control, congestion reduction, efficient data communication, networking protocol. 2. Control over network: This area deals with control strategies and control systems design over the network trying to minimize the effect of adverse network parameters on NCS performance such as network delay. These systems have many challenges to maintain the the Quality of Service (QoS) and Quality of Control (QoC). In the networks, QoS is the idea that transmission rates, error rates, and other characteristics can be measured and improved. The QoS can be degraded due to congestion and interference.

/pdf/issues-on-communication-network-control-system-based-upon-150zlz7owf.pdf

Issues on Communication Network Control System Based Upon Scheduling Strategy Using Numerical Simulations

In hard real-time systems, faults happen every now and then. With respect to the criticality of this kind of system, the capability of fault tolerance is necessary. A fault tolerant scheduling algorithm is capable of guaranteeing timing requirements of tasks even when faults occur. A primary-backup based fault-tolerant global scheduling algorithm RRFTGS (resource reclaim fault tolerant global scheduling) is proposed in this paper. RRFTGS pushes the execution of backup as late as possible and reclaims the resource distributed to backups when it is no longer needed. By this method the additional cost for achieving fault tolerance is significantly reduced. Simulation result shows that, comparing to the scheduling algorithm which only use passive backups, RRFTGS needs much less processors to tolerate a single fault. RRFTGS also could be adopted when high utilization task exists.

/pdf/fault-tolerant-global-scheduling-for-multiprocessor-hard-38k3md5i7h.pdf

Fault Tolerant Global Scheduling for Multiprocessor Hard Real Time Systems

In the context of real-time fault-tolerant scheduling in Distributed Control systems, Primary-backup scheme plays an important role. A backup copy is always preferred to be executed as passive backup-copy whenever possible because it can take the advantages of backup copy de-allocation technique and overloading technique to improve schedulabilty. In this paper, we propose a novel Fault-tolerant Rate-Monotonic Best-fit algorithm (NRMBF) based on Distributed Control systems to enhance the schedulability. Unlike existing scheduling algorithms (FTRMBF or FTRMFF) that will allocate a new processor for backup copy when no existing processors can accommodate the current backup copy to be assigned. NRMBF uses a back tracing strategy that reassign the primary copy when the aforementioned situation occurs. As a result, NRMBF can use fewer processors to schedule a set of tasks without losing real-time and fault-tolerant capability of the system. Simulation results reveal that NRMBF significantly improves the schedulabilty over existing, comparable algorithms in literature.

A novel real-time fault-tolerant scheduling algorithm based on distributed control systems

While Deep Reinforcement Learning (DRL) has emerged as a prospective method to many tough tasks, it remains laborious to train DRL agents with a handful of data collection and high sample efficiency. In this paper, we present an Adaptive Fuzzy Reinforcement Learning framework (AFuRL) for accelerating the learning process by incorporating adaptive fuzzy logic rules, enabling DRL agents to improve the efficiency of exploring the state space. In AFuRL, the DRL agent first leverages prior fuzzy logic rules designed especially for the actor-critic framework to learn some near-optimal policies, then further improves these policies by automatically generating adaptive fuzzy rules from state-action pairs. Ultimately, the RL algorithm is applied to refine the rough policy obtained by a fuzzy controller. We demonstrate the validity of AFuRL in both discrete and continuous control tasks, where our method surpasses DRL algorithms by a substantial margin. The experiment results show that AFuRL can find superior policies in comparison with imitation-based and some prior knowledge-based approaches.

Accelerating Deep Reinforcement Learning Under the Guidance of Adaptive Fuzzy Logic Rules

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A novel real-time fault-tolerant scheduling algorithm based on distributed control systems

Accelerating Deep Reinforcement Learning Under the Guidance of Adaptive Fuzzy Logic Rules