A novel thruster fault detection (FD) strategy is investigated in this article for unmanned marine vehicles (UMVs) under external disturbances and aperiodic denial-of-service (DoS) attacks. To address the challenge of the inability to timely detect DoS attacks, the UMV under consideration and its corresponding filters are initially modeled under the framework of an asynchronous switched system. Then, sufficient conditions guaranteeing the system to be exponentially stable and with the prescribed performances are derived by leveraging the model-dependent average dwell time (MDADT) and piecewise Lyapunov functions (PLFs). Simultaneously, the lower bound of the tolerable sleep interval and the upper bound of the DoS attack interval are rigorously calculated. The design criteria of the FD filters are subsequently obtained through the employment of some decoupling techniques. Finally, the simulations on a UMV demonstrate the effectiveness of the proposed methods.

Many real-time systems have both performance requirements and reliability requirements. Performance is usually measured in terms of the value in completing tasks on time. Reliability is evaluated by hardware and software failure models. In many situations, there are trade-offs between task performance and task reliability. Thus, a mathematical assessment of performance-reliability trade-offs is necessary to evaluate the performance of real-time fault-tolerance systems. Assuming that the reliability of task execution is achieved through task replication, we present an approach that mathematically determines the replication factor for tasks. Our approach is novel in that it is a task schedule based analysis rather than a state based analysis as found in other models. Because we use a task schedule based analysis, we can provide a fast method to determine optimal redundancy levels, we are not limited to hardware reliability given by constant failure rate functions as in most other models, and we hypothesize that we can more naturally integrate with online real-time scheduling than when state based techniques are used. In this work, the goal is to maximize the total performance index, which is a performance-related reliability measurement. We present a technique based on a continuous task model and show how it very closely approximates discrete models and tasks with varying characteristics.>

The authors analyze the performance of a heuristic algorithm, H/sub k/, which tries to keep at least k processors busy, if possible. H/sub k/ combines the features of two known heuristic scheduling algorithms: list scheduling and the H scheduling algorithm. The authors analyze its schedule length bound for both uniform tasks, i.e. tasks with the same computation time, and nonuniform tasks, i.e. tasks with arbitrary computation times. When k=2, the time complexity of H is the same as the complexity of the H scheduling algorithm and list scheduling, which is O(n/sup 2/r), where n is the number of tasks and r is the number of resources. Whereas the H scheduling algorithm has a poor schedule length bound but performs very well in finding feasible schedules, and list scheduling does not perform well in finding feasible schedules but has a good bound, the results show that H/sub 2/ has both a good schedule length bound and performs well in finding feasible schedules.>

The authors study the performance of online algorithms in environments where no value is obtained for the partial execution of a request. They prove that no online scheduling algorithm can have a competitive factor greater than 0.25 times the optimal. They further refine this bound by considering the effect of the loading factor. Other models of task systems (for example, tasks systems consisting of many types of task requests), are considered. Similar upper bounds on the competitive factor that can be made by online scheduling algorithms in these environments are proved. It is shown that the performance bound of 0.25 is tight by means of a simple online uniprocessor scheduling algorithm has a competitive factor of 1/4. The authors extend the discussion to systems with dual processors. They show that the upper bound for the dual-processor online scheduling problem is 1/2 if all tasks have the same value density. This bound is tight if the tasks all also have zero laxity.>