We present a novel scheduling approach for servicing soft aperiodic requests in a hard real time environment, where a set of hard periodic tasks is scheduled using the Earliest Deadline First algorithm. The main characteristic of the proposed algorithm is that it achieves full processor utilization and optimal aperiodic responsiveness, still guaranteeing the execution of the periodic tasks. Another interesting feature of the proposed algorithm is that it can easily be tuned to balance performance versus complexity for adapting it to different application requirements. Schedulability issues, performance results, and implementation complexity of the algorithm are discussed and compared with other methods, such as Background, the Total Bandwidth Server, and the Slack Stealer. Resource reclaiming and extensions to more general cases are also considered. Extensive simulations show that a substantial improvement can be achieved with a little increase of complexity, ranging from the performance of the Total Bandwidth Server up to the optimal behavior.

In this paper we present a new scheduling approach for servicing soft aperiodic requests in a hard real-time environment, where a set of hard periodic tasks is scheduled using the Earliest Deadline First algorithm. The main characteristic of the proposed algorithm is that it achieves full processor utilization and optimal aperiodic responsiveness, still guaranteeing the execution of the periodic tasks. Another interesting feature of the algorithm is that it can easily be tuned to change its performance and complexity according to the application requirements. Schedulability issues, performance results, and implementation complexity of the algorithm are discussed and compared with other classical methods, such as Background, the Total Bandwidth Sewer (TBS), and the optimal EDL server. Extensive simulations show that a substantial improvement can be achieved with a little increase of complexity, ranging from the TBS performance up to the optimal behavior.

We present a comparative study among scheduling algorithms which use different priority assignments and different guarantee mechanisms to improve the performance of a real-time system during overload conditions. In order to enhance the quality of service, we assume that tasks are characterized not only by a deadline, but also by an importance value. The performance of the scheduling algorithm is then evaluated by computing the cumulative value gained on a task set, i.e. the sum of the values of those tasks that completed by their deadline. The purpose of this simulation study was twofold. Firstly, we wanted to discover which priority assignment is able to achieve the best performance in overload conditions. Secondly, we were interested in understanding how the pessimistic assumptions made in the guarantee test affect the performance of the scheduling algorithms, and how much a reclaiming mechanism can compensate this degradation. Simulation results show that, without any admission control, value-density scheduling performs best. Simple admission control based on worst case estimates of the load worsen the performance of all value based algorithms. EDF scheduling performs best if admission control is used along with a reclaiming mechanism that takes advantage of early completions. Finally, scheduling by deadline before overload and by value during overload works best in most practical conditions.

When hard periodic and firm aperiodic tasks are jointly scheduled in the same system, the processor workload can vary according to the arrival times of aperiodic requests. In order to guarantee the schedulability of the periodic task set, in overload conditions some aperiodic tasks must be rejected. In this paper we propose a technique that, in overload conditions, adds robustness to the joint scheduling of periodic and aperiodic tasks in systems with dynamic priorities. Our technique is based on an aperiodic server, called total bandwidth server, already proven effective in a previous work. Here the algorithm is first extended to efficiently handle firm aperiodic tasks and then integrated with a robust guarantee mechanism that allows to achieve graceful degradation in case of transient overloads. Extensive simulations show that the proposed new algorithm is effective in all workload conditions.