An improved preemption delay upper bound for floating non-preemptive region

In embedded systems, the timing behaviour of the control mechanisms are sometimes of critical importance for the operational safety. These high criticality systems require strict compliance with the offline predicted task execution time. The execution of a task when subject to preemption may vary significantly in comparison to its non-preemptive execution. Hence, when preemptive scheduling is required to operate the workload, preemption delay estimation is of paramount importance. In this paper a preemption delay estimation method for floating non-preemptive scheduling policies is presented. This work builds on [1], extending the model and optimising it considerably. The preemption delay function is subject to a major tightness improvement, considering the WCET analysis context. Moreover more information is provided as well in the form of an extrinsic cache misses function, which enables the method to provide a solution in situations where the non-preemptive regions sizes are small. Finally experimental results from the implementation of the proposed solutions in Heptane are provided for real benchmarks which validate the significance of this work.

Job phasing aware preemption deferral

Preemptions account for a non-negligible overhead during system execution. There has been substantial amount of research on estimating the delay incurred due to the loss of working sets in the processor state (caches, registers, TLBs) and some on avoiding preemptions, or limiting the preemption cost. We present an algorithm to reduce preemptions by further delaying the start of execution of high priority tasks in fixed priority scheduling. Our approaches take advantage of the floating non-preemptive regions model and exploit the fact that, during the schedule, the relative task phasing will differ from the worst-case scenario in terms of admissible preemption deferral. Furthermore, approximations to reduce the complexity of the proposed approach are presented. Substantial set of experiments demonstrate that the approach and approximations improve over existing work, in particular for the case of high utilisation systems, where savings of up to 22% on the number of preemption are attained.

Reabilitação energética de edifícios

A reabilitação é uma intervenção que confere ao edifício uma qualidade superior à que possuía aquando da sua construção. Desta forma, e com a entrada em vigor em final de 2013 da nova legislação referente à certificação energética dos edifícios, surge novamente o interesse na temática de reabilitar energeticamente o parque habitacional. Esta nova legislação aparece na sequencia das novas exigências da Comissão Europeia e do Parlamento Europeu face às alterações climáticas e consequentemente, em relação à eficiência energética. A reabilitação energética de edifícios, visa principalmente a melhoria das condições de conforto térmico, a redução dos consumos energéticos com aquecimento, arrefecimento e as águas quentes sanitárias. Tais melhorias são alcançáveis com intervenções que se foquem na envolvente dos edifícios, com a aplicação de sistemas de alta eficiência para o aquecimento, arrefecimento, iluminação e águas quentes sanitárias e com a integração de fontes de energias renováveis. Assim é possível alcançar edifícios com necessidades nulas ou quase nulas de energia, tal como exigem as metas impostas a nível europeu.

Capacity sharing and stealing in dynamic server-based real-time systems

This paper proposes a dynamic scheduler that supports the coexistence of guaranteed and non-guaranteed bandwidth servers to efficiently handle soft-tasks’ overloads by making additional capacity available from two sources: (i) residual capacity allocated but unused when jobs complete in less than their budgeted execution time; (ii) stealing capacity from inactive non-isolated servers used to schedule best-effort jobs. The effectiveness of the proposed approach in reducing the mean tardiness of periodic jobs is demonstrated through extensive simulations. The achieved results become even more significant when tasks’ computation times have a large variance.