The paper addresses the real-time fixed-priority scheduling problem for battery-powered embedded systems whose energy storage unit is replenished by an environmental energy source. In this context, a task may meet its deadline only if its cost of energy can be satisfied early enough. Hence, a scheduling policy for such a system should account for properties of the source of energy, capacity of the energy storage unit and tasks cost of energy. Classical fixed-priority schedulers are no more suitable for this model. Based on these motivations, we propose P F PASAP an optimal scheduling algorithm that handles both energy and timing constraints. Furthermore, we state the worst case scenario for non concrete tasksets 1 scheduled with this algorithm and build a necessary and sufficient feasibility condition for non concrete tasksets. Moreover, a minimal bound of the storage unit capacity that keeps a taskset schedulable with P F PASAP is also proposed. Finally, we validate the proposed theory with large scale simulations and compare our algorithm with other existing ones.
Abstract-Energy harvesting is the process of generating electrical energy from environmental sources such as solar panels. In recent years, this term has been frequently applied in the context of small autonomous devices such as wireless sensor nodes. The classical scheduling theory is insufficient for this kind of systems and new scheduling problems arise in this context. Until now, the research on this area focused in trying to improve the efficiency of existing algorithms. Our approach is to complete these efforts by a feasibility theory allowing us to understand why classical optimal algorithms are not efficient anymore with energy constraints.In this paper, we try to establish a schedulability test for a fixed priority real-time scheduling problem with energy constraints. We first introduce the problem and describe the model. Then, to illustrate the difficulty of the problem, we focus on a preemptive fixed priority scheduling policy where all the executions are postponed as long as possible. This policy lets the harvester the maximal amount of time to refill the battery. We call this policy P F PALAP for As Late As Possible. We try to define sufficient and/or necessary schedulability conditions and discuss its potential optimality under some additional assumptions. Then, through simple counter examples, we show that intuitive assumptions are wrong for this scheduling problem, making it very interesting to study.
International audienceThis paper introduces sufficient schedulability tests for fixed-priority pre-emptive scheduling of a real-time system under energy constraints. In this problem, energy is harvested from the ambient environment and used to replenish a storage unit or battery. The set of real-time tasks is decomposed into two different types of task depending on whether their rate of energy consumption is (i) more than or (ii) no more than the storage unit replenishment rate. We show that for this task model, where execution may only take place when there is sufficient energy available, the worst-case scenario does not necessarily correspond to the synchronous release of all tasks. We derive sufficient schedulability tests based on the computation of worst-case response time upper and lower bounds. We show that these tests are sustainable with respect to decreases in the energy consumption of tasks, and increases in the storage unit replenishment rate. Further, we show that Deadline Monotonic priority assignment is optimal with respect to the derived tests. We examine both the effectiveness and the tightness of the bounds, via an empirical investigation
International audienceThis paper introduces sufficient schedulability tests for fixed-priority pre-emptive scheduling of a real-time system under energy constraints. In this problem, energy is harvested from the ambient environment and used to replenish a storage unit or battery. The set of real-time tasks is decomposed into two different types of task depending on whether their rate of energy consumption is (i) more than or (ii) no more than the storage unit replenishment rate. We show that for this task model, where execution may only take place when there is sufficient energy available, the worst-case scenario does not necessarily correspond to the synchronous release of all tasks. We derive sufficient schedulability tests based on the computation of worst-case response time upper and lower bounds. We examine both the effectiveness and the tightness of the bounds, via an empirical investigation
This paper investigates schedulability analysis for thermal-aware realtime systems. Thermal constraints are becoming more and more critical in new generation miniaturized embedded systems, e.g. medical implants. As part of this work, we adapt the P F PASAP algorithm proposed in [1] for energy-harvesting systems to thermal-aware ones. We prove its optimality for non-concrete 1 fixed-priority task sets and propose a responsetime analysis based on worst-case response-time upper bounds. We evaluate the efficacy of the proposed bounds via extensive simulation over randomly-generated task systems.
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