The Fixed Priority Scheduling Problem for Energy Harvesting Real-Time Systems

Abstract

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.

The Optimality of PFPasap Algorithm for Fixed-Priority Energy-Harvesting Real-Time Systems

“…Some scheduling algorithms and heuristics were proposed in [7], [9]. We selected P F P ST which is not optimal but has the lowest failure rate according to the experiment performed in [7].…”

“…We selected P F P ST which is not optimal but has the lowest failure rate according to the experiment performed in [7]. We also selected the P F P ALAP algorithm because it can be used to implement a sufficient feasibility condition [9]. In this section, we compare these two algorithms with P F P ASAP , then we analyze their performance.…”

Energy Allocation for LoRaWAN Nodes with Multi-Source Energy Harvesting

“…Energy allocation for multi-task systems can be seen as a task scheduling problem, where tasks are constrained by both their energy consumption and QoS requirements instead of their deadline and/or period. Most energy-aware task scheduling policies [ 28 , 29 , 30 , 31 , 32 , 33 ] target real-time systems, for which the objective is to ensure that all tasks meet their deadline requirements instead of allocating energy to different tasks. DEOS [ 34 ] takes a different approach by considering energy as a schedulable resource to dynamically schedule tasks depending on their energy consumption and the available energy.…”

Energy Management for Energy Harvesting-Based Embedded Systems: A Systematic Mapping Study

“…As shown in Figure 7, 31% of papers focused on energy scheduling (see, e.g., [52,59,62,65,97,107]), such as studying how to combine energy-constrained scheduling algorithms with the introduction of an energy harvesting unit; 28% of papers focused on energy harvesting methods (see, e.g., [29]), such as using reinforcement learning (RL) to automatically configure a device to maximize energy storage or minimize energy consumption as much as possible (see, e.g., [23,29,36,45]). Since the energy harvesting by EHES is affected by geographic location and the surrounding environment, the collected data are different.…”