An optimal energy aware aperiodic task server for autonomous IoT sensors

A real-time applicative software consists of both aperiodic and periodic tasks. The periodic tasks have regular arrival times and strict deadlines. The aperiodic tasks have irregular arrival times and no deadline. The objective of an optimal aperiodic task server is to guarantee minimal response times for the aperiodic tasks and no violation of hard deadlines for periodic tasks. We consider a real-time energy harvesting system composed of energy harvester, energy storage unit, uniprocessing unit and the real-time tasks. We introduce a novel periodic task scheduler, namely SSP which is an extension of the slack stealing server so as to cope with fluctuations in energy availability. Experimental results show that the proposed algorithm achieves better performance in terms of aperiodic responsiveness. Simulations have been conducted for various settings of workloads and harvested energy profiles.

Optimal Slack Stealing Servicing for Real-Time Energy Harvesting Systems

We consider the problem of real-time scheduling in uniprocessor devices powered by energy harvesters. In particular, we focus on mixed sets of tasks with time and energy constraints: hard deadline periodic tasks and soft aperiodic tasks without deadlines. We present an optimal aperiodic servicing algorithm that minimizes the response times of aperiodic tasks without compromising the schedulability of hard deadline periodic tasks. The server, called Slack Stealing with energy Preserving (SSP), is designed based on a slack stealing mechanism that profits whenever possible from available spare processing time and energy. We analytically establish the optimality of SSP. Our simulation results validate our theoretical analysis.

Scheduling of Aperiodic Tasks on Multicore Systems

For a hard real-time multicore system, the two important issues that are required to be addressed are feasibility of a task set and balancing of load amongst the cores of the multicore systems. Most of the previous work done considers the scheduling of periodic tasks on a multicore system. This chapter deals with scheduling of aperiodic tasks on a multicore system in a hard real-time environment. In this regard, a multicore total bandwidth server (MTBS) is proposed which schedules the aperiodic tasks with already guaranteed periodic tasks amongst the cores of the multicore processor. The proposed MTBS algorithm works by computing a virtual deadline for every aperiodic task that is arriving to the system. Apart from schedulability of aperiodic tasks, the MTBS approach also focuses on reducing the response time of aperiodic tasks. The simulation studies of MTBS were carried out to find the effectiveness of the proposed approach, and it is also compared with the existing strategies.

Online Slack-Stealing Scheduling with Modified laEDF in Real-Time Systems

In hard real-time task systems where periodic and aperiodic tasks coexist, the object of task scheduling is to reduce the response time of the aperiodic tasks while meeting the deadline of periodic tasks. Total bandwidth server (TBS) and advanced TBS (ATBS) are used in dynamic priority systems. However, these methods are not optimal solutions because they use the worst-case execution time (WCET) or the estimation value of the actual execution time of the aperiodic tasks. This paper presents an online slack-stealing algorithm called SSML that can make significant response time reducing by modification of look-ahead earliest deadline first (laEDF) algorithm as the slack computation method. While the conventional slack-stealing method has a disadvantage that the slack amount of each frame must be calculated in advance, SSML calculates the slack when aperiodic tasks arrive. Our simulation results show that SSML outperforms the existing TBS based algorithms when the periodic task utilization is higher than 60%. Compared to ATBS with virtual release advancing (VRA), the proposed algorithm can reduce the response time up to about 75%. The performance advantage becomes much larger as the utilization increases. Moreover, it shows a small performance variation of response time for various task environments.

Ensuring the sustainability of real-time embedded system under both QoS and Energy Constraints

Nowadays, wireless sensor networks (WSNs) are more and more used in applications such as environment monitoring, healthcare monitoring, etc...The challenge in sensor networks is to ensure the sustainability of the system by guaranteeing the required performance level. However, with the limited capacity of finite power sources and the need of guaranteeing a long lifetime of those systems, it is suitable to use energy harvesting which allows to supply low-power electronic systems by converting ambient energy into electric power. Hence, our study is concerned with the problem of soft periodic and aperiodic tasks scheduling in sensor nodes powered by energy harvesters. In this paper, we address this issue by proposing three energy-aware schedulers, namely BG-Green-RTO, BG-Green-BWP and Green-AWP which aim to improve the responsiveness of aperiodic tasks while still guaranteeing the execution of periodic tasks considering their timing and energy constraints. Such algorithms allow to gracefully cope with processing overload and energy starvation. Moreover, a simulation study permits to show their performance.