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.

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.

A Research on Dynamic Scheduling Strategy for Aperiodic Tasks in Multiprocessor

This paper in-depth analyses the existing multiprocessor dynamic scheduling algorithms for aperiodic task, andproposes a moderately grouping algorithmbased on grouping and selecting appropriate strategy for the multiprocessor system.Satisfyingthe task deadline ofthe smaller objective function value, grouping strategy give priority to the taskshared access to the resources, improving the utilization rate of resources. Based on the earliest available time between therequired task and theprocessor in system, selecting appropriate strategy will schedule the task to the processor, improvingthe processor utilization.Compared with the existing algorithms, moderately grouping algorithm has a better performance.