Feedback-Based Admission Control for Hard Real-Time Task Allocation Under Dynamic Workload on Many-Core Systems

TL;DR: An approach for applying control-theory-based admission control to predict the task Schedulability so that the exact schedulability analysis is performed only to the tasks with positive prediction results, which is particularly beneficial for heavier workloads.

Abstract: In hard real-time systems, a computationally expensive schedulability analysis has to be performed for every task. Fulfilling this requirement is particularly tough when system workload and service capacity are not available a priori and thus the analysis has to be conducted at runtime. This paper presents an approach for applying control-theory-based admission control to predict the task schedulability so that the exact schedulability analysis is performed only to the tasks with positive prediction results. In case of a careful fine-tuning of parameters, the proposed approach can be successfully applied even to many-core embedded systems with hard real-time constraints and other time-critical systems. The provided experimental results demonstrate that, on average, only 62i?ź% of the schedulability tests have to be performed in comparison with the traditional, open-loop approach. The proposed approach is particularly beneficial for heavier workloads, where the number of executed tasks is almost unchanged in comparison with the traditional open-loop approach. By our approach, only 32i?ź% of exact schedulability tests have to be conducted. Moreover, for the analysed industrial workloads with dependent jobs, the proposed technique admitted and executed 11i?ź% more tasks while not violating any timing constraints.