A Multi-Level DPM Approach for Real-Time DAG Tasks in Heterogeneous Processors

Abstract

The modeling and analysis of real-time applications focus on the worst-case scenario because of their strict timing requirements. However, many real-time embedded systems include critical applications requiring not only timing constraints but also other system limitations, such as energy consumption.In this paper, we study the energy-aware real-time scheduling of Directed Acyclic Graph (DAG) tasks. We integrate the Dynamic Power Management (DPM) policy to reduce the Worst-Case Energy Consumption (WCEC), which is an essential requirement for energy-constrained systems. Besides, we extend our analysis with tasks' probabilistic information to improve the Average-Case Energy Consumption (ACEC), which is, instead, a common non-functional requirement of embedded systems. To verify the benefits of our approach in terms of reduced energy consumption, we finally conduct an extensive simulation, followed by an experimental study on an Odroid-H2 board. Compared to the state-of-the-art solution, our approach is able to reduce the power consumption up to 32.1%.

Real-time task scheduling and network device security for complex embedded systems based on deep learning networks

“…Many research works have been performed in this area. [17], including the attributes of the tasks such as worst-case execution time (WCET) [7] [9] [17], deadline [7] [9] [10] [11] [12] [13] [14] [15] [16], task period [8] [9] [10] [13] [15], possible priorities [8] [10], or the relations of the tasks specified as a DAG task graph with the communication cost and precedence order of the tasks [6] [7] [9] [10] [11] [12] [14] [15] [16]. The mathematical model of the tasks, together with the machine architecture features, are used to find an optimal strategy for power management.…”