TSSA: Task structure-aware scheduling of energy-constrained parallel applications on heterogeneous distributed embedded platforms

Heterogeneous distributed embedded systems are widely used because they can significantly improve the computing performance of parallel applications in embedded systems. Since such systems are limited by both system energy consumption and schedule length, further scheduling optimization design is required. To achieve a balance between computing performance and energy consumption, the task scheduling problem under energy constraints has attracted great attention from researchers in recent years. However, state-of-the-art algorithms ignore the impact of the task structure of the application on scheduling performance. In this study, a structure-aware task scheduling strategy is proposed for the parallel application scheduling problem in heterogeneous distributed embedded systems. Specifically, the structure of the application is considered a factor affecting the algorithm. Meanwhile, to reduce the schedule length of the application and avoid pessimistic energy allocation, this study proposes an improved weighted energy pre-allocation algorithm. Experimental results demonstrate that the task structure-aware algorithm can decrease the scheduling length for energy consumption-constrained parallel applications. Compared with the MSLECC algorithm, the task scheduling length of the task structure-aware algorithm in the Gaussian Elimination transform can be shortened by 42.39%.

Automated summary

This study proposes a structure-aware task scheduling strategy for parallel application scheduling in heterogeneous distributed embedded systems, along with an improved energy pre-allocation algorithm. Experimental results demonstrate that the algorithm can reduce task scheduling length and energy consumption.