Energy-aware Scheduling of Workflow Applications Towards Schedule Length Optimization in Heterogeneous Distributed Embedded Systems

Energy optimization constitutes a paramount design consideration in the realm of embedded systems development since these devices are inherently constrained by finite battery resources. Designing and developing an effective energy-aware scheduling approach is a desirable work to provide excellent processing capability while keeping the energy consumption under control. Although previous approaches can obtain reasonable scheduling solutions for tasks with energy consumption constraints, they are computationally expensive and have deficiencies in effectiveness or efficiency due to unfair or inefficient energy pre-assignment strategies. In this article, we study the energy-aware workflow scheduling problem and present a three-stage list-based approach to minimize the schedule length of workflows in heterogeneous distributed embedded systems. First, the workflow applications and energy consumption of processors are modelled, and the energy-aware workflow scheduling problem is formulated as a non-linear mixed integer programming one with various dependency and energy constraints. Then, with an effective task prioritization strategy and a reasonable energy pre-assignment strategy, a three-stage list-based scheduling approach is proposed to schedule the tasks and minimize the schedule length of workflows. Experiments on randomly-generated and real-life workflows demonstrate that our proposed approach constantly outperforms the existing approaches and our algorithm can, respectively, reduce the normalized schedule length and the deviation ratio by 16.7% and 7.6% in average.