Task Scheduling Algorithms for Energy Optimization Under Scheduling Duration and Reliability Constraints

Download PDF
Open Webpage

Shizhuang Li, Bide Hao, Shichun Yang, Qianlong Duan, Wenlong Chen, Fan Zhou

Published: 01 Jan 2024, Last Modified: 03 Aug 2025INDIN 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: High-performance domain controllers' hardware and software are put to the test by the growing array of additional capabilities found in smart, connected cars. In smart connected automobiles, the energy consumption of high-performance domain controllers is contributing to the whole vehicle's energy consumption at an increasing rate, even though hardware systems' computing capacity is also expanding quickly. Thus, it is crucial to research reducing processor energy consumption while maintaining system performance and dependability. This paper investigates the problem of energy-optimized task scheduling in an on-board operating system for SOA-oriented architectures by combining the DVFS technique and the DAG task model under the constraints of scheduling duration and reliability. Firstly, the energy-optimal task scheduling problem for heterogeneous multicore systems is described, focusing on the constraints of scheduling duration and reliability. The shortcomings of traditional task scheduling algorithms are also analyzed. Secondly, a task scheduling algorithm based on the meta-heuristic Whale Optimization Algorithm (WOA) is proposed. This algorithm includes the design of encoding, decoding, and constraint processing schemes, and assigns processing units and corresponding DVFS levels to each task in the DAG task set to achieve energy consumption optimization while satisfying constraints. To address the suboptimal performance of the Whale Optimization Algorithm in high-dimensional problems, a multi-strategy optimization approach is introduced. This enhanced algorithm incorporates chaotic mapping, adaptive nonlinear convergence factors, dynamic inertia coefficients, the Lévy flight strategy, and the evolutionary population dynamics strategy. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments.