Han Li, Zhu Shuaijie, Zhao Haoyang, He Yanqiang
College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, China.
Front Big Data. 2024 Oct 30;7:1422546. doi: 10.3389/fdata.2024.1422546. eCollection 2024.
The widespread use of mobile devices and compute-intensive applications has increased the connection of smart devices to networks, generating significant data. Real-time execution faces challenges due to limited resources and demanding applications in edge computing environments. To address these challenges, an enhanced whale optimization algorithm (EWOA) was proposed for task scheduling. A multi-objective model based on CPU, memory, time, and resource utilization was developed. The model was transformed into a whale optimization problem, incorporating chaotic mapping to initialize populations and prevent premature convergence. A nonlinear convergence factor was introduced to balance local and global search. The algorithm's performance was evaluated in an experimental edge computing environment and compared with ODTS, WOA, HWACO, and CATSA algorithms. Experimental results demonstrated that EWOA reduced costs by 29.22%, decreased completion time by 17.04%, and improved node resource utilization by 9.5%. While EWOA offers significant advantages, limitations include the lack of consideration for potential network delays and user mobility. Future research will focus on fault-tolerant scheduling techniques to address dynamic user needs and improve service robustness and quality.
移动设备和计算密集型应用程序的广泛使用增加了智能设备与网络的连接，产生了大量数据。由于边缘计算环境中的资源有限和应用需求苛刻，实时执行面临挑战。为应对这些挑战，提出了一种用于任务调度的增强型鲸鱼优化算法（EWOA）。开发了一种基于CPU、内存、时间和资源利用率的多目标模型。该模型被转化为一个鲸鱼优化问题，纳入混沌映射以初始化种群并防止过早收敛。引入了一个非线性收敛因子来平衡局部搜索和全局搜索。在实验性边缘计算环境中评估了该算法的性能，并与ODTS、WOA、HWACO和CATSA算法进行了比较。实验结果表明，EWOA将成本降低了29.22%，将完成时间减少了17.04%，并将节点资源利用率提高了9.5%。虽然EWOA具有显著优势，但局限性包括未考虑潜在的网络延迟和用户移动性。未来的研究将集中在容错调度技术上，以满足动态用户需求并提高服务的鲁棒性和质量。
