武汉科技大学计算机科学与技术学院;
多接入边缘计算(MEC)已经成为一种重要的范式，通过使用MEC技术，终端设备可以将其计算任务卸载到邻近的边缘服务器或远程云服务器上执行。但对于庞大的任务量而言，它们的计算能力、内存和能源仍然受限。如何进行有效的任务分流至关重要。该文允许任务在三个位置(本地设备、边缘端、云端)处理，有效避免了传统的二元规划机制缺乏灵活性，不考虑任务依赖等问题。并提出了一种河马优化算法(HOA)与改进型强度Pareto进化算法(SPEA2)相结合的多目标河马优化算法(MHOA)。MHOA算法充分利用HOA的全局搜索能力和多样性维护等特点，以及SPEA2中Pareto前沿优化和局部开发能力，并采用特定的初始化算法，在考虑任务依赖关系的同时，解决边缘云计算环境下多用户依赖任务的卸载问题，同时最小化执行时延、能耗、资源使用成本三个约束。将MHOA与其他优化算法进行了比较，结果表明MHOA在适应度方面比第二优的优化器优化了3.5%。
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基本信息:
DOI：10.20165/j.cnki.ISSN1673-629X.2024.0378
中图分类号:TP18;TP393.09
引用信息:
[1]郑文祥,刘静,陈家辉.HOA和SPEA2结合的边缘云计算任务卸载优化[J].计算机技术与发展,2025,35(04):7-14.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0378.
基金信息:
湖北省教育科学研究计划重点项目(D20201102)