电子测量与仪器学报

王昊,李晖,宋端正,诸锦涛.面向云-雾计算系统中的遗传算法任务调度研究[J].电子测量与仪器学报,2023,37(8):40-51

面向云-雾计算系统中的遗传算法任务调度研究

Research on genetic algorithm task scheduling in cloud-fog oriented computing systems

DOI：

英文关键词:cloud-fog computing task scheduling genetic algorithm entropy method

基金项目:国家自然科学基金项目 ( 61661018 )、江苏省基础研究计划青年基金项目 ( BK20210064 )、江苏省双创博士人才项目(JSSCBS20210863)、南京信息工程大学滨江学院科研启动项目(2021r006)资助

作者	单位
王昊	1. 南京信息工程大学电子与信息工程学院
李晖	1. 南京信息工程大学电子与信息工程学院，2. 无锡学院电子信息工程学院
宋端正	1. 南京信息工程大学电子与信息工程学院
诸锦涛	1. 南京信息工程大学电子与信息工程学院

Author	Institution
Wang Hao	1. College of Electronics and Information Engineering, Nanjing University of Information Science and Technology
Li Hui	1. College of Electronics and Information Engineering, Nanjing University of Information Science and Technology，2. College of Electronics and Information Engineering, Wuxi University
Song Duanzheng	1. College of Electronics and Information Engineering, Nanjing University of Information Science and Technology
Zhu Jintao	1. College of Electronics and Information Engineering, Nanjing University of Information Science and Technology

摘要点击次数: 233

全文下载次数: 355

中文摘要:

近年来,不断发展物联网产生出海量数据,这给网络云等基础设施带来巨大压力。对此学者们提出的“云-雾”计算的架构模型,其中雾计算的障碍之一是如何分配计算资源以最小化网络资源。研究提出了一种基于启发式算法的 TCC( time cost computing-power)算法,以优化该异构系统中基于遗传算法的“云-雾”计算中的任务调度问题,包括执行时间、操作成本以及总算力资源。算法以“云-雾-端-网”混合计算任务调度为研究对象,以进化遗传算法为研究手段,结合云计算、雾计算、遗传算法等技术优势,实现时延、代价以及算力之间的平衡。在混合计算任务调度中,对于仅考量单一指标的 TCaS 算法,本算法具有更好的均衡性;本算法适应度值分别比 BLA 算法 0. 93%以及 RR 算法 26. 02%。本算法还可以灵活地匹配用户对高性能-成本-算力多方面的需求,提升系统的有效性。

英文摘要:

In recent years, the growing Internet of Things (IoT) has generated huge amounts of data, which has put enormous pressure on infrastructures such as the network cloud. One of the obstacles to fog computing is how to allocate computing resources in a way that minimizes network resources. A heuristic-based TCC (time cost computing-power) algorithm is proposed to optimise the task scheduling problem in genetic algorithm-based “cloud-fog” computing in this heterogeneous system, including execution time, operational cost and total computing power resources. The algorithm is based on “ cloud-fog-end-net ” hybrid computing task scheduling, and uses evolutionary genetic algorithms as a research tool, combining the advantages of cloud computing, fog computing and genetic algorithms to achieve a balance between latency, cost and computing power. In the hybrid computing task scheduling, this algorithm has a better balance performance than TCaS algorithm which only considers a single metric; the adaptation value of this algorithm is 0. 93% and 26. 02% higher than BLA algorithm and RR algorithm respectively. The algorithm is also flexible enough to match the user’ s needs in terms of high performance-cost-computing power, enhancing the effectiveness of the system.

查看全文查看/发表评论下载PDF阅读器