| 基于烟花算法的基站群协作计算卸载模型研究 |
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| 引用本文： | 许斌,闫文卿,韩卓凡,何广深,邓涛,赵云凯,亓晋. 基于烟花算法的基站群协作计算卸载模型研究[J]. 系统仿真学报, 2022, 34(2): 354-365. DOI: 10.16182/j.issn1004731x.joss.20-0700 |
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| 作者姓名： | 许斌 闫文卿 韩卓凡 何广深 邓涛 赵云凯 亓晋 |
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| 作者单位： | 1.南京邮电大学, 江苏 南京 2100032.南京医药股份有限公司, 江苏 南京 2100123.金陵科技学院 数据科学与智慧软件江苏省重点建设实验室, 江苏 南京 211169 |
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| 基金项目： | 国家自然科学基金(61802208);中国博士后基金(2019M651923);江苏省博士后科研资助(2019K223);NUPTSF(NY220060);数据科学与智慧软件江苏省重点建设实验室开放基金资助(2020DS301) |
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| 摘 要： | 车联网、AR、AI等计算密集、时延敏感型应用迅速发展，而移动设备因自身计算能力相对不足，执行此类应用任务时会因高时延而严重影响用户体验甚至无法满足用户需求。针对此问题，提出综合考虑时延与成本的多用户、多MEC (mobile edge computing)服务器的基站群协作计算卸载模型。并提出基于凸优化的改进烟花算法（improved fireworks algorithm based on convex optimization, CVX-FWA）来对模型进行求解，对用户任务进行合理的卸载与资源分配。仿真结果表明，提出的计算卸载方案有效降低了任务总时延成本值，实现计算卸载资源的整体优化配置。
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| 关 键 词： | 移动边缘计算 卸载决策 资源分配 烟花算法 凸优化 |
| 收稿时间： | 2020-09-15 |
Research on Collaborative Computing Offloading Model for Base Station Groups Based on Fireworks Algorithm |
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| Bin Xu,Wenqing Yan,Zhuofan Han,Guangshen He,Tao Deng,Yunkai Zhao,Jin Qi. Research on Collaborative Computing Offloading Model for Base Station Groups Based on Fireworks Algorithm[J]. Journal of System Simulation, 2022, 34(2): 354-365. DOI: 10.16182/j.issn1004731x.joss.20-0700 |
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| Authors: | Bin Xu Wenqing Yan Zhuofan Han Guangshen He Tao Deng Yunkai Zhao Jin Qi |
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| Affiliation: | 1.Nanjing University of Posts and Telecommunications, Nanjing 210003, China2.Nanjing Pharmaceutical Co. , Ltd. , Nanjing 210012, China3.Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China |
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| Abstract: | Internet of Vehicles (IoV), AR, AI, and other computing-intensive, time-delay-sensitive applications are developing rapidly. However, due to the relatively insufficient computing capacity of mobile devices, such application tasks face serious latency, which seriously affects user experience and even fails to meet the needs of users. To solve this problem, by comprehensively considering delays and costs, we propose a cooperative computing offloading model based on a multi-user and multi-mobile edge computing (multi-MEC) server for base station groups. In addition, an improved fireworks algorithm based on convex optimization (CVX-FWA) is presented to solve the model and perform reasonable offloading and resource allocation for user tasks. The simulations show that the computing offloading scheme proposed effectively reduces the execution delay and cost of all user tasks and realizes the overall optimal allocation of computing offloading resources. |
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| Keywords: | mobile edge computing offloading decision resource allocation fireworks algorithm convex optimization |
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