计算机科学 ›› 2010, Vol. 37 ›› Issue (5): 143-145.

• 数据库与数据挖掘 • 上一篇    下一篇

基于蚁群优化算法的云数据库动态路径规划

史恒亮,白光一,唐振民,刘传领   

  1. (南京理工大学计算机学院 南京210094);(河南科技大学电信学院 洛阳471003);(方舟信息技术有限公司 苏州215021)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(90820306)资助。

Cloud Database Dynamic Route Scheduling Based on Ant Colony Optimization Algorithm

SHI Heng-liang,BAI Guang-yi,TANG Zhen-min, LIU Chuan-ling   

  • Online:2018-12-01 Published:2018-12-01

摘要: 云计算是下一代计算网络模型的发展趋势。云中的存储资源如何快速地路由,更是研究的难点。蚁群算法是基于群体的仿生优化算法,具有智能搜索、全局优化、鲁棒性、分布式计算和容易与其他算法相结合等优点。根据这两种事物的特点,提出了合理的结合算法,该算法能够在云中快速、合理地找到所需访问的数据库,减少云数据库数路由的动态负荷,从而很大程度上提高云计算的效率。

关键词: 蚁群优化算法,云计算,云数据库,动态路径规划

Abstract: Cloud computing is the development trend of next generated computing network model. And how to effectively route storage resource in cloud is development's difficulty in ring of industry. Ant colony algorithm is a bionics optimization algorithm based group ants which has many priorities such as intelligent routing, overall optimization, robust ness,distributed computing,ability to mix itself with other algorithm. Based on the above two factors, this paper proposed a reasonable algorithm which can find the requiring database rapidly and effectively, and reduce the dynamic routing burdens of cloud database routing, and enhance the efficiency of cloud computing at a large.

Key words: ACO(ant colony optimization) , Cloud computing, Cloud database, Dynamic routing scheduling

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