大数据 ›› 2019, Vol. 5 ›› Issue (6): 62-72.doi: 10.11959/j.issn.2096-0271.2019050

• 研究 • 上一篇    

基于APMSSGA-LSTM的容器云资源预测

谢晓兰1,2,张征征1(),郑强清1,陈超泉1   

  1. 1 桂林理工大学信息科学与工程学院,广西 桂林 541004
    2 广西嵌入式技术与智能系统重点实验室,广西 桂林 541004
  • 出版日期:2019-11-15 发布日期:2020-01-10
  • 作者简介:谢晓兰(1974- ),女,博士,桂林理工大学信息科学与工程学院教授、院长、博士生导师,主要研究方向为云计算、并行计算、大数据、地球物理勘查与信息技术|张征征(1994- ),女,桂林理工大学信息科学与工程学院硕士生,主要研究方向为云计算、大数据|郑强清(1993- ),男,桂林理工大学信息科学与工程学院硕士生,主要研究方向为云计算、大数据|陈超泉(1963- ),男,桂林理工大学信息科学与工程学院副教授、硕士生导师,主要研究方向为大数据
  • 基金资助:
    国家自然科学基金资助项目(6176203);广西创新驱动重大专项(2018AA32003);广西重点研发计划基金资助项目(AB17195029);广西重点研发计划基金资助项目(AB18126006);广西硕士研究生创新基金资助项目(YCSW2017156);广西硕士研究生创新基金资助项目(YCSW2018157);广西中青年教师基础能力提升基金资助项目(KY2016YB184)

Container cloud resource prediction based on APMSSGA-LSTM

Xiaolan XIE1,2,Zhengzheng ZHANG1(),Qiangqing ZHENG1,Chaoquan CHEN1   

  1. 1 College of Information Science and Engineering,Guilin University of Technology,Guilin 541004,China
    2 Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin 541004,China
  • Online:2019-11-15 Published:2020-01-10
  • Supported by:
    The National Natural Science Foundation of China(6176203);Science and Technology Major Project of Guangxi(2018AA32003);The Key Research and Development Program of Guangxi(AB17195029);The Key Research and Development Program of Guangxi(AB18126006);Innovation Project of Guangxi Graduate Education(YCSW2017156);Innovation Project of Guangxi Graduate Education(YCSW2018157);Subsidies for the Project of Promoting the Ability of Young and Middle-Aged Scientific Research in Universities and Colleges of Guangxi(KY2016YB184)

摘要:

容器云的发展与应用对资源的高并发、高可用、高弹性、高灵活性等的需求越来越强烈。在对容器云资源预测问题研究现状进行调查后,提出一种采用自适应概率的多选择策略遗传算法(APMSSGA)优化长短期记忆网络(LSTM)的容器云资源预测模型。实验结果表明,与简单遗传算法(SGA)相比,APMSSGA在LSTM参数最优解组合搜索方面更加高效,APMSSGA-LSTM模型的预测精度较高。

关键词: 容器云, 资源预测, 长短期记忆网络, 遗传算法

Abstract:

With the development and application of container cloud,the demand for high concurrency,high availability,high flexibility,and high flexibility of resources is becoming more and more intense.After investigating the current research status of container cloud resource prediction,a container cloud resource prediction model which using an adaptive probability multiselection strategy genetic algorithm (APMSSGA) to optimize the long short term memory network (LSTM) was proposed.The experimental results show that compared with the simple genetic algorithm (SGA),APMSSGA is more efficient in LSTM parameter optimal solution combination search,and the APMSSGA-LSTM model has higher prediction accuracy.

Key words: container cloud, resource prediction, long short term memory network, genetic algorithm

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