基于APMSSGA-LSTM的容器云资源预测

谢晓兰; 张征征; 郑强清; 陈超泉

doi:10.11959/j.issn.2096-0271.2019050

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基于APMSSGA-LSTM的容器云资源预测

研究 | 更新时间：2024-06-03

- 基于APMSSGA-LSTM的容器云资源预测
- Container cloud resource prediction based on APMSSGA-LSTM
- 大数据 2019年5卷第6期页码：2019050-1
- 作者机构：
  
  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
- 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" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(6176203);广西创新驱动重大专项;Science and Technology Major Project of Guangxi(2018AA32003);广西重点研发计划基金资助项目;The Key Research and Development Program of Guangxi(AB17195029);广西重点研发计划基金资助项目;The Key Research and Development Program of Guangxi(AB18126006);广西硕士研究生创新基金资助项目;Innovation Project of Guangxi Graduate Education(YCSW2017156);广西硕士研究生创新基金资助项目;Innovation Project of Guangxi Graduate Education(YCSW2018157);广西中青年教师基础能力提升基金资助项目;Subsidies for the Project of Promoting the Ability of Young and Middle-Aged Scientific Research in Universities and Colleges of Guangxi(KY2016YB184)
- DOI：10.11959/j.issn.2096-0271.2019050
  中图分类号： TP393
- 网络首发：2019-11，
  
  纸质出版：2019-11-15
- 稿件说明：
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谢晓兰, 张征征, 郑强清, 等. 基于APMSSGA-LSTM的容器云资源预测[J]. 大数据, 2019,5(6):2019050-1.

Xiaolan XIE, Zhengzheng ZHANG, Qiangqing ZHENG, et al. Container cloud resource prediction based on APMSSGA-LSTM[J]. Big Data Research, 2019, 5(6): 2019050-1.
谢晓兰, 张征征, 郑强清, 等. 基于APMSSGA-LSTM的容器云资源预测[J]. 大数据, 2019,5(6):2019050-1. DOI： 10.11959/j.issn.2096-0271.2019050.

Xiaolan XIE, Zhengzheng ZHANG, Qiangqing ZHENG, et al. Container cloud resource prediction based on APMSSGA-LSTM[J]. Big Data Research, 2019, 5(6): 2019050-1. DOI： 10.11959/j.issn.2096-0271.2019050.

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。

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.

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references

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