Container cloud resource prediction based on APMSSGA-LSTM

Xiaolan XIE; Zhengzheng ZHANG; Qiangqing ZHENG; Chaoquan CHEN

doi:10.11959/j.issn.2096-0271.2019050

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Container cloud resource prediction based on APMSSGA-LSTM

STUDY | 更新时间：2024-06-03

- Container cloud resource prediction based on APMSSGA-LSTM
- Big Data Research Vol. 5, Issue 6, Pages: 2019050-1(2019)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019050
  CLC： TP393
- Online First：2019-11，
  
  Published：15 November 2019
- 稿件说明：
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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：

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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