基于LSTM和GRNN的容器配额优化算法

周泓岑; 白恒; 才振功; 蔡亮; 顾静; 汤志敏

doi:10.12263/DZXB.20201211

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基于LSTM和GRNN的容器配额优化算法

学术论文 | 更新时间：2025-12-08

- 基于LSTM和GRNN的容器配额优化算法
- Container Quota Optimization Algorithm Based on GRNN and LSTM
- 电子学报 2022年50卷第2期页码：366-373
- 作者机构：
  
  1.浙江大学软件学院, 浙江宁波 315000
  2.阿里巴巴集团, 浙江杭州 310000
- 作者简介：
  
  [ "周泓岑男，1994年8月出生，四川达州人.现为浙江大学软件工程硕士研究生.主要研究方向为云计算、运筹优化、深度学习." ]
  [ "白恒男，1995年7月出生，山西吕梁人.现为浙江大学软件工程硕士研究生.主要研究方向为云计算、容器化、大数据.E-mail: zhouhongcen@zju.edu.cn" ]
  [ "才振功（通讯作者）男，1983年出生，河南商丘人.博士.现为浙江大学软件学院副研究员，硕士生导师.主要研究方向为云计算、边缘计算、软件工程.E-mail: cstcaizg@zju.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2019YFB1600700);阿里巴巴-浙江大学前沿技术联合研发中心(XT622019000187);阿里巴巴创新研究计划（AIR）合作项目;浙江大学-中移在线联合创新实验室(CMOS01HT20180623)
- DOI：10.12263/DZXB.20201211
  中图分类号： TP18;TP393.09
- 收稿：2020-10-29，
  
  修回：2021-01-08，
  
  纸质出版：2022-02-25
- 稿件说明：
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周泓岑,白恒,才振功等.基于LSTM和GRNN的容器配额优化算法[J].电子学报,2022,50(02):366-373.

ZHOU Hong-cen,BAI Heng,CAI Zhen-gong,et al.Container Quota Optimization Algorithm Based on GRNN and LSTM[J].ACTA ELECTRONICA SINICA,2022,50(02):366-373.
周泓岑,白恒,才振功等.基于LSTM和GRNN的容器配额优化算法[J].电子学报,2022,50(02):366-373. DOI： 10.12263/DZXB.20201211.

ZHOU Hong-cen,BAI Heng,CAI Zhen-gong,et al.Container Quota Optimization Algorithm Based on GRNN and LSTM[J].ACTA ELECTRONICA SINICA,2022,50(02):366-373. DOI： 10.12263/DZXB.20201211.

摘要

为了实现容器配额设置自动化和集群资源利用最大化，本文设计了一种容器配额优化算法.本文在长短期记忆（Long Short-Term Memory，LSTM）神经网络和广义回归神经网络（Generalized Regression Neural Network，GRNN）的基础上设计了深度神经网络（Long short-term memory and GRNN Network，LGN），并使用改进量子粒子群算法优选网络结构超参数，以实现自动调参和更快的收敛速度.容器配额优化算法步骤如下：首先根据历史数据使用LGN训练资源容量模型，然后使用改进的量子粒子群算法优化模型参数，最后使用资源容量模型计算容器配额.通过与谷歌容器垂直自动扩展器（Vertical Pod Autoscaler，VPA）和水平自动扩展器（Horizontal Pod Autoscaler，HPA）生成的配额进行对比发现，本文提出的优化算法较VPA和HPA降低了至少10%的资源分配总量，同时提升了至少6%的资源利用率.

Abstract

In order to realize the automation of container quota setting and the maximization of cluster resource utilization

this paper designed a container quota optimization algorithm. In this paper

LGN(long short-term memory and GRNN network) was designed based on the LSTM(long short-term memory) and the GRNN(generalized regression neural network). Also

the improved quantum particle swarm algorithm was used to optimize the hyperparameters of the network structure to achieve automatic parameter adjustment and faster convergence speed. The steps of the container quota optimization algorithm are as follows. First

LGN is used to train the resource capacity model based on historical data. Then the improved quantum particle swarm algorithm is adopted to optimize the model parameters. Finally

the resource capacity model is designed to calculate container quotas. By comparing with the quotas generated by Google Container VPA(vertical pod autoscaler) and HPA(horizontal pod autoscaler)

the optimization algorithm proposed in this paper has at least 10% lower total resource allocation and 6% higher resource utilization.

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references

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