Container Quota Optimization Algorithm Based on GRNN and LSTM

ZHOU Hong-cen; BAI Heng; CAI Zhen-gong; CAI Liang; GU Jing; TANG Zhi-min

doi:10.12263/DZXB.20201211

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Container Quota Optimization Algorithm Based on GRNN and LSTM

PAPERS | 更新时间：2025-12-08

- Container Quota Optimization Algorithm Based on GRNN and LSTM
- ACTA ELECTRONICA SINICA Vol. 50, Issue 2, Pages: 366-373(2022)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20201211
  CLC： TP18;TP393.09
- Received：29 October 2020，
  
  Revised：2021-01-08，
  
  Published：25 February 2022
- 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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