Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (7): 1984-1995.doi: 10.13229/j.cnki.jdxbgxb.20241348

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Adaptive container auto-scaling optimization method in cloud environments

Yu DING1(),Qi-jing YUAN2,Zhen-gong CAI2(),Hai-yu ZUO2,Chen-hao LI1,Bo-wei YANG3   

  1. 1.College of Computer Science and Technology，Zhejiang University，Hangzhou 310027，China
    2.College of Software Technology，Zhejiang University，Ningbo 315000，China
    3.College of Aeronautics and Astronautics，Zhejiang University，Hangzhou 310027，China
  • Received:2024-12-23 Online:2026-07-01 Published:2026-08-12
  • Contact: Zhen-gong CAI E-mail:12021191@zju.edu.cn;cstcaizg@zju.edu.cn

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Abstract:

This paper introduces DEBI-HPA， an adaptive container auto-scaling method for cloud services that integrates Bi-directional Long Short-Term Memory （BiLSTM）， extreme value theory， and dynamic utilization ratios. DEBI-HPA dynamically adjusts the number of Pods based on workload changes， accurately estimates extreme load levels， and adaptively sets target utilization ratios， thereby enhancing service quality， resource utilization， and cost efficiency. It employs predictive scaling during low-load periods to anticipate growth and reactive scaling under high loads to ensure efficient resource allocation. Experimental results demonstrate that DEBI-HPA outperforms Kubernetes' native Horizontal Pod Autoscaler （HPA） by increasing resource utilization by up to 30% and reducing costs by up to 47%. Compared to other predictive scaling methods like ARIMA-HPA and LSTM-HPA， DEBI-HPA improves resource utilization by approximately 18% and lowers costs by about 37%. Additionally， DEBI-HPA achieves effective scaling with common prediction models， reducing dependency on model accuracy. This method offers a novel and robust solution for container auto-scaling in cloud environments.

Key words: auto-scaling, extreme value theory, dynamic target utilization, adaptive algorithm, bidirectional LSTM

CLC Number: 

  • TP399

Fig.1

Working principle of HPA"

Fig.2

HPA pod expansion flowchart"

Fig.3

HPA expansion timing diagram"

Fig.4

Basic structure of the LSTM model［24］"

Fig.5

Structure of the BiLSTM model"

Table 1

Gγ distribution tail shape［28］"

尾部形状范围示例
厚重尾部 P(X>x)≈x-1yy>0Frechet
指数尾部 P(X>x)≈e-xy=0Gamma
有界尾部 PX>x≈0,x≥τy<0Uniform

Fig.6

Overall flowchart of the adaptive elastic scaling strategy"

Fig.7

Data set with strong load periodicity"

Fig.8

Data set with smooth load fluctuations"

Fig.9

Data set with frequent load fluctuations and no clear periodicity"

Fig.10

Changes in the number of Pods for each strategy under periodic load conditions"

Fig.11

Changes in the number of Pods for each strategy under smooth load conditions"

Fig.12

Changes in the number of Pods for each strategy under conditions of frequent and non-periodic load fluctuations"

Table 2

Total resource usage area for different scaling strategies under periodic load conditions"

模型名称S
HPA42 930
BiLSTM-HPA37 200
EVT-BiLSTM-HPA31 860

Table 3

Total resource usage area for different scaling strategies under smooth load conditions"

模型名称S
HPA35 520
BiLSTM-HPA28 620
EVT-BiLSTM-HPA22 290

Table 4

Total resource usage area for different strategies under conditions of non-periodic load fluctuations"

模型名称S
HPA37 260
BiLSTM-HPA28 500
EVT-BiLSTM-HPA25 860

Fig.13

Changes in the number of Pods for each strategy under periodic load conditions"

Fig.14

Changes in the number of Pods for each algorithm under periodic load conditions"

Fig.15

Changes in the number of Pods for each algorithm under conditions of frequent and non-periodic load fluctuations"

Fig.16

Changes in the number of Pods and corresponding load variations under different strategies"

Table 5

Average resource utilization and resource usage area of different models under periodic load conditions"

模型名称平均资源利用率/%S
HPA33.5442 930
ARIMA-HPA34.5041 730
LSTM-HPA35.6439 302
BiLSTM-HPA38.7037 200
EVT-BiLSTM-HPA45.1931 860
DEBI-HPA64.3322 380

Table 6

Table of average resource utilization and resource usage area of different models under smooth load conditions"

模型名称平均资源利用率/%S
HPA30.8135 520
ARIMA-HPA33.0733 090
LSTM-HPA35.8730 510
BiLSTM-HPA38.2328 620
EVT-BiLSTM-HPA49.0922 290
DEBI-HPA52.4820 850

Table 7

Table of average resource utilization and resource usage area of different scaling models under conditions of frequent and non-periodic load fluctuations"

模型名称平均资源利用率/%S
HPA29.7837 260
ARIMA-HPA32.4234 230
LSTM-HPA33.2133 420
BiLSTM-HPA38.9428 500
EVT-BiLSTM-HPA42.9125 860
DEBI-HPA48.3823 790
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