A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction

With the rapid development and application of container cloud computing-related technologies, more and more applications are being deployed to container cloud clusters. As an essential feature of container cloud platforms and cloud-native architecture, auto-scaling aims to automatically and quickly...

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Main Authors: Yu Ding, Chenhao Li, Zhengong Cai, Xinghao Wang, Bowei Yang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10786189/
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author Yu Ding
Chenhao Li
Zhengong Cai
Xinghao Wang
Bowei Yang
author_facet Yu Ding
Chenhao Li
Zhengong Cai
Xinghao Wang
Bowei Yang
author_sort Yu Ding
collection DOAJ
description With the rapid development and application of container cloud computing-related technologies, more and more applications are being deployed to container cloud clusters. As an essential feature of container cloud platforms and cloud-native architecture, auto-scaling aims to automatically and quickly adjust the allocation of cloud resources according to the resource requirements of applications. Currently, widely used responsive auto-scaling methods, such as Kubernetes HPA, exhibit certain lags due to the startup time costs of containers and Pods. This lag makes it difficult to guarantee the service quality of applications when there is a sudden increase in online application load. This paper proposes a dynamic interval auto-scaling optimization method based on Informer time series prediction. By predicting online application load and dynamically determining the auto-scaling interval, sufficient resources are allocated to the application in advance. In the experiments conducted on the official World Cup forum load and Alibaba cluster CPU load, the Informer time series prediction algorithm demonstrated better long-sequence time series prediction capabilities compared to algorithms such as LSTM and RNN. In elastic scaling experiments, compared to Kubernetes HPA, the method proposed in this paper reduces the average application response time from 0.821 seconds to 0.692 seconds, and the SLA violation rate decreases from 18.277% to 9.157%. This indicates a significant improvement in the service quality metrics of online applications. Furthermore, the proposed method effectively maintains a balance between high CPU resource utilization and low application response time and SLA violation rate, which is something RNN-based elastic scaling method cannot achieve, as it can only reduce application response time and SLA violation rate by sacrificing CPU resource utilization.
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institution Kabale University
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spelling doaj-art-f56a5ee1cf3e411ebcb5b5702903445c2025-01-25T00:01:17ZengIEEEIEEE Access2169-35362025-01-0113145721458310.1109/ACCESS.2024.351356410786189A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series PredictionYu Ding0Chenhao Li1Zhengong Cai2https://orcid.org/0000-0001-5003-6085Xinghao Wang3https://orcid.org/0009-0005-6772-3072Bowei Yang4College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, ChinaSchool of Software Technology, Zhejiang University, Ningbo, Zhejiang, ChinaSchool of Software Technology, Zhejiang University, Ningbo, Zhejiang, ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou, Zhejiang, ChinaWith the rapid development and application of container cloud computing-related technologies, more and more applications are being deployed to container cloud clusters. As an essential feature of container cloud platforms and cloud-native architecture, auto-scaling aims to automatically and quickly adjust the allocation of cloud resources according to the resource requirements of applications. Currently, widely used responsive auto-scaling methods, such as Kubernetes HPA, exhibit certain lags due to the startup time costs of containers and Pods. This lag makes it difficult to guarantee the service quality of applications when there is a sudden increase in online application load. This paper proposes a dynamic interval auto-scaling optimization method based on Informer time series prediction. By predicting online application load and dynamically determining the auto-scaling interval, sufficient resources are allocated to the application in advance. In the experiments conducted on the official World Cup forum load and Alibaba cluster CPU load, the Informer time series prediction algorithm demonstrated better long-sequence time series prediction capabilities compared to algorithms such as LSTM and RNN. In elastic scaling experiments, compared to Kubernetes HPA, the method proposed in this paper reduces the average application response time from 0.821 seconds to 0.692 seconds, and the SLA violation rate decreases from 18.277% to 9.157%. This indicates a significant improvement in the service quality metrics of online applications. Furthermore, the proposed method effectively maintains a balance between high CPU resource utilization and low application response time and SLA violation rate, which is something RNN-based elastic scaling method cannot achieve, as it can only reduce application response time and SLA violation rate by sacrificing CPU resource utilization.https://ieeexplore.ieee.org/document/10786189/Auto-scalingcontainer clouddynamic intervalinformertime series prediction
spellingShingle Yu Ding
Chenhao Li
Zhengong Cai
Xinghao Wang
Bowei Yang
A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction
IEEE Access
Auto-scaling
container cloud
dynamic interval
informer
time series prediction
title A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction
title_full A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction
title_fullStr A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction
title_full_unstemmed A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction
title_short A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction
title_sort dynamic interval auto scaling optimization method based on informer time series prediction
topic Auto-scaling
container cloud
dynamic interval
informer
time series prediction
url https://ieeexplore.ieee.org/document/10786189/
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AT xinghaowang adynamicintervalautoscalingoptimizationmethodbasedoninformertimeseriesprediction
AT boweiyang adynamicintervalautoscalingoptimizationmethodbasedoninformertimeseriesprediction
AT yuding dynamicintervalautoscalingoptimizationmethodbasedoninformertimeseriesprediction
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AT zhengongcai dynamicintervalautoscalingoptimizationmethodbasedoninformertimeseriesprediction
AT xinghaowang dynamicintervalautoscalingoptimizationmethodbasedoninformertimeseriesprediction
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