A Hybrid Multi-Head Attention and Residual Correction Model for Elastic Scaling Behavior Learning in Cloud Platforms

ZHAO Nan-nan; YANG Fan; WANG Hao; ZHANG Jia-meng; WU Ruo-fei; ZHAO Tong-xuan; SHI Yu-lu; ZHANG Xiao; ZHAO Xiao-nan; HUANG Zhi-jie; HAN Shu-jie

doi:10.12263/DZXB.20250831

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A Hybrid Multi-Head Attention and Residual Correction Model for Elastic Scaling Behavior Learning in Cloud Platforms

PAPERS | 更新时间：2026-04-24

- A Hybrid Multi-Head Attention and Residual Correction Model for Elastic Scaling Behavior Learning in Cloud Platforms
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4408-4428(2025)
- 作者机构：
  
  1.西北工业大学计算机学院，陕西西安 710129
  2.西北工业大学自动化学院，陕西西安 710129
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62202382;62272394);Guangdong Basic and Applied Basic Research Foundation(2021A1515110080)
- DOI：10.12263/DZXB.20250831
  CLC： TP393;
- Received：24 September 2025，
  
  Accepted：08 December 2025，
  
  Published：25 December 2025
- 稿件说明：
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赵楠楠, 杨帆, 王浩, 等. 面向云平台弹性伸缩的多头注意力-残差修正容器伸缩行为学习模型[J]. 电子学报, 2025, 53(12): 4408-4428.

ZHAO Nan-nan, YANG Fan, WANG Hao, et al. A Hybrid Multi-Head Attention and Residual Correction Model for Elastic Scaling Behavior Learning in Cloud Platforms[J]. Acta Electronica Sinica, 2025, 53(12): 4408-4428.
赵楠楠, 杨帆, 王浩, 等. 面向云平台弹性伸缩的多头注意力-残差修正容器伸缩行为学习模型[J]. 电子学报, 2025, 53(12): 4408-4428. DOI：10.12263/DZXB.20250831

ZHAO Nan-nan, YANG Fan, WANG Hao, et al. A Hybrid Multi-Head Attention and Residual Correction Model for Elastic Scaling Behavior Learning in Cloud Platforms[J]. Acta Electronica Sinica, 2025, 53(12): 4408-4428. DOI：10.12263/DZXB.20250831

摘要

云平台和微服务架构的快速发展，使弹性伸缩成为保障性能与资源效率的核心机制.现有研究虽在负载预测与组合建模方面取得了进展，但大多仍以 CPU、内存等资源利用率为预测目标，再通过阈值规则或控制逻辑间接触发副本调整.这种“预测-控制解耦”的设计带来多重不足：一方面，预测误差在规则映射过程中被放大，难以保证伸缩动作的准确性；另一方面，真实调度器中的滞回、冷却与离散动作机制难以刻画，使得预测结果难以直接落地.因此，直接对伸缩行为进行学习，即建模副本数的动态变化规律，成为一种更具控制导向和部署价值的思路.针对这一问题，本文提出一种多头注意力-残差修正容器伸缩行为学习模型.该模型首先利用自回归滑动平均模型（Auto Regressive Integrated Moving Average，ARIMA）对副本数序列进行趋势分解，再以双向长短期记忆（Bidirectional Long-Short Term Memory，BiLSTM）神经网络对残差序列建模，并引入多头注意力机制（Multi-Head Attention，MHA）自动捕获关键时序特征，通过残差修正（Residual Correction）提升预测精度与健壮性.基于Alibaba公开的cluster-trace-microservices-v2022真实数据集，系统对比了PETformer、SparseTSF、TFEGRU、GRU、Transformer、Seq2Seq-LSTM、Seq2Seq-GRU、Seq2Seq-Transfomer、GRU-LSTM、CNN-LSTM、CNN-LSTM-GRU等主流模型.实验结果表明，所提ARIMA-BiLSTM-MHA组合模型在均方误差（Mean Squared Error，MSE）、均方根误差（Root Mean Squared Error，RMSE）、平均绝对误差（Mean Absolute Error，MAE）、平均绝对百分比误差（Mean Absolute Percentage Error，MAPE）、决定系数（Coefficient of Determination，R²）等核心指标上，较各类基线方法分别取得了1.57%～71.56%（MSE）、0.72%～46.67%（RMSE）、1.57%～59.10%（MAE）、1.97%～60.48%（MAPE）、0.27%~15.70%（R²）的相对百分比性能提升，其中R²最高提升至0.954 3.进一步地，在基于DeathStarBench socialNetwork基准应用构建的容器副本伸缩控制实验中，行为学习驱动的伸缩策略相较于CPU阈值型HPA策略，在将平均 P99 延迟降低2.11%并有效抑制负载跃迁阶段尾延迟尖峰的同时，成功将平均副本数减少约17%，显著缓解了资源过度配置问题.结果证明，该方法能够更准确、稳定地学习伸缩动作并输出直接可用的控制语义，为云平台自动扩缩容提供高效、可靠的决策支持.

Abstract

The rapid development of cloud platforms and microservice architectures has made elastic scaling a critical mechanism for ensuring both performance and cost efficiency. Although prior studies have advanced workload forecasting and hybrid modeling

most approaches still focus on predicting resource utilization (e.g.

CPU or memory) and then mapping forecasts to scaling actions through threshold rules or controller logic. This forecast-control decoupling amplifies prediction errors and fails to capture practical mechanisms such as hysteresis

cooldown

and discrete scaling steps

thereby limiting deployment feasibility. To overcome these limitations

we directly learn scaling behaviors

modeling replica count dynamics as autoscaler control actions. We propose a hybrid model

ARIMA-BiLSTM-MHA

that integrates ARIMA for long-term trend extraction

BiLSTM for residual sequence modeling

multi-head attention for capturing critical temporal dependencies

and residual correction for improving robustness against bursty and non-stationary workloads. We conduct extensive experiments on the real-world Alibaba cluster-trace-microservices-v2022 dataset

where we systematically compare our method with baselines including PETformer

SparseTSF

TFEGRU

GRU

Transformer

Seq2Seq-LSTM

Seq2Seq-GRU

Seq2Seq-Transfomer

GRU-LSTM

CNN-LSTM and CNN-LSTM-GRU. Our results demonstrate that our approach consistently outperforms existing methods

achieving relative improvements of 1.57%~71.56% (MSE)

0.72%~46.67% (RMSE)

1.57%~59.10% (MAE)

1.97%~60.48% (MAPE)

and 0.27%~15.70% (R²)

with R² reaching up to 0.954 3. Furthermore

we conduct container replica autoscaling experiments based on the DeathStarBench socialNetwork benchmark. We show that the behavior learning-driven strategy

compared with the CPU-threshold HPA strategy

successfully reduces the average replica count by approximately 17% while lowering the average P99 latency by 2.11% and effectively suppressing tail-latency spikes during load transitions

thereby significantly mitigating resource over-provisioning. We show that our model can more accurately and stably learn and forecast scaling actions

providing forward-looking decision support for autoscaling in practical cloud environments.

关键词

Keywords

references

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