Scholarlink

<p>As cloud computing continues to evolve, efficient container orchestration has become critical for optimizing resource utilization and maintaining scalability. This paper addresses the challenges of container placement and autoscaling in cloud environments by proposing a predictive Artificial Intelligence based solution. We utilize a Bidirectional Long Short-Term Memory model to predict CPU and memory utilization of containerized microservices, achieving a Mean Squared Error of 0.00172 and R<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="607_2025_1505_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^2\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> score of 0.7055. We then employ a Random Forest Regressor to predict required container replicas, resulting in an R<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="607_2025_1505_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^2\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> score of 0.9248. Our experimental validation using the Alibaba Cluster Trace dataset confirms that our Bidirectional Long Short-Term Memory approach significantly outperforms traditional techniques and alternative models like Convolutional Neural Network and Temporal Convolutional Network for resource prediction. This integrated approach enables proactive resource management, reducing operational costs and improving application performance through more accurate autoscaling decisions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predictive container orchestration in the cloud using artificial intelligence techniques

  • 期刊论文

  • 发表日期：

    2025年6月16日

查看全文
  • Annette John,
  • Jalal Kawash,
  • Reda Alhajj

摘要

As cloud computing continues to evolve, efficient container orchestration has become critical for optimizing resource utilization and maintaining scalability. This paper addresses the challenges of container placement and autoscaling in cloud environments by proposing a predictive Artificial Intelligence based solution. We utilize a Bidirectional Long Short-Term Memory model to predict CPU and memory utilization of containerized microservices, achieving a Mean Squared Error of 0.00172 and R \(^2\) 2 score of 0.7055. We then employ a Random Forest Regressor to predict required container replicas, resulting in an R \(^2\) 2 score of 0.9248. Our experimental validation using the Alibaba Cluster Trace dataset confirms that our Bidirectional Long Short-Term Memory approach significantly outperforms traditional techniques and alternative models like Convolutional Neural Network and Temporal Convolutional Network for resource prediction. This integrated approach enables proactive resource management, reducing operational costs and improving application performance through more accurate autoscaling decisions.