Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments
In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/646 |
| _version_ | 1797340164485283840 |
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| author | Dariusz R. Augustyn Łukasz Wyciślik Mateusz Sojka |
| author_facet | Dariusz R. Augustyn Łukasz Wyciślik Mateusz Sojka |
| author_sort | Dariusz R. Augustyn |
| collection | DOAJ |
| description | In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations. |
| first_indexed | 2024-03-08T09:59:05Z |
| format | Article |
| id | doaj.art-a7929f5c276e4feda4e25d59b5cde083 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:59:05Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a7929f5c276e4feda4e25d59b5cde0832024-01-29T13:43:21ZengMDPI AGApplied Sciences2076-34172024-01-0114264610.3390/app14020646Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud DeploymentsDariusz R. Augustyn0Łukasz Wyciślik1Mateusz Sojka2Department of Applied Informatics, Faculty of Automatic Control, Electronics and Computer Sciences, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Applied Informatics, Faculty of Automatic Control, Electronics and Computer Sciences, Silesian University of Technology, 44-100 Gliwice, PolandIndependend Researcher, 43-100 Tychy, PolandIn the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations.https://www.mdpi.com/2076-3417/14/2/646Kubernetesautoscalingautoscalercloud computinghorizontal scalingHPA |
| spellingShingle | Dariusz R. Augustyn Łukasz Wyciślik Mateusz Sojka Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments Applied Sciences Kubernetes autoscaling autoscaler cloud computing horizontal scaling HPA |
| title | Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments |
| title_full | Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments |
| title_fullStr | Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments |
| title_full_unstemmed | Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments |
| title_short | Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments |
| title_sort | tuning a kubernetes horizontal pod autoscaler for meeting performance and load demands in cloud deployments |
| topic | Kubernetes autoscaling autoscaler cloud computing horizontal scaling HPA |
| url | https://www.mdpi.com/2076-3417/14/2/646 |
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