An Experimental Evaluation of the Kubernetes Cluster Autoscaler in the Cloud

Tamiru, Mulugeta Ayalew; Tordsson, Johan; Elmroth, Erik; Pierre, Guillaume

doi:10.1109/cloudcom49646.2020.00002

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…Tamiru et al [2] show that scaling performance depends heavily on the workload and that using multiple node pools increases performance. Toka et al [3] design an ML-based autoscaler.…”

Section: Related Workmentioning

confidence: 99%

“…K8s employs a horizontal pod autoscaler (HPA) to realize scalable service management. The HPA monitors a predefined metric, e.g., CPU utilization of pods, and tries to meet Quality of Service (QoS) requirements by adding or removing pods, while avoiding over-provisioning [2], [3]. To achieve these goals, the HPA can optimize two parameters:…”

Section: Introductionmentioning

confidence: 99%

“…(1) the frequency of checking conditions to scale; (2) the utilization thresholds at which the HPA scales [2], [3]. If the workload varies, e.g., the application's resource requirements, or the arrival patterns of the requests change, HPA settings are likely to become invalid [2].…”

Section: Introductionmentioning

confidence: 99%

“…Both can only be obtained empirically, and do not directly translate into HPA settings. Obtaining the necessary information and translating them into HPA setting are considered timeconsuming and tedious [2], [3]. III.…”

Section: Introductionmentioning

confidence: 99%

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KAPETÁNIOS: Automated Kubernetes Adaptation through a Digital Twin

Zerwas

Kalmbach

Ursu

et al. 2022

2022 13th International Conference on Network of the Future (NoF)

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This demo presents a self-operating Kubernetes (K8s) cluster that uses digital twinning and machine learning to autonomously adapt its Horizontal Pod Autoscaler (HPA) to workload changes. The demo uses a digital twin of a K8s cluster to gather performance statistics and learn a model for the workload. With the model, the cluster autonomously adjusts HPA parameters for better performance. The demo illustrates this process and shows that the requested pod seconds decrease by ∼37 %, while the request latency stays mostly unaffected.

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“…Tamiru et al [2] show that scaling performance depends heavily on the workload and that using multiple node pools increases performance. Toka et al [3] design an ML-based autoscaler.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

KAPETÁNIOS: Automated Kubernetes Adaptation through a Digital Twin

Zerwas

Kalmbach

Ursu

et al. 2022

2022 13th International Conference on Network of the Future (NoF)

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“…The creators Mulugeta Ayalew Tamiru, Johan Tordsson, Erik Elmroth and group in paper [3], express that 'Kubernetes has arisen as the accepted compartment arrangement stage in the cloud'. In the paper the creators presume that default design (CA) the autoscaler arrangements hubs at the hour of scale-out from just a single hub pool, while when arranged with hub autoprovisioning (CA-NAP) it arrangements hubs from numerous hub pools.…”

Section: Literature Reviewmentioning

confidence: 99%

Infrastructure Optimization in Kubernetes Cluster

L¹,

Mahith²,

B³

2021

JUSST

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Kubernetes is a compact, extensible, open-source stage for overseeing containerized responsibilities and administrations, that works with both decisive setup and robotization. Kubernetes is like VMs, however having loosened up isolation properties to share the Operating System (OS) among the applications. The container conversely with VM, has its own document framework, a portion of Central Processing Unit(CPU), memory, process space, and much more. Kubernetes cluster is a bunch of node machines for running containerized applications. Each cluster contains a control plane and at least one node. Infrastructure Optimization is the process of analyzing and arranging the portion of cloud resources that power applications and workloads to augment the presentation and limit squander due to over-provisioning. In the paper, a “Movie Review System” web application is designed using GoLang for backend components and HTML, CSS, and JS for frontend components. Using AWS, an EC2 instance is created and the web application is deployed onto EC2 and hosted in the instance server. The web application is also deployed on Kubernetes locally using the MiniKube tool. A performance analysis is performed for both the deployments on considering common performance metrics for both AWS EC2 / Virtual Machine (VM) and Kubernetes.

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