Dynamic Resource Adjustment Operator Based on Autoscaling for Improving Distributed Training Job Performance on Kubernetes
KIPS Transactions on Computer and Communication Systems, Vol. 11, No. 7, pp. 205-216, Jul. 2022
https://doi.org/10.3745/KTCCS.2022.11.7.205, PDF Download:
Keywords: Kubeflow, Kubernetes, Distributed Deep Learning Training, Resource Adjustment Operator
Abstract
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Cite this article
[IEEE Style]
J. Jeong and H. Yu, "Dynamic Resource Adjustment Operator Based on Autoscaling
for Improving Distributed Training Job Performance on Kubernetes," KIPS Transactions on Computer and Communication Systems, vol. 11, no. 7, pp. 205-216, 2022. DOI: https://doi.org/10.3745/KTCCS.2022.11.7.205.
[ACM Style]
Jinwon Jeong and Heonchang Yu. 2022. Dynamic Resource Adjustment Operator Based on Autoscaling
for Improving Distributed Training Job Performance on Kubernetes. KIPS Transactions on Computer and Communication Systems, 11, 7, (2022), 205-216. DOI: https://doi.org/10.3745/KTCCS.2022.11.7.205.