Kubernetes Pod Autoscaling Optimization Using Long Short-Term Memory Workload Predictors

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DOI https://doi.org/10.55923/aksara.v3i1.2
Ilham Susanto 1 , Doni Panjaitan 2
1, 2Faculty of Social Sciences, Universitas Airlangga
1 ilham.susanto@unair.ac.id , 2 doni.panjaitan@unair.ac.id

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Default Horizontal Pod Autoscaling triggers reactive scaling only after CPU and memory thresholds are breached, causing temporary performance degradation. We integrate an LSTM-driven predictive scaling engine that forecasts load surges five minutes in advance, maintaining SLA guarantees without idle resource waste.
Issue Vol. 3 No. 1 (2024)
Section Articles
Pages 143-156
Published 2024-06-30

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