KubeCon + CloudNativeCon North America 2024

At Intuit, our control plane components such as IstioD are responsible for hundreds of applications per cluster. It is responsible for configuring data plane, as well as injecting the istio-proxy container. With an increase in application traffic, there is an increase in application pods, which results in the control plane to scale up. For critical control planes such as IstioD, it is wise to scale proactively, rather than as a reaction to increase in load. With traditional approaches, like tuning HPA thresholds, to scale in advance, we might pre scale even when not required due to outliers, which could be wasteful. At Intuit a novel deep learning forecasting model called N-HiTS was employed to solve this issue. This session will discuss and demo how we train N-HiTS, our most important model features, and how we deploy our service on a per-cluster basis to provide contextualized predictions for cost effective and on time auto-scaling.