Compute sylvactl/unitTimeout based on node count for neuvector

What does this MR do and why?

The neuvector-enforcer-pod DaemonSet uses a sequential rolling update strategy (maxUnavailable: 1), meaning upgrade time scales linearly with the number of nodes.

Replaced the hardcoded 15m timeout with a formula that multiplies an estimated per-node time (5 minutes) by the number of nodes.

The 5 minutes estimate is a middle ground between the observed upgrade time (~1 min per pod in CI runs) and the configured terminationGracePeriodSeconds (1200s = 20 min).

Related reference(s)

Closes #4162 (closed)

Test coverage

The fix was validated on a management cluster with 4 nodes. The computed timeout was confirmed to be 1200s (5 min x 4 nodes) via the sylvactl/unitTimeout annotation on the neuvector HelmRelease. All 4 enforcer pods were observed to be Running and Ready, with each pod becoming ready in 13-34 seconds during initial deployment. It was tested in the CI too, with a branch where it was needed to change the flavor to B1.large24 and added a node.

CI configuration

Below you can choose test deployment variants to run in this MR's CI.

Click to open to CI configuration

Legend:

Icon	Meaning	Available values
☁️	Infra Provider	capd, capo, capm3
🚀	Bootstrap Provider	kubeadm (alias kadm), rke2, okd, ck8s
🐧	Node OS	ubuntu, suse, na, leapmicro
🛠️	Deployment Options	Deployment option list and description
🎬	Pipeline Scenarios	Available scenario list and description
🟢	Enabled units	Any available units name, by default apply to management and workload cluster. Can be prefixed by mgmt: or wkld: to be applied only to a specific cluster type
🔴	Disabled units	Any available units name, by default apply to management and workload cluster. Can be prefixed by mgmt: or wkld: to be applied only to a specific cluster type
🏗️	Target platform	Can be used to select specific deployment environment Available platform list and description
⚡	Pipeline control	autorun, manual or blocking. Can be used to override global config and start a deployment pipeline the required way

• 🎬 preview ☁️ capd 🚀 kadm 🐧 ubuntu

• 🎬 preview ☁️ capo 🚀 rke2 🐧 suse

• 🎬 preview ☁️ capm3 🚀 rke2 🐧 ubuntu

• ☁️ capd 🚀 kadm 🛠️ light-deploy 🐧 ubuntu

• ☁️ capd 🚀 rke2 🛠️ light-deploy 🐧 suse

• ☁️ capo 🚀 rke2 🛠️ ha 🐧 suse 🟢 neuvector

• ☁️ capo 🚀 rke2 🐧 leapmicro

• ☁️ capo 🚀 kadm 🐧 ubuntu

• ☁️ capo 🚀 kadm 🐧 ubuntu 🟢 neuvector,mgmt:harbor

• ☁️ capo 🚀 rke2 🎬 rolling-update 🛠️ ha 🐧 ubuntu

• ☁️ capo 🚀 kadm 🎬 wkld-k8s-upgrade 🐧 ubuntu

• ☁️ capo 🚀 rke2 🎬 rolling-update-no-wkld 🛠️ ha 🐧 suse

• ☁️ capo 🚀 rke2 🎬 sylva-upgrade 🛠️ ha 🐧 ubuntu

• ☁️ capo 🚀 rke2 🎬 sylva-upgrade-from-1.6.x 🛠️ ha,misc 🐧 ubuntu

• ☁️ capo 🚀 rke2 🛠️ ha 🟢 neuvector 🎬 sylva-upgrade 🐧 ubuntu

• ☁️ capo 🚀 rke2 🛠️ misc 🐧 ubuntu 🟢 mgmt:harbor 🔴 neuvector

• ☁️ capo 🚀 rke2 🛠️ ha,misc,openbao🐧 suse

• ☁️ capo 🚀 rke2 🐧 suse 🎬 upgrade-from-prev-tag

• ☁️ capm3 🚀 rke2 🐧 suse

• ☁️ capm3 🚀 kadm 🐧 ubuntu

• ☁️ capm3 🚀 ck8s 🐧 ubuntu

• ☁️ capm3 🚀 kadm 🎬 rolling-update-no-wkld 🛠️ ha,misc 🐧 ubuntu

• ☁️ capm3 🚀 rke2 🎬 wkld-k8s-upgrade 🛠️ ha 🐧 suse

• ☁️ capm3 🚀 kadm 🎬 rolling-update 🛠️ ha 🐧 ubuntu

• ☁️ capm3 🚀 rke2 🎬 upgrade-from-prev-release-branch 🛠️ ha 🐧 suse

• ☁️ capm3 🚀 rke2 🛠️ misc,ha 🐧 suse

• ☁️ capm3 🚀 rke2 🎬 sylva-upgrade 🛠️ ha,misc 🐧 suse

• ☁️ capm3 🚀 kadm 🎬 rolling-update 🛠️ ha 🐧 suse

• ☁️ capm3 🚀 ck8s 🎬 rolling-update 🛠️ ha 🐧 ubuntu

• ☁️ capm3 🚀 rke2|okd 🎬 no-update 🐧 ubuntu|na

• ☁️ capm3 🚀 rke2 🐧 suse 🎬 upgrade-from-release-1.5

• ☁️ capm3 🚀 rke2 🐧 suse 🎬 upgrade-to-main

Global config for deployment pipelines

• autorun pipelines
• allow failure on pipelines
• record sylvactl events

Notes:

• Enabling autorun will make deployment pipelines to be run automatically without human interaction
• Disabling allow failure will make deployment pipelines mandatory for pipeline success.
• if both autorun and allow failure are disabled, deployment pipelines will need manual triggering but will be blocking the pipeline

Be aware: after configuration change, pipeline is not triggered automatically. Please run it manually (by clicking the run pipeline button in Pipelines tab) or push new code.