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Scenario
Mesh Traffic Management
Istio canary release for go-api: a 90/10 weighted split between v1 and v2, mesh-level latency fault injection, and STRICT mTLS on the canary workload. Watch the split and faults in mesh telemetry.
NetworkingVerifiedk3dkind
Definition on GitHubWhat you'll do
- Route traffic 90/10 across two go-api versions with an Istio VirtualService
- Inject mesh-level latency without touching the app, and observe the impact
- Enforce STRICT mTLS between meshed workloads and verify it
Stages
- 1canary-rollout
Deploy v1 + v2, split 90/10, and require mTLS
canary - 2fault-injection
Add a mesh-level latency fault to a fraction of requests
latency-fault
Prerequisites
These are installed into the lab cluster for you — listed so you know what the scenario actually depends on.
ingressmeshmonitoring/metricsgo-api
Related scenarios
- Autoscaling Under LoadWatch KEDA scale go-api on Prometheus RPS: a spike drives it from 1 to several replicas, then cooldown brings it back. The flagship 'autoscaling actually works' demo, verified under traffic from the load generator.
- Chaos EngineeringFailure injection with Chaos Mesh — pod kills, network delays, CPU/memory stress, partitions. Correlate chaos experiments with application metrics via Grafana dashboard.
- Cost & Capacity: Right-Sizing Over-Provisioned ResourcesDeploy go-api with deliberately over-provisioned CPU and memory requests, observe the inflated cost in OpenCost, then right-size the requests to a realistic baseline. Checks verify that requests were reduced to the target thresholds while go-api stays healthy under traffic. Teaches the observe-measure-reduce loop that keeps platform costs rational.