I want to build a Python-based simulator that shows how application traffic and workload behavior change when Kubernetes settings are adjusted, such as pod counts, replicas, CPU and memory requests or limits, and other deployment or autoscaling parameters. Ideally, it would let me test different traffic patterns without repeatedly deploying everything to a real cloud cluster. Are there existing tools that already provide this kind of simulation, or would I need to combine several tools to build it?
1 Answer
There probably isn’t one tool that models every Kubernetes configuration and traffic effect end to end, but several projects cover important pieces. Goldilocks, VPA in recommendation mode, and kube-capacity can help compare resource requests and limits with real workload usage. For generating traffic against an actual cluster, Locust and Fortio are common choices. There are also academic and open-source discrete-event simulators, including Kubernetes autoscaling simulators, that can test HPA or VPA behavior with synthetic request patterns without provisioning full cloud infrastructure. A practical approach would be to use a simulator for scaling logic, a load generator for realistic traffic, and then add a dashboard to compare the results.
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