www.cloudworldmodel.ai · 17 listed services · provider site
Simulate a cloud architecture (1-8 resources on AWS, GCP, Azure, OCI or DigitalOcean) under a traffic load and get modeled cost per hour, p50/p95/p99 latency where supported, throughput, error rate, reliability and autos...
Advance a cloud simulation with hybrid ML/rules stepping to evaluate autoscaling and configured resilience behavior.
Inject traffic into a running simulation — provide `targetRps` (absolute RPS) or `deltaPercent` (relative change, e.g. 75 for +75%); omit both only when a random uncontrolled spike is intentional.
Create a cloud infrastructure simulation with either an explicit resource graph or a live scenarioId, plus traffic and autoscaling configuration.
Run a chaos engineering failure injection against a cloud architecture to analyze resilience and recovery.
Fail a specific compute node by `resourceId` or `resourceName`; omit both only when random selection is explicitly intended (non-deterministic).
Clear a single failed resource's reversible failure state (instance_down / database_overload; instance_kill is permanent) so the engine restores it to healthy over the next few steps.
Find lower-cost or higher-performing cloud architecture configurations.
Evaluate equivalent deployments across AWS, Azure, GCP, OCI, and DigitalOcean.
Identify the primary performance bottlenecks in a simulated deployment.
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