BEGINNER · 12 MIN · FREE LESSON
EC2 & Compute Engine
Explore ec2 & compute engine through a visual, step-by-step model.
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BEGINNER · 12 MIN · FREE LESSON
Explore ec2 & compute engine through a visual, step-by-step model.
Loading interactive lesson…
Edit requests/second, replica count, per-replica CPU, cache hit target and a timed failure. One tick is one modeled second. Capacities and latency are illustrative model assumptions, not cloud benchmarks.
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Set explicit workload, capacity, failure-domain and queue assumptions.
Adjust app replicas and per-replica CPU work capacity. Requests enter the app pool, then the separately limited database; queues, rejected requests and completions are all accounted for. The AWS/GCP toggle changes component mappings, not a universal hardware performance claim. The simulator costs O(T·R) for T ticks and R offered requests per tick, using bounded FIFO queues. Results are deterministic workload-model outcomes, not production latency estimates.
Requests processed at the app tier, seed 42. Plotted sample sizes respect each lesson’s explicit input limits.
Keyboard: Space play/pause · ←/→ step · R restart · 1–5 speed. Reduced motion disables transitions.
A virtual machine contributes a finite processing budget; adding machines does not repair every downstream failure.
Choose AWS or GCP mapping, change one validated model parameter and load the trace. Provider labels identify concepts; the model never provisions resources or calls a provider account.
Adjust app replicas and per-replica CPU work capacity. Requests enter the app pool, then the separately limited database; queues, rejected requests and completions are all accounted for. The AWS/GCP toggle changes component mappings, not a universal hardware performance claim.
AWS: EC2 / Compute Engine virtual machines. GCP: EC2 / Compute Engine virtual machines. EC2 and Compute Engine expose different instance families, images, disks and billing choices. The model uses assumed work units, not measured vCPU throughput or current pricing. OS patching, image policy, IAM and network configuration remain part of VM operation.
Overload one replica, add CPU or replicas, then fail the database. Explain why application scaling stops helping once the shared dependency is the bottleneck. Predict the next modeled decision before advancing.
Use the displayed measured operation and explicit bounds. Simulator operations, service quotas, network latency and cloud billing are different measurements. Validate production capacity using the selected product and workload.
The four references implement the core local state model. Provider annotations and UI controls surround that model; the snippets do not authenticate, provision resources or replace a vendor SDK. Worked saga references assume reliable local compensation.
Use the scenario to explain access patterns, ownership, failure policy and the operational assumptions behind a cloud service choice.
EC2 and Compute Engine expose different instance families, images, disks and billing choices. The model uses assumed work units, not measured vCPU throughput or current pricing. OS patching, image policy, IAM and network configuration remain part of VM operation.
Explain one concrete outcome, one provider distinction and one unmodeled production concern.
MAKE IT STICK
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