Proposed flow for this scenario
- Identify limiting resource
- Measure useful work per worker
- Check parallel independence
- Choose size or count change
- Account for replacement
- Validate dependency and recovery
This flow describes a design to evaluate. The local experiment explores one stated mechanism. Its scope appears with the controls; the proposed services are not provisioned.
Decision checkpoints
| Choice | Fits when | Watch for |
|---|---|---|
| Vertical scaling | A worker’s resource constraint can benefit from a suitable larger shape. | Assuming more memory fixes a slow remote dependency. |
| Horizontal scaling | Work can be distributed across replaceable workers. | Keeping required shared state only in one process. |
| Workload redesign | The limiting operation or shared dependency needs a different approach. | Treating scaling as an alternative to diagnosing the bottleneck. |
Use useful completion as the comparison unit
A synthetic QuantumSketch render may require more memory within one native process. LetX may have many independent exports waiting for workers. Those situations suggest different scaling directions, but only evidence of the actual constraint can justify the change.
Record work size and successful completion per worker alongside resource and dependency measurements. A high attempt count or a low average CPU can conceal waiting, retries or skewed tasks. Do not multiply an optimistic average into a promised production capacity.
Vertical changes have compatibility and interruption questions
For real EC2, changing instance type has compatibility requirements. Decide how the application handles any stop, replacement or interruption required by the supported change procedure. A larger worker remains one failure unit unless the broader architecture provides another path.
In this console, selected instance and volume metadata do not execute workloads or benchmark sizes. An EBS capacity modification is a storage workflow; it does not prove CPU or application throughput increases. Read the supported controls before assuming a full instance-resizing wizard exists.
Horizontal changes need a valid work-distribution boundary
Workers need independent or coordinated units of work and durable state outside the replaceable process. A queue or load balancer distributes work, but cannot resolve a shared mutation invariant by itself. Define how a worker starts, becomes useful and stops accepting work.
Scale-in must account for unfinished jobs and active requests. A worker removed before durable completion can require a safe retry. Extra workers also need roles, network access, healthy registration and downstream capacity; desired count is not the same as useful capacity.
Original comparison worksheet
For a concrete comparison, give the synthetic LetX system independent jobs with an assumed drain rate, then add one job that needs a larger memory footprint. Increasing count can help the independent backlog but may not make that one job executable. Increasing size can help the footprint while leaving a shared dependency limit unchanged.
Before a change, write the first observation expected to improve and a stop condition if it does not. Preserve the earlier configuration and the recovery path for in-flight work. That turns scaling into a testable hypothesis rather than an endless sequence of larger resources or higher desired counts.
Changed scenario and bounded experiment
Question: LetX doubles workers but its database connection limit is already reached. Is another horizontal increase justified? Not by the unchanged completion rate. Restore a safe cap and address the dependency or workload design. A larger VM may also leave that same limit unchanged.
Use the backlog experiment to change assumed drain capacity and explain what evidence would justify that input. The local ASG is a bounded teaching model with manual synthetic CPU, not a real autoscaling benchmark. Compare complete cost and failure recovery for the chosen size/count rather than declaring one direction universally cheaper.
Practice the supported console workflow
Predict it. Test it. Change one thing.
Local educational model. No account or cloud charges. Nothing is deployed to AWS.
Constant rates; no polling, retries, batch effects or service quotas. This models work conservation.
sim.shahriarlabs.com · Free to explore
Sources and scope
Reviewed against these official references. The model’s supported scope appears alongside its controls.
- AWS: EC2 instance-type compatibility
- AWS: Auto Scaling architectural benefits
- AWS: resource type, size and count selection