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Find experiments or standard operation that might utilize simulation a bit is not enough.
Look at the idea of cascading failures.
A single failure can propagate.
It makes it difficult simulate to completely.
Why is it difficult with failures to use naive simulation.
And then your thesis proposal is that a digital twin would help out these failures.
The use case that you are specifically looking at is failures.
Then of course you need to introduce failures.
You are not focusing enough on digital twinning.
You should focus more on this than predictive analytics.
Digital twinning is the key -- argumentation and whatnot, not yet faults or predictive analysis.
Opendc-web-server.
All the interesting endpoints are defined in `rests/resources`
Do not use Javalin, use Quarkus, because we use Quarkus in the web module.
To find the documentation and find the web module, find `localhost:8080/q/dev-ui/extensions` or `localhost:8080/q/dev`.
The API models the API everything can do.
We need to bridge the experiment runner to the API.
Some small fixes to the API have to be done.
The API has some schemas defined in the schemas.
Read the documentation of quarkus to add your own endpoints.
Here add your endpoints `opendc-web/opendc-web-server/src/main/java/org/opendc/web/server/rest`
What gets stored in the databse:
- aggregate results are stored there.
- detailed results are in the development tree (the thing that you did with PostgreSQL).
Have a look at the website graphs of OpenDC.
Failure traces are needed to demonstrate failures.
Have a look at Jure's experiments and the cost impact of failures as this might be nice to show in your own work.
Failures tap well into what he is doing.
The degradation model of CPU.
Shows how much money is being lost due to failures.
Daniel cannot model some experiments in the web module.
He would not get results.
There are two different runners for the web module that are different from the generic `ExperimentRunner`.
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