From-Scratch Build · 05 · Data & Autoscaling
What happens to an edge service when you keep adding robots? This is the dataset that answers it — and the machine-learning angle on top: teach a model to predict when a digital-twin service is about to break its SLA, so it can scale before it does.
What it is
This is the sibling project to the Robot-Arm Digital Twin. Where that one was the system, this is the measurement: a dataset captured while progressively spinning up more robot-manipulator instances against the same edge-based digital-twin service, watching how resource use and responsiveness degrade as load climbs.
It comes in three flavours that differ only in how aggressively new robots arrive — and a labelled version exists to train a classifier, which is where the project stops being "a CSV" and becomes a question about intelligent autoscaling.
The stack
The robot-arm twin service under test — the thing being scaled and measured.
Resource and performance metrics logged as robot instances are added at fixed intervals.
A classifier trained on the labelled big dataset — an ensemble of decision trees that votes on the system's state.
Service-Level Agreements set the threshold; the model predicts breaches so scaling can act in time.
The payoff: scale the service up/down based on a prediction, not a lagging alarm.
The same prediction can be offered as a network service — autoscaling intelligence delivered on demand rather than hand-tuned.
How it works
Choose micro / small / big — i.e. how fast new robot instances join the load.
Spin up instances on schedule, logging resource and timing metrics throughout the run.
Tag samples (e.g. SLA-met vs. at-risk) to create the supervised target for the big dataset.
Fit a Random Forest to map live telemetry to a predicted SLA state.
In deployment, the model flags imminent breaches so the orchestrator scales ahead of failure.
Reflection