Cloudcast — Multi-Cloud Data Transfer Optimization

Broadcast a dataset from a source cloud region to multiple destinations at minimum total cost. The evolved search_algorithm constructs routing topologies (relay trees, Steiner-like structures) that exploit shared intermediate hops across cloud providers.

Based on the Skyplane/Cloudcast system (NSDI'24).

Setup

Download the dataset (network profiles and evaluation configs):
```
cd benchmarks/ADRS/cloudcast
bash download_dataset.sh
```
This downloads:
- profiles/cost.csv — egress cost ($/GB) per region pair
- profiles/throughput.csv — measured throughput (bps) per region pair
- examples/config/*.json — 5 network configurations used for evaluation (intra-AWS, intra-Azure, intra-GCP, inter-cloud)
Set your API key:
```
export OPENAI_API_KEY=...
```

Run

From the repo root:

uv run skydiscover-run \
  benchmarks/ADRS/cloudcast/initial_program.py \
  benchmarks/ADRS/cloudcast/evaluator.py \
  -c benchmarks/ADRS/cloudcast/config.yaml \
  -s [your_algorithm] \
  -i 100

Files

File	Description
`initial_program.py`	Baseline `search_algorithm` function to evolve
`evaluator.py`	Scores programs on total transfer cost across 5 network configs
`config.yaml`	Task-specific config (LLM, evaluator timeout, system prompt)
`simulator.py`	Broadcast cost simulator
`broadcast.py`	`BroadCastTopology` data structure
`utils.py`	Graph construction from profile CSVs
`download_dataset.sh`	Script to download required data files