A university is running a DNA decoding project that will take seven years if it runs on its current internal mainframe. The university negotiated a deal with a large cloud provider, which will…

While storage for results is necessary, the primary bottleneck for a seven-year project is the processing itself and the efficient input data transfer, not just the output storage.

A larger pipe for results (egress) is important but secondary to efficiently getting the initial, massive input data into the cloud and minimizing latency during the actual computation.

For a massive DNA decoding project with data originating on-premises, efficiently transferring this large dataset into the cloud environment is a critical initial bottleneck. A closer datacenter minimizes network latency and can facilitate higher bandwidth connections, significantly accelerating data ingestion from the university's mainframe to the cloud for processing.

The question implies the cloud provider is already donating 'any available compute resource,' so the university should focus on enabling that compute with efficient data transfer, not merely reiterating the compute donation.