Data transfer is slow -- so in AI and HPC, data locality matters. As workloads scale, optimizing where and how to run data-heavy workloads in Kubernetes becomes critical. Yet this area remains underexplored. The CNCF Batch Subproject shares findings from our work on data-locality-aware scheduling across clusters. Should we move compute to the data or the data to compute? What are the trade-offs in latency, cost, and efficiency?

We present methods to test potential policies: splitting jobs, exposing location-aware metadata from compute/storage, and basing scheduling on historical data and pricing. We share early discoveries from real-world tests across regions with limited bandwidth, storage, and power.

If your workloads are bottlenecked by data gravity -- or you’re chasing GPU efficiency across sites -- join us to explore emerging patterns for intelligent, cost-aware data placement in Kubernetes.