On the Fundamental Limits of Coded Data Shuffling

We consider the data shuffling problem, in which a master node is connected to a set of worker nodes, via a shared link, in order to communicate a set of files to the worker nodes. The master node has access to a database of files. In every shuffling iteration, each worker node processes a new subset of files, and has excess storage to partially cache the remaining files. We characterize the exact rate-memory trade-off for the worst-case shuffling under the assumption that cached files are uncoded, by deriving the minimum communication rate for a given storage capacity per worker node. As a byproduct, the exact rate-memory trade-off for any random shuffling is characterized when the number of files is equal to the number of worker nodes. We propose a novel deterministic and systematic coded shuffling scheme, which improves the state of the art. Then, we prove the optimality of our proposed scheme by deriving a matching lower bound and showing that the placement phase of the proposed coded shuffling scheme is optimal over all shuffles.