This paper studies design challenges faced by a geo-distributed cloud data market: which data to purchase (data purchasing) and where to place/replicate the data (data placement). We show that the joint problem of data purchasing and data placement within a cloud data market is NP-hard in general. However, we give a provably optimal algorithm for the case of a data market made up of a single data center, and then generalize the structure from the single data center setting and propose Datum, a near-optimal, polynomial-time algorithm for a geo-distributed data market.
Ren et al. REF focus on the joint problem of data purchasing and data placement in a cloud data market.
Joint Data Purchasing and Data Placement in a Geo-Distributed Data Market
SIGMETRICS '16
Computer Science