  This paper studies two design tasks faced by a geo-distributed cloud data
market: which data to purchase (data purchasing) and where to place/replicate
the data for delivery (data placement). We show that the joint problem of data
purchasing and data placement within a cloud data market can be viewed as a
facility location problem, and is thus NP-hard. 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
in order to develop a near-optimal, polynomial-time algorithm for a
geo-distributed data market. The resulting design, Datum, decomposes the joint
purchasing and placement problem into two subproblems, one for data purchasing
and one for data placement, using a transformation of the underlying bandwidth
costs. We show, via a case study, that Datum is near-optimal (within 1.6%) in
practical settings.
