Joint Data Purchasing and Data Placement in a Geo-Distributed Data Market
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.
© 2016 Copyright held by the owner/author(s). This work is partially supported by NSF grants CNS-1254169, CNS-1319820, NETS-1518941, and BSF grant 2012348.
Submitted - 1604.02533.pdf
Published - p383-ren.pdf