HCube: A Server-centric Data Center Structure for Similarity Search

The information society is facing a sharp increase in the amount of information driven by the plethora of new applications that sprouts all the time. The amount of data now circulating on the Internet is over zettabytes (ZB), resulting in a scenario defined in the literature as Big Data. In order to handle such challenging scenario, the deployed solutions rely not only on massive storage, memory and processing capacity installed in Data Centers (DC) maintained by big players all over the globe, but also on shrewd computational techniques, such as Big Table, MapReduce and Dynamo. In this context, this work presents a DC structure designed to support the similarity search. The proposed solution aims at concentrating similar data on servers physically close within a DC, accelerating the recovery of all data related to searches performed using a primitive get(k, sim), in which k represents the query identifier, i.e., the data used as reference, and sim a similarity level.