View article

Efficiently analyzing big data is a major issue in our current era. Examples of analysis tasks include identification or detection of global weather patterns, economic changes, social phenomena, or epidemics. The cloud computing paradigm along with software tools such as implementations of the popular MapReduce framework offer a response to the problem by distributing computations among large sets of nodes. In many scenarios, input data are, however, geographically distributed (geodistributed) across data centers, and straightforwardly moving all data to a single data center before processing it can be prohibitively expensive. Above-mentioned tools are designed to work within a single cluster or data center and perform poorly or not at all when deployed across data centers. This paper deals with executing sequences of MapReduce jobs on geo-distributed data sets. We analyze possible ways of executing …