Evolutionary data reorganization for efficient workload processing

Digital data universe size is exponentially growing up from year to year and currently is estimated to be more than 4.4 Zb. It compels scientific community to found out more efficient approaches in collecting, organizing and processing of information. A lot of enterprise solutions offer extended software tools based on MapReduce principles for big data analytics. One of the required parts of MapReduce solutions is data replication organization which permanently helps to increase safety and to provide increased performance. In this paper we investigate the possibility of applying queries workload optimization using metaheuristic algorithm for data dynamic reorganization according to executed tasks influence in MapReduce-based storages.