Evolutionary data reorganization for efficient workload processing

Author

Razumovskiy, Andrew ; Spivak, Anton ; Nasonov, Denis ; Boukhanovsky, Alexander

Author_Institution

ITMO Univ., St. Petersburg, Russia

fYear

2014

fDate

15-17 Oct. 2014

Firstpage

1

Lastpage

6

Abstract

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.

Keywords

Big Data; data analysis; file organisation; genetic algorithms; Big Data analytics; MapReduce; data replication organization; evolutionary data reorganization; metaheuristic algorithm; query workload optimization; workload processing; Bandwidth; Biological cells; Educational institutions; Genetic algorithms; Gravity; Greedy algorithms; Optimization; MapReduce; genetic algorithm; metaheuristic; optimization; reorganization;

fLanguage

English

Publisher

ieee

Conference_Titel

Application of Information and Communication Technologies (AICT), 2014 IEEE 8th International Conference on

Conference_Location

Astana

Print_ISBN

978-1-4799-4120-9

Type

conf

DOI

10.1109/ICAICT.2014.7035952

Filename

7035952