TY - GEN
T1 - Analyzing power and energy consumption of large join queries in database systems
AU - Rodriguez, Miguel
AU - Jabba, Daladier
AU - Zurek, Eduardo E.
AU - Salazar, Augusto
AU - Wightmam, Pedro
AU - Barros, Alber
AU - Nieto, Wilson
PY - 2013
Y1 - 2013
N2 - Performance of large join queries has been widely addressed in the database literature, explicitly the problem of finding a join order that minimizes the execution cost of a given query has been treated. Lately, the elevated electricity consumption of data center facilities have lead to the development of energy-efficient hardware and software. Relational Database Management Systems (RDBMS) have been redesigned to predict power consumption of queries and guide plan selection towards energy reduction goals. In this article, a power consumption comparison between different large join query optimization approaches will be presented. For that purpose, three large join query optimization algorithms will be used, the PostgreSQL genetic algorithms GEQO, a simulated annealing approach SAIO and an automata theory based meta-heuristic DSQO our previous work. The main goal of our research is to analyze the power behavior of different large join query optimizers solving queries derived from the TPC-DS benchmark.
AB - Performance of large join queries has been widely addressed in the database literature, explicitly the problem of finding a join order that minimizes the execution cost of a given query has been treated. Lately, the elevated electricity consumption of data center facilities have lead to the development of energy-efficient hardware and software. Relational Database Management Systems (RDBMS) have been redesigned to predict power consumption of queries and guide plan selection towards energy reduction goals. In this article, a power consumption comparison between different large join query optimization approaches will be presented. For that purpose, three large join query optimization algorithms will be used, the PostgreSQL genetic algorithms GEQO, a simulated annealing approach SAIO and an automata theory based meta-heuristic DSQO our previous work. The main goal of our research is to analyze the power behavior of different large join query optimizers solving queries derived from the TPC-DS benchmark.
KW - energy consumption
KW - large join queries
KW - query optimization
KW - relational database systems
UR - http://www.scopus.com/inward/record.url?scp=84897691367&partnerID=8YFLogxK
U2 - 10.1109/ISIEA.2013.6738985
DO - 10.1109/ISIEA.2013.6738985
M3 - Conference contribution
AN - SCOPUS:84897691367
SN - 9781479911257
T3 - ISIEA 2013 - 2013 IEEE Symposium on Industrial Electronics and Applications
SP - 148
EP - 153
BT - ISIEA 2013 - 2013 IEEE Symposium on Industrial Electronics and Applications
T2 - 2013 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2013
Y2 - 22 September 2013 through 25 September 2013
ER -