Energy Efficiency in Main-Memory Databases
As the operating costs of today’s data centres continue to increase and processor manufacturers are forced to meet thermal design power constraints when designing new hardware, the energy efficiency of a main-memory database management system becomes more and more important. Plus, lots of database workloads are more memory-intensive than compute-intensive, which results in computing power being unused and wasted. This can become a problem because wasting computing also means wasting electrical power.
In this paper, we experimentally study the impact of reducing the clock frequency of the processor and the impact of using fewer processor cores on the energy efficiency of common database algorithms such as scans, simple aggregations, simple hash joins, and state-of-the-art join algorithms. We stress the fundamental trade-off between peak performance and energy efficiency, as opposed to the established race-to-idle strategy. Ultimately, we show that reducing unused computing power significantly improves the energy efficiency of memory-bound database algorithms.
This work was supported by the DFG, Collaborative Research Center SFB 876, A2.
- 3.Barroso LA, Hölzle U (2007) The Case for Energy-Proportional Computing. Computer (Long Beach Calif) 40(12):33–37Google Scholar
- 5.Harizopoulos S, Shah MA, Meza J, Ranganathan P (2009) Energy Efficiency: The New Holy Grail of Data Management Systems Research. Biennial Conference on Innovative Data Systems Research (CIDR) January 4-7, 2009. Asilomar, California (arXiv preprint arXiv:0909.1784)Google Scholar
- 6.Intel Corporation (2016) Intel 64 and IA-32 Architectures Software Developer’s Manual – Volume 3BGoogle Scholar
- 9.Psaroudakis I, Kissinger T, Porobic D, Ilsche T, Liarou E, Tözün P, Ailamaki A, Lehner W (2014) Dynamic Fine-grained Scheduling for Energy-Efficient Main-memory Queries. Proc Damon Acm Pp 1(7):1–1Google Scholar
- 10.Schall D, Härder T (2013) Energy-proportional Query Execution Using a Cluster of Wimpy Nodes. Proceedings of the International Workshop on Data Management on New Hardware. ACM, DaMoN ’13, pp 1:1–1:6Google Scholar
- 11.Tsirogiannis D, Harizopoulos S, Shah MA (2010) Analyzing the Energy Efficiency of a Database Server. Proceedings of the SIGMOD International Conference on Management of Data. ACM, SIGMOD ’10, pp 231–242Google Scholar
- 15.Xu Z, Tu Y, Wang X (2010) Exploring Power-Performance Tradeoffs in Database Systems. Proceedings of the International Conference on Data Engineering (ICDE)., pp 485–496Google Scholar