@inproceedings{ffeac1885e584e75bff759a3b13320d3,
title = "QAMEM: Query aware memory energy management",
abstract = "As memory becomes cheaper, use of it has become more prominent in computer systems. This increase in number of memory modules increases the ratio of energy consumption by memory to the overall energy consumption of a computer system. As Database Systems become more memory centric and put more pressure on the memory subsystem, managing energy consumption of main memory is becoming critical. Therefore, it is important to take advantage of all memory idle times and lower power states provided by newer memory architectures by placing memory in low power modes using application level cues. While there have been studies on CPU power consumption in Database Systems, only limited research has been done on the role of memory in Database Systems with respect to energy management. We propose Query Aware Memory Energy Management (QAMEM) where the Database System provides application level cues to the memory controller to switch to lower power states using query information and performance counters. Our results show that by using QAMEM on TPC-H workloads one can save 25% of total system energy in comparison to the state of the art memory energy management mechanisms.",
keywords = "Database, Memory, Memory energy management, Postgresql, SQL queries, TPCH",
author = "Srinivasan Chandrasekharan and Chris Gniady",
note = "Funding Information: VIII. ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation under Grant No. 1551057. Publisher Copyright: {\textcopyright} 2018 IEEE.; 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018 ; Conference date: 01-05-2018 Through 04-05-2018",
year = "2018",
month = jul,
day = "13",
doi = "10.1109/CCGRID.2018.00068",
language = "English (US)",
series = "Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "412--421",
booktitle = "Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018",
}