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.
| Original language | English (US) |
|---|---|
| Title of host publication | 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 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781538658154 |
| DOIs | |
| State | Published - Jul 13 2018 |
| Event | 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018 - Washington, United States Duration: May 1 2018 → May 4 2018 |
Other
| Other | 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 5/1/18 → 5/4/18 |
Fingerprint
Keywords
- Database
- Memory
- Memory energy management
- Postgresql
- SQL queries
- TPCH
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
Cite this
QAMEM : Query aware memory energy management. / Chandrasekharan, Srinivasan; Gniady, Christopher.
Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 412-421.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - QAMEM
T2 - Query aware memory energy management
AU - Chandrasekharan, Srinivasan
AU - Gniady, Christopher
PY - 2018/7/13
Y1 - 2018/7/13
N2 - 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.
AB - 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.
KW - Database
KW - Memory
KW - Memory energy management
KW - Postgresql
KW - SQL queries
KW - TPCH
UR - http://www.scopus.com/inward/record.url?scp=85050979193&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050979193&partnerID=8YFLogxK
U2 - 10.1109/CCGRID.2018.00068
DO - 10.1109/CCGRID.2018.00068
M3 - Conference contribution
AN - SCOPUS:85050979193
SP - 412
EP - 421
BT - Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -