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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 412-421
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.
INDEX TERMS
energy management systems, low-power electronics, memory architecture, power aware computing, query processing, random-access storage, storage management
CITATION

S. Chandrasekharan and C. Gniady, "QAMEM: Query Aware Memory Energy Management," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 412-421.
doi:10.1109/CCGRID.2018.00068
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