QAMEM query aware memory energy management

• Published in: 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) pp. 412 - 421
• Main Authors: Chandrasekharan, Srinivasan, Gniady, Chris
• Format: Conference Proceeding
• Language: English
• Published: Piscataway, NJ, USA IEEE Press 01.05.2018; IEEE
• Series: ACM Conferences
• Subjects: Database systems; Delays; Energy consumption; Energy management; memory; memory energy management; Memory management; postgresql; Servers; sql queries; TPCH
• ISBN: 1538658151; 9781538658154
• DOI: 10.1109/CCGRID.2018.00068

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.