Upload
paula-hunt
View
217
Download
0
Tags:
Embed Size (px)
Citation preview
Analyzing the Energy Efficiency of a Database Server
Hanskamal Patel
SE 521
Article
• Analyzing the Energy Efficiency of a
Database Server
– Dimitris Tsirogiannis – University of
Toronto
– Stavros Harizopoulos – HP Labs
–Mehul A. Shah – HP Labs
Introduction
• Evaluating database system in terms of performance is
measured in task per second or queries per second.
• Similarly, energy-efficiency is determined by the
measure of completed task per energy/Queries per
Joule.
• Improving performance is hardware/platform oriented
or workload-management oriented.
• Exploring ways to improve energy efficiency of a single-
machine database server.
Test Machine ConfigurationComponent Min (W) Max (W)
Two Intel Xenon E5430 Quad Core 2.66 GHz 48 W 160 W
Four 4GB FB-DIMMS (RAM) 40 W 40 W
Three 300 GB Seagate Savvio 10k.3 2.5” 14W 24W
Four 64 GB Intel X-25E 2.5’ (SSD) 0.2 W 10W
System board components 54W 54W
Power Breakdown• About half of the peak power
is idle system
– Two CPU’s
– Fixed RAM Power
– Board components
– SDD and HDD Minimal Power
• Left side of the chart is active
power consumption
– CPU is dominant component
– SSD and HDD draw similar
power
CPU Usage vs. Power
What affects energy efficiency?
• EE = Work/Energy = Performance/Power
• Several options affect power-use and potentially
affect energy efficiency
– CPU cycles to fetch data from disk
– Scans, record access, compressions, sorting, and
joining
• Energy efficiency can be improved but it may
sacrifice performance
Energy efficiency vs. Performance
• Experimented with five different overhead
kernels
– Parallel performing, cache-conscious hash join,
sorting, alphasort and parallel merging
• High performance storage engine that supports
column and row oriented database scans.
• PostgreSQL and System-X DBMS
Performance vs. Energy
Performance vs. Energy
Assembling data-management architectures
• Scale-up
– Shared memory and shared disk
– Choosing the balance of components and power down
unneeded resources
• Scale-out
– Share nothing
– Single node configurations connected by scaled network
– Choose energy efficient components for one node and
performance optimized for another
Power Profiles of Hardware Components
• RAM
– RAM is responsible for 20% of the power
consumption and stays the same
throughout
– Only way to vary power usage by
memory is to physically remove the
modules from the board
Power Profiles of Hardware Components
• Disks
– Both HDD and SSD in the configuration
– Supports active and idle stages, consuming
different amount of power – 15% in the active
stage
• Test Configuration
– Raid-0 configuration for both HDD and HDD
– Reading 100GB file @ block size of 128KB
Power Consumption of Disks
Power Profiles of Hardware Components
• CPU
– The two CPU’s are responsible for the 85% of power
increase in the system while active
– Interested in understanding:
• How CPU power is affected by database operations and the
efficacy of hardware and software power management
• Developed a set of micro-benchmarks that performs three
classes of database operations: hashing, sorting, and scans.
Micro-benchmarks
• Custom Join Kernel
– Hash join algorithm for computing join of two
relations in parallel.
• Sort Kernel
– Two in-memory parallel sorting algorithm
• Scan kernel
– Scan uncompressed rows in memory
– Scan compressed column on disk
Analyzing Power Consumption
Memory bus utilization
Hashjoin Operator
Sort Operator
Scan Operator
Energy vs. Performance
• Parameters that have greatest
impact on energy
– Algorithm/plan selection
– Intra-operator parallelism
– Inter-query parallelism
Algorithm/Plan selection
• Access Methods
• Join Algorithms
• Complex Queries and Join Ordering
Intra-operator and Inter-query Parallelism
• Intra-operator parallelism
– Parallel hash join
– Parallel Sorts
• Inter-query parallelism
– Executing multiple queries at the same
time
Implications for Database Computing
• One size fits all
– Collection of nodes, where each node is optimized for
specific task
– High parallelism, low-frequency, small cache, and simple
design CPU
– Solid state drives
• Shared nothing, everything, or in-between
– Shared nothing and shared disk
• Controlling peak power
Conclusion
• CPU power usage by different operators can vary by
up to 60%
• The best performing system was the most energy
efficient
• Future investigations:
– Improving resources across unutilized nodes to save
power
– Alternative energy efficient hardware for lower fixed-
power cost
Questions?