budw: energy-efficient parallel storage systems with write-buffer disks
DESCRIPTION
A critical challenge with modern parallel I/O systems is that parallel disks consume a significant amount of energy in servers and high-performance computers. To conserve energy consumption in parallel I/O systems, one can immediately spin down disks when disk are idle; however, spinning down disks might not be able to produce energy savings due to penalties of spinning operations. Unlike powering up CPUs, spinning down and up disks need physical movements. Therefore, energy savings provided by spinning down operations must offset energy penalties of the disk spinning operations. To reduce the penalties incurred by disk spinning operations, we describe in this talk an approach to conserving energy of parallel I/O systems with write buffer disks, which are used to accumulate small writes using a log file system. Data sets buffered in the log file system can be transferred to target data disks in a batch way. Thus, buffer disks aim to serve a majority of incoming write requests, attempting to reduce the large number of disk spinning operations by keeping data disks in standby for long period times. Interestingly, the write buffer disks not only can achieve high energy efficiency in parallel I/O systems, but also can shorten response times of write requests. To evaluate the performance and energy efficiency of our parallel I/O systems with buffer disks, we implemented a prototype using a cluster storage system as a testbed. Experimental results show that under light and moderate I/O load, buffer disks can be employed to significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance.TRANSCRIPT
Energy-Efficient Parallel Storage Systems with Write-Buffer Disks
Xiaojun Ruan and Xiao Qin
Computer Science and Software EngineeringSamuel Ginn College of Engineering
Auburn University
My Research Group: 2011
Xiaojun Ruan
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Overview of the Project
Performance of Secure Disk Systems
[IEEE NAS09]
Energy Efficiency
Security Solid State Drives
Design, Model, Simulate, And Evaluate
Disk Systems with Buffer Disks
[ACM SAC09][ICPP09]
Energy-EfficientDistributed StorageSystems [IPCCC10]
Enhancing Internal Parallelism of SSDs[To Be Submitted11]
BUD
Message Passing Interface with
Enhanced Security[IPCCC 2010]
Energy-Efficient Dynamic Voltage Scaling[ICCCN07]
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Annual Data Center Electricity Usage and Electricity Price increase Every year
Electricity Usage in Data Centers
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• The average power consumption of TOP10 supercomputing systems is 1.32 Mwatt.
Storage
37%
Server
40%
Network 23%
Dell’s Texas Data Center
Energy Efficiency of Supercomputers
Electrical Cost of Data Centers
Using 2010 Trends Scenario◦ Server and Data Centers Consume 110 Billion kWh
per year◦ Assume average commercial end user is charged 9.46
kWh◦ Disk systems can account for 27% of the energy cost
of data centers
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Server and data centers may have an electrical cost of 10.4 billion dollars.
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Energy Consumption of Disks
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Active State: high energy consumption
Power States of Disks
Active
StandbyState transition penalty
Standby State: low energy consumption
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A10000RPM Hard Drive may take 10.9 seconds to wake up!
A Hard Disk Drive
Parallel Disks
Performance
Energy Efficiency
Challanges
Performance Oriented:
• Best Performance
• Huge Electricity Bills
Energy Efficiency Oriented:
• Worst Performance?
• Small Electricity Bills
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Basic Idea of BUD
• Keep Disks in Standby mode as long as possible
• Reduce Status Transitions as many as possible
IBM Ultrastar 36Z15
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Transfer Rate 55 MB/s Spin Down Time: TD 1.5 s
Active Power: PA 13.5 W Spin Up Time: TU 10.9 s
Idle Power: PI 10.2 W Spin Down Energy: ED 13 J
Standby Power: PA 2.5 W Spin Up Energy: EU 135 J
Break-Even Time: TBE 15.2 S
A Parallel Disk System with a Write Buffer Disk
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The BUD Architecture
Data Disks can serve requests without buffer disks when workload is high
Auburn University 16
Sum of Requests in Buffer (SRB)
• SRB is Number of the buffered requests targeting at the same data disk.
• SRB is set by administrators• Once SRB is satisfied, spin up the targeted
data disk, dump all those data, then spin the disk down.
Scheduling Strategy
DynAmic Request Allocatio
n algorithm for Writes
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Put the Request in the Buffer Disk Queue
Yes
Data Disk Availabe?
No
Write the Request into a Buffer Disk
Is theTargeted Data Disk Availabe?
No
Write the Request into Data Disk
Yes
Yes
New Request?
No
To buffer enough requests targeting at the same data disk
Example
Buffer Disk
Requests Queue
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Auburn University Xiaojun Ruan 19
From Design to Simulation
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Simulation Environment
System Parameter. IBM 36Z15 UltraStar IBM 40GNX Travel Star
Rotations Per Minute 10000 RPM 5400 RPM
Working Power 13.5 W 3 W
Standby Power 2.5 W 0.25 W
Spin up Energy 135 Joule 8.7 Joule
Spin down Energy 13 Joule 0.4 Joule
Spin up Time 10.9 sec 3.5 sec
Spin Down Time 1.5 sec 0.5 sec
Transfer Rate 52.8 MB/s 25 MB/s
Auburn University 21
Workloads
Impact of SRB—Low Workload, UltraStar
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Auburn University Xiaojun Ruan 23
Non-Buffer Experiments
Auburn University 24
BUD with IBM 40GNX TravalStar
Buffer Disk Number and Workload-- UltraStar
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Auburn University 26
Energy Consumption
E = Active Energy Consumption + Standby Energy Consumption + Transition Penalty
Auburn University Xiaojun Ruan 27
From Simulation to Real Implementation
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An Energy-Efficient Cluster Storage System
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Implementation (no buffer disks)
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Implementation (with buffer-disks)
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Experimental Design
• Disk Category I/O Node 1
• Data Disk 1: WesternDigital 400, 20GB• Data Disk 2: WesternDigital 400, 20GB
• Disk Category I/O Node 2
• Data Disk 1: WesternDigital 400, 20GB• Data Disk 2: Maxtor D740X-6L, 20GB
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Experimental Design
• Disk Category I/O Node 1• Buffer Disk: Maxtor DiamondMax Plus 9• Data Disk 1: WesternDigital 400, 20GB• Data Disk 2: WesternDigital 400, 20GB
• Disk Category I/O Node 2• Buffer Disk: Seagate Barracuda 7200• Data Disk 1: WesternDigital 400, 20GB• Data Disk 2: Maxtor D740X-6L, 20GB
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34idle time gap is 200sidle time gap is 100s
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GreenFS[ACM EuroSys 2008]
Massive Arrays of Idle Disks[SC 2002]
Popular Data Concentration[ACM ICS 2004]
Previous Research
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http://www.eng.auburn.edu/~xqin
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Auburn University 40
Questions?