1. Storage Modeling for Power Estimation Ronen Kat
Joint work with Miriam Allalouf, Yuriy Arbitman, Michael Factor, Kalman Meth, and Dalit Naor
Scope of work:
Understand and model the power consumption of storage systems
What are we modeling: Disk drives and storage controllers
IBM Haifa Research Lab

2. Outline Motivation Introduction to Power Modeling for Storage
Power Estimation Methodology
Modeling and Validation Framework
Disk Power Model
Controller Array Power Model
Discussion
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3. Information Technology (IT) Accounts for….
Data Centers consumed 180BkWh (2007) - doubling in next 4 years
Over $29B in power & cooling industry wide in 2007
– U.S. Energy Information Administration, IDC
Accounts for 2% of CO2 emissions
Roughly equivalent to aviation industry
IT energy usage will double next 4 years
IBM Haifa Research Lab
Data source: Green IT: A New Industry Shock Wave, Gartner Symposium/ITxpo, October 2007,

4. Datacenter Power Projections
$1.6B
$4.1B
$5.1B
Based on study by the Lawrence Berkeley National Laboratory
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5. Datacenter Power Breakdown
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6. Storage Modeling Motivation
Understand the effect of storage workload on power
Derive application storage (logical volume) power
Capacity planning for power and performance
Understand cooling requirements and utility bills
Avoid over-provisioning
Provide a power estimate in the absence of meters
IBM Haifa Research Lab

7. Previous Works Storage energy reduction works:
Caching and data allocation schemes for spinning down the disks
Dynamic RPM (DRPM) techniques for energy reduction
Storage modeling and measurements:
Disk energy simulators Zedlewski et al. (FAST’03) and Zhang et al. (DAC’07)
Disk energy analysis Hylick et al. (MASCOOT’08)
Utilization modeling Stoess et al. (USENIX’07)
DAC - Design Automation Conference
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8. Outline Motivation Introduction to Power Modeling for Storage
Power Estimation Methodology
Modeling and Validation Framework
Disk Power Model
Controller Array Power Model
Discussion
IBM Haifa Research Lab

9. Introduction to Power Modeling for Storage
Hosts
Disks
Caching
Virtualization
I/O
I/O
I/O
I/O
I/O
I/O
Resiliency
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10. Introduction to Power Modeling for Storage
Storage controller
Hosts
Disks
Cache
RAID
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11. Introduction to Power Modeling for Storage
Storage controller
Hosts
Disks
Cache
RAID
I/O
I/O
I/O
I/O
I/O
Front-end workload
Fixed = static
Dynamic = differential
Controller power is the power of all its components. Non-disk components are fixed.
Back-end workload
Energy consumption breakdown: Fixed power and Dynamic power
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12. Introduction to Power Modeling for Storage
Fixed: Spindle power, electronics
Dynamic: Seek activity, data transfer
Spindle energy is affected by RPM, #platters and platter diameter.
12V 5V
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13. Initial Measurements Results
max power
random read – multiple block sizes
power performance difference
max performance
4K random read
Explain what each curve is
Emphasis on the max performance vs. power
Performance = IOPS
Latency
300GB 15K Enterprise FC disk drive
Power consumption is determined by primitive activities:
Seek activity power (12V)
Data transfer activity power (5V)
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14. Outline Motivation Introduction to Power Modeling for Storage
Power Estimation Methodology
Modeling and Validation Framework
Disk Power Model
Controller Array Power Model
Discussion
IBM Haifa Research Lab

15. Power Estimation Methodology
Workload translation
Translate frontend host workload to backend disk workload
Power tables
A pair of <activity performance, activity power> for each activity type that spans the possible range of performance and power
Interpolation
Estimate power for each workload using interpolation and the power tables
Translation of performance counters
For each storage device type we prepare in advance a data structure of power, named power table.
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16. Power Estimation Methodology (cont)
Workload translation
Storage controller
Back-end workload
Hosts
Disks
Cache
I/O
I/O
I/O
Front-end workload
Read caching
Write caching
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17. Power Estimation Methodology (cont)
Workload translation
Back-end workload
Disks
RAID translation
Cache
I/O
I/O
I/O
Stripe alignment
Block size
Storage controller
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18. Power Estimation Methodology (cont)
Workload translation
Back-end workload
Disks
RAID translation
Write
Cache
I/O
I/O
I/O
Write transaction:
Read, modify, write
RAID5: 2(4), RAID6: 3(6)
Storage controller
IBM Haifa Research Lab

19. Power Estimation Methodology (cont)
Workload translation (summary)
Back-end workload
Disks
RAID translation
Cache
Caching
RAID striping (Arrays I/O distribution)
RAID write transactions
Read and write queuing
Reading queuing is mainly a disk based function
Write queuing is mainly a controller function
Storage controller
IBM Haifa Research Lab

20. Power Estimation Methodology (cont)
Power tables
Span the performance range of the disk
Seek activity <#seek, power>
Data transfer <MBPS, power>
Interpolation
Use the data point in the power tables for each activity and use interpolation to estimate disk power
Assumptions
Uniform distribution over the array disks
Uniform distribution over the disk blocks
Sample performance and power information for each storage device
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21. Outline Motivation Introduction to Power Modeling for Storage
Power Estimation Methodology
Modeling and Validation Framework
Disk Power Model
Controller Array Power Model
Discussion
IBM Haifa Research Lab

22. Modeling and Validation Framework
Measuring single (not RAID) disk power/performance
Performance: Iometer
Power: A 0.1Ω shunt resistor connected to NI PCI-6230 DAQ card (measuring 5V and 12V current)
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23. Disk Power Model: 12V seek cost per I/O
noise
add queuing effect
300GB 15K Enterprise FC disk drive
Concurrency level ~= queue length
Noise appears when the disk has idle time. Below concurrency level of 2.
12V seek power:
Power per seek is determined by the I/O queuing
Dynamic power (total) is computed by the I/O rate
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24. Disk Power Model: 5V data transfer cost
0.3A (1.5W)
300GB 15K Enterprise FC disk drive
5V data transfer power:
Almost constant for small transfer sizes (left graph)
Linear in the amount of data transferred (right graph)
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25. Disk Power Model Validation
300GB 15K
Estimation error: % (average) 6.4% (max)
300GB 10K
Estimation error: % (average) 15% (max)
Estimation was less accurate for low I/O rates.
IBM Haifa Research Lab

26. Modeling and Validation Framework
Measuring storage controller power/performance
Performance: Iometer and controller performance counters
Power: iPDU (IBM C19 PDU+) accuracy: ±5%
Storage: 16x146GB 10K FC disks installed on a mid-range enterprise controller
16 x 146GB 10K
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27. Controller Array Power Model Validation
Write accuracy 4K-64K
Read accuracy
Write accuracy 128K,256K and 512K
We have defined a target bound of 5% estimation accuracy
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28. Power Model Validation using SPC-1 Benchmark
SPC-1 is built around steps and phases
SPC-1 workload
SPC-1 Industry standard benchmark -
Estimation error of up to 2.5%
Power consumption drops with reduction of workload
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29. Outline Motivation Introduction to Power Modeling for Storage
Power Estimation Methodology
Modeling and Validation Framework
Disk Power Model
Controller Array Power Model
Discussion
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30. Discussion Power efficiency metrics
Should use Workload per Watt instead of GB per Watt
The subtle difference between idle power and fixed power
Background activities. E.g., disk scrubbing
Power related events. E.g., fans, battery recharge
We can improve accuracy by integrating access distributions
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31. Discussion (cont)
Power consumption is not monotonically increasing with storage performance
Max power is not consumed at max performance
Array writes are more expensive than reads
Sequential access is more economical than random access
A 4 Watts difference between idle and active disk leads to a 4KWatts difference in a 1000 disk array.
Workload aware power modeling allows fine tuned power/performance planning
Match power to workload performance instead of label power
Remove unnecessary power planning overheads
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32. Questions?
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33. Acknowledgements
Lee La Frese, Joshua Martin, David Whitworth, Jeff Duan, and John Elliott for their ongoing help in studying the storage performance modeling and providing us with power information
We thank George Goldberg, Jonathan Goldberg and Dmitry Sotnikov for running the benchmarks and helping in development
We thank Julian Satran and Al Thomason for many helpful discussions
IBM Haifa Research Lab