Storage Modeling for Power Estimation Ronen Kat Joint work with Miriam Allalouf, Yuriy Arbitman, Michael Factor, Kalman Meth, and Dalit Naor http://www.haifa.il.ibm.com/projects/storage/pmss/ 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

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

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,

Datacenter Power Projections $1.6B $4.1B $5.1B Based on study by the Lawrence Berkeley National Laboratory IBM Haifa Research Lab

Datacenter Power Breakdown IBM Haifa Research Lab

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

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 IBM Haifa Research Lab

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

Introduction to Power Modeling for Storage Hosts Disks Caching Virtualization I/O I/O I/O I/O I/O I/O Resiliency IBM Haifa Research Lab

Introduction to Power Modeling for Storage Storage controller Hosts Disks Cache RAID IBM Haifa Research Lab

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 IBM Haifa Research Lab

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 IBM Haifa Research Lab

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) IBM Haifa Research Lab

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

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. IBM Haifa Research Lab

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 IBM Haifa Research Lab

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 IBM Haifa Research Lab

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

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

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 IBM Haifa Research Lab

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

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) IBM Haifa Research Lab

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 IBM Haifa Research Lab

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) IBM Haifa Research Lab

Disk Power Model Validation 300GB 15K Estimation error: 1.6 - 2.8% (average) 6.4% (max) 300GB 10K Estimation error: 1.8 - 5.1% (average) 15% (max) Estimation was less accurate for low I/O rates. IBM Haifa Research Lab

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 IBM Haifa Research Lab

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 IBM Haifa Research Lab

Power Model Validation using SPC-1 Benchmark SPC-1 is built around steps and phases SPC-1 workload SPC-1 Industry standard benchmark - www.storageperformance.org Estimation error of up to 2.5% Power consumption drops with reduction of workload IBM Haifa Research Lab

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

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 IBM Haifa Research Lab

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 IBM Haifa Research Lab

Questions? IBM Haifa Research Lab

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