Energy Efficient Prefetching – from models to Implementation

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Adam Manzanares and Xiao Qin

Department of Computer Science and Software Engineering

Auburn Universityhttp://www.eng.auburn.edu/~xqin

xqin@auburn.edu

Adam Manzanares

Ph.D. May 2010.

About me

Ph.D.’04, U. of Nebraska-Lincoln

04-07, New Mexico Tech 07-10, Auburn University

About My Research Group

Presentation Outline

• Motivation

• Modeling Work

• DiskSim Modifications

• Energy Efficient Virtual File System (EEVFS)

• Parallel Striping Groups in EEVFS

• Conclusion

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MotivationEPA Report to Congress on Server and Data Center Energy Efficiency, 2007

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Motivation Using 2010 Historical 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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Buffer Disk Architecture

RAM BufferRAM Buffer

m buffer disksm buffer disks n data disksn data disks

Buffer Disk ControllerBuffer Disk Controller

Data Partitioning Data Partitioning

Security Model Security Model

Load BalancingLoad Balancing

Power ManagementPower Management

PrefetchingPrefetching

Disk RequestsDisk Requests

Energy-Related Reliability Model Energy-Related Reliability Model

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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

Prefetching

Disk 1

Disk 2

Disk 3

Buffer Disk

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Why Modeling & Simulation

• Allows us to determine the potential of our research ideas

• Can quickly evaluate many simulation parameters

• Allows us to test architectures and hardware without having the physical resources

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Modeling & Simulation Work

Developed Mathematical Model◦Disk Energy Consumption◦Conditions to prefetch

Developed Energy Saving Principles◦Investigated cases that exploit the energy

saving principles Implemented model in JAVA based simulator

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Energy Saving Principles

Energy Saving Principle One◦Increase the length and number of idle

periods larger than the disk break-even time TBE

Energy Saving Principle Two◦Reduce the number of power-state

transitions

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Paramaters TestedParameter Values

Data Size 1, 5, 10, 25 MB

# of Data Disks 4, 8, 12

Inter-arrival Delay 0, 0.1, 0.5, 1 S

Hit Rate 85, 90, 95, 100%

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Energy Savings Hit Rate 85%

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State Transitions

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Parameter Generalizations

• Larger data sizes produce greater energy savings and less state transitions

• Increasing the inter-arrival delay increases energy savings

• More data disks per buffer disks increases energy efficiency

• High hit rates produce the greatest energy efficiency

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Modeling & Sim. Summary

Hit Rate, Inter-arrival Delay, & Data Size combine to produce Idle Windows

Transitions important to reduce energy consumption◦ May increase/decrease to reduce energy consumption

Disk parameters have large impact on energy savings

Model and simulator developed in-house

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DiskSim

• Event driven simulator developed at CMU

• Simulates disks at the block level

• The simulator has been validated

• Discrete event based simulator

• Provides a large amount of statistics

• Lacks Disk Power Models

• Ability to simulate large storage systems

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File System Simulator

• Large files important to energy savings

• Popularity of data is also useful

• Developed a block to file translator

• Interacts with DiskSim

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DiskSim with File System Simulator

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Modified DiskSim Results

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Modified DiskSim Summary

• Provides us with accurate disk statistics

• Only the changes to DiskSim need to be validated

• Heavily dependent upon disk parameters

• May miss details that can only be found in implementation

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Why a Cluster File System

• Block level prefetching difficult

• Natural place to track file accesses

• Control placement of data among storage nodes, and data disks

• Tiered approach simplifies management of files and disk states

• Eliminates some shortcomings of modeling and simulation

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Energy Efficient Virtual File System

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EEVFS Process Flow

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EEVFS Testbed

Parameter Storage Server Storage Node Type 1

Storage Node Type 2

CPU P4 2.0 GHz P4 3.2 GHz P4 2.4 GHz

Memory (MB) 2000 1000 512

Network Interconnect

1000 1000 100

Disk Type SATA ATA/133 ATA/133

Disk Capacity 120 GB 80 GB 80 GB

Disk Bandwidth 100 MB/s 58 MB/s 34 MB/s

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Energy Savings

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State Transitions

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Response Times

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Berkeley Web Trace

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EEVFS Summary

• Knowledge of requests assumed and may be hard to come by

• Performance tied to one of the buffer disks

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Parallel Striping Groups

Disk 1 Disk 2

Group 1

Buffer Disk

Storage Node 1

Disk 3 Disk 4Buffer Disk

Storage Node 2

Disk 5 Disk 6

Group 2

Buffer Disk

Storage Node 3

Disk 7 Disk 8Buffer Disk

Storage Node 4

File 1 File 2File 3 File 4

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Striping Within a Group

Disk 1 Disk 2

Group 1

Buffer Disk

Storage Node 1

Disk 3 Disk 4Buffer Disk

Storage Node 2

1 3 5 7 9 4 6 8

4 6 81 3 5 7 9

10

10

1

2

1

2

File 1 File 22 2

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Striping Within a Group

• Number of disks in a group can be matched to nearest bottleneck

• Striping within the group maintains relatively high performance

• Allows us to use a buffer disk for each storage node, while still maintaining file striping level

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TestbedParameter Storage Server Storage Node

CPU Celeron 2.2 GHz Celeron 2.2 GHz

Memory (MB) 2000 2000

Network Interconnect

1000 1000

Disk Type SATA SATA

Disk Capacity 160 GB 480 GB

Disk Bandwidth 126 MB/s 126 MB/s

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Measured Results

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Measured Results

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Berkeley Web Trace

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Response Time Comparison

• Energy efficiency is slightly improved

• Response time gain is significant

Parameter Striping No Striping

Energy Consumption (J) 2,088,113 2,100,243

Response Time (S) 2.78 13.87

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Parallel Striping Groups Summary

• Improves the energy efficiency and performance of a storage system

• Designed to scale– Needs to be tested on large scale storage

system

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Conclusions

• Modeling and simulation used to test our ideas

– System, Disk, Trace Parameters varied to study their impacts

• DiskSim Modifications

– Added disk power models to DiskSim

– Implemented block to file translator

• Energy Aware Virtual Cluster File System (EEVFS)

– Implemented a prototype

– Added parallel striping groups to improve the energy efficiency

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Future Work

• Improve the EEVFS prototype for production use

• Run EEVFS on large scale storage system– Investigate scaling effects

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http://www.auburn.edu/~xzq0001

Download the presentation slides

Download the presentation slides

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Questions