Energy Efficient Prefetching – from models to Implementation 6/19/ Adam Manzanares and Xiao Qin Department of Computer Science and Software Engineering Auburn University

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 6/19/2015 5

Motivation EPA Report to Congress on Server and Data Center Energy Efficiency, /19/2015 6

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 6/19/2015 7

Buffer Disk Architecture RAM Buffer m buffer disks n data disks Buffer Disk Controller Data Partitioning Security Model Load Balancing Power Management Prefetching Disk Requests Energy-Related Reliability Model 6/19/2015 8

IBM Ultrastar 36Z15 6/19/ Transfer Rate55 MB/sSpin Down Time: T D 1.5 s Active Power: P A 13.5 WSpin Up Time: T U 10.9 s Idle Power: P I 10.2 WSpin Down Energy: E D 13 J Standby Power: P A 2.5 WSpin Up Energy: E U 135 J Break-Even Time: T BE 15.2 S

Prefetching Disk 1 Disk 2 Disk 3 Buffer Disk 6/19/

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 6/19/

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 6/19/

Energy Saving Principles  Energy Saving Principle One ◦ Increase the length and number of idle periods larger than the disk break-even time T BE  Energy Saving Principle Two ◦ Reduce the number of power-state transitions 6/19/

Paramaters Tested ParameterValues Data Size1, 5, 10, 25 MB # of Data Disks4, 8, 12 Inter-arrival Delay0, 0.1, 0.5, 1 S Hit Rate85, 90, 95, 100% 6/19/

Energy Savings Hit Rate 85% 6/19/

State Transitions 6/19/

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 6/19/

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 6/19/

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 6/19/

File System Simulator Large files important to energy savings Popularity of data is also useful Developed a block to file translator Interacts with DiskSim 6/19/

DiskSim with File System Simulator 6/19/

Modified DiskSim Results 6/19/

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 6/19/

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 6/19/

Energy Efficient Virtual File System 6/19/

EEVFS Process Flow 6/19/

EEVFS Testbed ParameterStorage ServerStorage Node Type 1 Storage Node Type 2 CPUP4 2.0 GHzP4 3.2 GHzP4 2.4 GHz Memory (MB) Network Interconnect Disk TypeSATAATA/133 Disk Capacity120 GB80 GB Disk Bandwidth100 MB/s58 MB/s34 MB/s 6/19/

Energy Savings 6/19/

State Transitions 6/19/

Response Times 6/19/

Berkeley Web Trace 6/19/

EEVFS Summary Knowledge of requests assumed and may be hard to come by Performance tied to one of the buffer disks 6/19/

Parallel Striping Groups Disk 1 Disk 2 Group 1 Buffer Disk Storage Node 1 Disk 3 Disk 4 Buffer Disk Storage Node 2 Disk 5 Disk 6 Group 2 Buffer Disk Storage Node 3 Disk 7 Disk 8 Buffer Disk Storage Node 4 File 1File 2File 3File 4 6/19/

Striping Within a Group Disk 1 Disk 2 Group 1 Buffer Disk Storage Node 1 Disk 3 Disk 4 Buffer Disk Storage Node File 1 File /19/

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 6/19/

Testbed ParameterStorage ServerStorage Node CPUCeleron 2.2 GHz Memory (MB)2000 Network Interconnect 1000 Disk TypeSATA Disk Capacity160 GB480 GB Disk Bandwidth126 MB/s 6/19/

Measured Results 6/19/

Measured Results 6/19/

Berkeley Web Trace 6/19/

Response Time Comparison Energy efficiency is slightly improved Response time gain is significant ParameterStripingNo Striping Energy Consumption (J)2,088,1132,100,243 Response Time (S) /19/

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 6/19/

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 6/19/

Future Work Improve the EEVFS prototype for production use Run EEVFS on large scale storage system –Investigate scaling effects 6/19/

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