Solving the IO Bottleneck in NextGen DataCenters ... - IMEX Research
Solving the IO Bottleneck in NextGen DataCenters ... - IMEX Research
Solving the IO Bottleneck in NextGen DataCenters ... - IMEX Research
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<strong>IMEX</strong><br />
RESEARCH.COM<br />
<strong>Solv<strong>in</strong>g</strong> <strong>the</strong> <strong>IO</strong> <strong>Bottleneck</strong><br />
<strong>in</strong> <strong>NextGen</strong> <strong>DataCenters</strong> &<br />
Cloud Comput<strong>in</strong>g<br />
Are SSDs Ready for Enterprise Storage Systems<br />
Anil Vasudeva, President & Chief Analyst, <strong>IMEX</strong> <strong>Research</strong><br />
© 2007-11 <strong>IMEX</strong> <strong>Research</strong><br />
All Rights Reserved<br />
Copy<strong>in</strong>g Prohibited<br />
Please write to <strong>IMEX</strong> for authorization<br />
Anil Vasudeva<br />
President & Chief Analyst<br />
imex@imexresearch.com<br />
408-268-0800<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Abstract<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
<strong>Solv<strong>in</strong>g</strong> <strong>the</strong> I/O <strong>Bottleneck</strong> <strong>in</strong> <strong>NextGen</strong> Data Centers & Cloud Comput<strong>in</strong>g<br />
Virtualization br<strong>in</strong>gs tremendous advantages to data centers of all sizes – large and<br />
small. But <strong>the</strong> rapid proliferation of virtual mach<strong>in</strong>es (VMs) per physical server, <strong>in</strong> its<br />
wake, creates a highly randomized I/O problem, rais<strong>in</strong>g performance bottlenecks. To<br />
address this I/O issue, <strong>the</strong> IT <strong>in</strong>dustry and storage adm<strong>in</strong>istrators <strong>in</strong> particular, have<br />
begun <strong>the</strong> adoption of a slew of newer technology solutions - rang<strong>in</strong>g from faster and<br />
larger memories <strong>in</strong> cache, SSDs for higher <strong>IO</strong>PS cost effectively, high bandwidth<br />
(10GbE) networks us<strong>in</strong>g NPIVs for virtualized networks between VMs and shared<br />
storage systems along with embedded <strong>in</strong>telligence software to optimize various VM<br />
workloads – all <strong>in</strong> order to meet various SLA metrics of performance, availability, cost<br />
etc..<br />
Are you aware of <strong>the</strong> side-effects that get created from Server Virtualization and<br />
prepared to ask pert<strong>in</strong>ent questions of your suppliers of IT Infrastructure Equipment,<br />
Storage Virtualization and Data Storage Management software as well as <strong>in</strong> <strong>the</strong>ir<br />
implementation to achieve targeted results <strong>in</strong> performance, availability, scalability,<br />
<strong>in</strong>teroperability and data management <strong>in</strong> <strong>the</strong>ir virtualized data centers<br />
This presentation provides an illustrative view of <strong>the</strong> impact of Server Virtualization on<br />
exist<strong>in</strong>g storage I/O solutions and best practices. It del<strong>in</strong>eates <strong>the</strong> roles, capabilities and<br />
cost effectiveness of emerg<strong>in</strong>g technologies <strong>in</strong> mitigat<strong>in</strong>g <strong>the</strong> I/O bottlenecks so <strong>the</strong> IT<br />
<strong>in</strong>frastructure implementers can achieve <strong>the</strong>ir targeted performance under various<br />
workloads, from <strong>the</strong>ir storage systems <strong>in</strong> virtualized data centers.<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
2
Agenda<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
• Data Centers & Cloud Infrastructure<br />
• Cloud Comput<strong>in</strong>g Architecture<br />
• Performance Metrics by Workload<br />
• Anatomy of Data Access<br />
• Data Center Performance <strong>Bottleneck</strong>s<br />
• Improv<strong>in</strong>g Query Response Time <strong>in</strong> OLTP<br />
• Role of SSD <strong>in</strong> Improv<strong>in</strong>g I/O Perf. Gap<br />
• SCM: A New Storage Class Memory SSDs<br />
• Price Erosion & <strong>IO</strong>PS/GB<br />
• Choos<strong>in</strong>g SSD vs. Memory to Improve TPS<br />
• New Storage Usage Hierarchy <strong>in</strong> NGDC & Clouds<br />
• <strong>IO</strong> <strong>Bottleneck</strong> Mitigation <strong>in</strong> Virtualized Servers<br />
• I/O Forensics for Auto Storage-Tier<strong>in</strong>g<br />
• Apps Benefitt<strong>in</strong>g from Improved I/O<br />
• Key Takeaways<br />
• Acknowledgements<br />
3<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Data Centers & Cloud Infrastructure<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Public CloudCenter©<br />
Enterprise VZ Data Center<br />
On-Premise Cloud<br />
Vertical<br />
Clouds<br />
IaaS, PaaS<br />
SaaS<br />
Servers<br />
VPN<br />
Switches: Layer 4-7,<br />
Layer 2, 10GbE, FC Stg<br />
Supplier/Partners<br />
Remote/Branch Office<br />
Cellular<br />
Wireless<br />
ISP<br />
ISP<br />
ISP<br />
ISP<br />
Internet<br />
Core<br />
Optical<br />
Edge<br />
ISP<br />
Web 2.0<br />
Social Ntwks.<br />
Facebook,<br />
Twitter, YouTube…<br />
Cable/DSL…<br />
ISP<br />
Home Networks<br />
ISP<br />
Cach<strong>in</strong>g, Proxy,<br />
FW, SSL, IDS, DNS,<br />
LB, Web Servers<br />
Tier-1<br />
Edge Apps<br />
Application Servers<br />
HA, File/Pr<strong>in</strong>t, ERP,<br />
SCM, CRM Servers<br />
FC/ IPSANs<br />
Database Servers,<br />
Middleware, Data<br />
Mgmt<br />
Tier-3<br />
Data Base<br />
Servers<br />
Tier-2 Apps<br />
Directory Security Policy Management<br />
Middleware Platform<br />
Source:: <strong>IMEX</strong> <strong>Research</strong> - Cloud Infrastructure Report © 2009-11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
4
IT Industry’s Journey - Roadmap<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
SIVAC<br />
®<strong>IMEX</strong><br />
Cloudization<br />
Integration/Consolidation<br />
On-Premises > Private Clouds > Public Clouds<br />
DC to Cloud-Aware Infrast. & Apps. Cascade migration to SPs/Public Clouds.<br />
Integrate Physical Infrast./Blades to meet CAPSIMS ®<strong>IMEX</strong><br />
Cost, Availability, Performance, Scalability, Inter-operability, Manageability & Security<br />
Standardization<br />
Automation<br />
Automatically Ma<strong>in</strong>ta<strong>in</strong>s Application SLAs<br />
(Self-Configuration, Self-Heal<strong>in</strong>g ©<strong>IMEX</strong> , Self-Acctg. Charges etc)<br />
Virtualization<br />
Pools Resources. Provisions, Optimizes, Monitors<br />
Shuffles Resources to optimize Delivery of various Bus<strong>in</strong>ess Services<br />
Standard IT Infrastructure- Volume Economics HW/Syst SW<br />
(Servers, Storage, Network<strong>in</strong>g Devices, System Software (OS, MW & Data Mgmt SW)<br />
Source:: <strong>IMEX</strong> <strong>Research</strong> - Cloud Infrastructure Report © 2009-11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
5
Cloud Comput<strong>in</strong>g Architecture<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Private Cloud<br />
Enterprise<br />
Cloud Comput<strong>in</strong>g<br />
Hybrid<br />
Cloud<br />
Public Cloud Service<br />
Providers<br />
SaaS Applications<br />
SaaS<br />
App<br />
SLA<br />
App<br />
SLA<br />
App<br />
SLA<br />
App<br />
SLA<br />
App<br />
SLA<br />
PaaS<br />
EJB<br />
Ruby<br />
Platform Tools & Services<br />
Python<br />
.Net<br />
PHP<br />
Operat<strong>in</strong>g Systems<br />
…..<br />
…..<br />
Management<br />
IaaS<br />
Virtualization<br />
Resources (Servers, Storage, Networks)<br />
Application’s SLA dictates <strong>the</strong> Resources Required to meet specific<br />
requirements of Availability, Performance, Cost, Security, Manageability etc.<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Performance Metrics by Workload<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
<strong>IO</strong>PS* (*Latency-1)<br />
1000 K<br />
100 K<br />
10K<br />
1K<br />
100<br />
Transaction<br />
Process<strong>in</strong>g<br />
(RAID - 1, 5, 6)<br />
TP<br />
OLTP<br />
eCommerce<br />
Data<br />
Warehous<strong>in</strong>g<br />
HPC<br />
Bus<strong>in</strong>ess<br />
Intelligence<br />
OLAP<br />
Scientific Comput<strong>in</strong>g<br />
Imag<strong>in</strong>g<br />
Audio<br />
Video<br />
(RAID - 0, 3)<br />
HPC<br />
Web 2.0<br />
10<br />
1 5<br />
10<br />
50<br />
*<strong>IO</strong>PS for a required response time ( ms)<br />
*=(#Channels*Latency-1)<br />
100<br />
MB/sec<br />
500<br />
Source:: <strong>IMEX</strong> <strong>Research</strong> - Cloud Infrastructure Report © 2009-11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Anatomy of Data Access<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Performance<br />
Server<br />
Processor<br />
Disk I/O<br />
I/O<br />
Gap<br />
For <strong>the</strong> time it takes to do each<br />
Disk Operation:<br />
- Millions of CPU Operations can be done<br />
- Hundreds of Thousands of Memory Operations<br />
can be accomplished<br />
1980 1990 2000 2010<br />
Anatomy of<br />
Data Access<br />
Time taken by CPU,<br />
Memory, Network, Disk<br />
for a typical I/O<br />
Operation dur<strong>in</strong>g a<br />
Data Access<br />
A 7.2K/15k rpm HDD can do 100/140 <strong>IO</strong>PS<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Data Center Performance <strong>Bottleneck</strong>s<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Applications<br />
Excessive Lock<strong>in</strong>g<br />
Data Contention<br />
I/O Delays/Errors<br />
Network I/O<br />
Network Congestion<br />
Dropped packets<br />
Data Retransmissions<br />
Timeouts<br />
Component Failures<br />
Storage I/O Connect<br />
Lack of Bandwidth<br />
Overloaded PCIe Connect<br />
Storage Device Contention<br />
LAN Access Networks<br />
Storage I/O Access<br />
Clients<br />
W<strong>in</strong>dows,<br />
L<strong>in</strong>ux, Unix<br />
Servers<br />
Web Servers<br />
Application Servers<br />
Database Servers<br />
Storage<br />
Web, Application,<br />
Database<br />
User <strong>Bottleneck</strong>s<br />
Connectivity Timeouts,<br />
Workload Surges<br />
Server <strong>Bottleneck</strong>s<br />
Lack of Srvr Power<br />
<strong>IO</strong> Wait & Queu<strong>in</strong>g CPU<br />
Overhead I/O Timeouts<br />
Device <strong>Bottleneck</strong>s<br />
Device I/O Hotspots<br />
Cache Flush<br />
Lack of Storage Capacity<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Improv<strong>in</strong>g Query Response Time <strong>in</strong> OLTP<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Query Response Time (ms)<br />
0<br />
8<br />
6<br />
4<br />
2<br />
HDDs<br />
14 Drives<br />
$$<br />
HDDs<br />
112 Drives<br />
w short<br />
strok<strong>in</strong>g<br />
$$$$$$$$<br />
Hybrid<br />
HDDs<br />
36 Drives<br />
+ SSDs<br />
$$$$<br />
SSDs<br />
12 Drives<br />
• Improv<strong>in</strong>g Query Response Time<br />
• Cost effective way to improve Query response time for a given number<br />
of users or servic<strong>in</strong>g an <strong>in</strong>creased number of users at a given response<br />
time is best served with use of SSDs or Hybrid (SSD + HDDs)<br />
approach, particularly for Database and Onl<strong>in</strong>e Transaction Applications<br />
$$$<br />
Conceptual Only -Not to Scale<br />
0 10,000 20,000 30,000<br />
40,000<br />
<strong>IO</strong>PS (or Number of Concurrent Users)<br />
Source: <strong>IMEX</strong> <strong>Research</strong> SSD Industry Report © 2011<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
10
Role of SSD <strong>in</strong> Improv<strong>in</strong>g I/O Perf. Gap<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Price<br />
$/GB<br />
NAND<br />
NOR<br />
SCM<br />
DRAM<br />
CPU<br />
SDRAM<br />
PCIe<br />
SSD<br />
DRAM gett<strong>in</strong>g<br />
Faster (to feed faster CPUs) &<br />
Larger (to feed Multi-cores &<br />
Multi-VMs from Virtualization)<br />
Servers<br />
Tape<br />
HDD<br />
SATA<br />
SSD<br />
Hybr. Storage<br />
HDD becom<strong>in</strong>g<br />
Cheaper, not faster<br />
SSD segment<strong>in</strong>g <strong>in</strong>to<br />
• PCIe SSD Cache<br />
- as backend to DRAM &<br />
• SATA SSD<br />
- as front end to HDD<br />
Source: <strong>IMEX</strong> <strong>Research</strong> SSD Industry Report © 2011<br />
Performance<br />
I/O Access Latency<br />
11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
• Performance<br />
• Data<br />
• Reliability<br />
• -<br />
• Environment<br />
• Volumetric<br />
• Resistance<br />
SCM: A New Storage Class Memory<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
• SCM (Storage Class Memory)<br />
Solid State Memory fill<strong>in</strong>g <strong>the</strong> gap between DRAMs & HDDs<br />
Marketplace segment<strong>in</strong>g SCMs <strong>in</strong>to SATA and PCIe based SSDs<br />
• Key Metrics Required of Storage Class Memories<br />
Device - Capacity (GB), Cost ($/GB),<br />
- Latency (Random/Block RW Access-ms); Bandwidth<br />
BW(R/W- GB/sec)<br />
Integrity - BER (Better than 1 <strong>in</strong> 10^17)<br />
- Write Endurance (30K PE Cycles No. of writes before<br />
death);<br />
Data Retention (5 Years); MTBF (2 millions of Hrs),<br />
- Power Consumption (Watts);<br />
Density (TB/cu.<strong>in</strong>.); Power On/Off Time<br />
(sec),<br />
- Shock/Vibration (g-force); Temp./Voltage Extremes<br />
4-Corner (oC,V); Radiation (Rad)<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
12
SSDs - Price Erosion & <strong>IO</strong>PS/GB<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
600<br />
8<br />
HDD<br />
<strong>IO</strong>PS/GB SSDs<br />
<strong>IO</strong>PS/GB<br />
300<br />
SSD<br />
SSD Units (M)<br />
Enterprise HDD Units (M)<br />
4<br />
Units (Millions)<br />
0<br />
<strong>IO</strong>PS/GB HDDs<br />
2009 2010 2011 2012 2013 2014<br />
Note: 2U storage rack, • 2.5” HDD max cap = 400GB / 24 HDDs, de-stroked to 20%, • 2.5” SSD max cap = 800GB / 36 SSDs<br />
0<br />
Source: <strong>IMEX</strong> <strong>Research</strong> SSD Industry Report © 2011<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Choos<strong>in</strong>g SSD vs. Memory to Improve TPS<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
2500<br />
2000<br />
1500<br />
TPS<br />
1000<br />
2.5 x<br />
4.0 x<br />
SSD PCIe<br />
500<br />
0<br />
4.0 x<br />
SSD SATA<br />
2.5 x<br />
0 2 4 6 8 10 12 14 16 18 20<br />
Buffer Pool (Memory) GB<br />
HDD RAID 10<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Data Storage Usage Patterns –<br />
Data Access vs. Age of Data<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
80% of <strong>IO</strong>Ps<br />
80% of TB<br />
Data Access<br />
Performance<br />
Data Protection<br />
Scale<br />
Cost<br />
Data Reduction<br />
Storage Growth<br />
SSDs<br />
1 Day 1 Week 1 Month 2 Mo. 3 Mo. 6 Mo.<br />
1 Year<br />
2 Yrs<br />
Age of Data<br />
Source:: <strong>IMEX</strong> <strong>Research</strong> - Cloud Infrastructure Report © 2009-11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
New Storage Hierarchy <strong>in</strong> NGDC &<br />
Clouds<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
I/O Access Frequency vs. Percent of Corporate Data<br />
95%<br />
75%<br />
65%<br />
% of I/O Accesses<br />
SSD<br />
• Logs<br />
• Journals<br />
• Temp Tables<br />
• Hot Tables<br />
FCoE/<br />
SAS<br />
Arrays<br />
• Tables<br />
• Indices<br />
• Hot Data<br />
Cloud<br />
Storage<br />
SATA<br />
• Back Up Data<br />
• Archived Data<br />
• Offsite DataVault<br />
1%<br />
2% 10% 50% 100%<br />
% of Corporate Data<br />
Source:: <strong>IMEX</strong> <strong>Research</strong> - Cloud Infrastructure Report © 2009-11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
16
<strong>IO</strong> <strong>Bottleneck</strong> Mitigation <strong>in</strong> Virtualized<br />
Servers<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
vSphere ESX<br />
VM Client 1<br />
VM Client 2<br />
VM Client n<br />
XCL<br />
Mgr.<br />
I/O<br />
Reg.<br />
XCL<br />
Driver<br />
I/O<br />
Reg.<br />
XCL<br />
Driver<br />
I/O<br />
Reg.<br />
XCL<br />
Driver<br />
Disk<br />
Controller.<br />
XCL<br />
VLUN<br />
SSD<br />
Driver<br />
ESX Kernel<br />
Primary<br />
Storage<br />
Offload<strong>in</strong>g <strong>IO</strong>PS from<br />
Primary Storage<br />
Both Applications &<br />
Storage Run Faster<br />
SSD Drive<br />
w/ESX Driver<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
I/O Forensics for Auto Storage-Tier<strong>in</strong>g<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Storage-Tiered Virtualization<br />
Storage-Tier<strong>in</strong>g at LBA/Sub-LUN Level<br />
Physical Storage Logical Volume<br />
SSDs<br />
Arrays<br />
Hot Data<br />
HDDs<br />
Arrays<br />
Cold Data<br />
LBA Monitor<strong>in</strong>g and Tiered Placement<br />
• Every workload has unique I/O access signature<br />
• Historical performance data for a LUN can identify<br />
performance skews & hot data regions by LBAs<br />
Source: IBM & <strong>IMEX</strong> <strong>Research</strong> SSD Industry Report 2011 ©<strong>IMEX</strong> 2010-11<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.<br />
18
Apps Benefitt<strong>in</strong>g from Improved I/O<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
Smart Mobile<br />
Devices<br />
Commercial<br />
Visualization<br />
Bio<strong>in</strong>formatics<br />
& Diagnostics<br />
Decision Support<br />
Bus. Intelligence<br />
Enterta<strong>in</strong>ment-<br />
VoD / U-Tube<br />
Data: <strong>IMEX</strong> <strong>Research</strong> & Panasas<br />
19<br />
Instant On Boot Ups<br />
Rugged, Low Power<br />
1GB/s, __ms<br />
Render<strong>in</strong>g (Texture & Polygons)<br />
Very Read Intensive, Small Block I/O<br />
Data Warehous<strong>in</strong>g<br />
Random <strong>IO</strong>, High OLTPM<br />
Most Accessed Videos<br />
Very Read Intensive<br />
10 GB/s, __ms 1GB/s, __ms<br />
4 GB/s, __ms<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.
Key Takeaways<br />
<strong>IMEX</strong><br />
RESEARCH.COM<br />
• <strong>Solv<strong>in</strong>g</strong> I/O Problems<br />
• I/O <strong>Bottleneck</strong>s occur at multiple places <strong>in</strong> <strong>the</strong> Compute Stack, <strong>the</strong> largest<br />
be<strong>in</strong>g at Storage I/O<br />
• SSD comes out cheaper/<strong>IO</strong>P for <strong>IO</strong> Intensive Apps<br />
• To get of Reads – Improve Index<strong>in</strong>g, archive out old data<br />
• M<strong>in</strong>imize <strong>the</strong> impact of writes – Get rid of temp tables/filesorts on slow<br />
disks.<br />
• Compress big varchar/text/blobs<br />
• Data Forensics and Tiered Placement<br />
• Every workload has unique I/O access signature<br />
• Historical performance data for a LUN can identify performance skews &<br />
hot data regions by LBAs<br />
• Use Smart Tier<strong>in</strong>g to identify hot LBA regions and non-disruptively migrate<br />
hot data from HDD to SSDs.<br />
• Typically 4-8% of data becomes a candidate and when migrated to SSDs<br />
can provide response time reduction of ~65% at peak loads<br />
© 2010‐11 <strong>IMEX</strong> <strong>Research</strong>, Copy<strong>in</strong>g prohibited. All rights reserved.