Solving io bottleneck

IMEX
RESEARCH.COM

Solving the IO Bottleneck
in NextGen DataCenters &
Cloud Computing
Are SSDs Ready for Enterprise Storage Systems
Anil Vasudeva, President & Chief Analyst, IMEX Research
© 2007-11 IMEX Research
All Rights Reserved
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Please write to IMEX for authorization

Anil Vasudeva
President & Chief Analyst
imex@imexresearch.com IMEX
RESEARCH.COM
408-268-0800
© 2010‐11 IMEX Research, Copying prohibited. All rights reserved.

Abstract IMEX
RESEARCH.COM

Solving the I/O Bottleneck in NextGen Data Centers & Cloud Computing

Virtualization brings tremendous advantages to data centers of all sizes – large and
small. But the rapid proliferation of virtual machines (VMs) per physical server, in its
wake, creates a highly randomized I/O problem, raising performance bottlenecks. To
address this I/O issue, the IT industry and storage administrators in particular, have
begun the adoption of a slew of newer technology solutions - ranging from faster and
larger memories in cache, SSDs for higher IOPS cost effectively, high bandwidth
(10GbE) networks using NPIVs for virtualized networks between VMs and shared
storage systems along with embedded intelligence software to optimize various VM
workloads – all in order to meet various SLA metrics of performance, availability, cost
etc..
Are you aware of the side-effects that get created from Server Virtualization and
prepared to ask pertinent questions of your suppliers of IT Infrastructure Equipment,
Storage Virtualization and Data Storage Management software as well as in their
implementation to achieve targeted results in performance, availability, scalability,
interoperability and data management in their virtualized data centers
This presentation provides an illustrative view of the impact of Server Virtualization on
existing storage I/O solutions and best practices. It delineates the roles, capabilities and
cost effectiveness of emerging technologies in mitigating the I/O bottlenecks so the IT
infrastructure implementers can achieve their targeted performance under various
workloads, from their storage systems in virtualized data centers.

2

Agenda IMEX
RESEARCH.COM

• Data Centers & Cloud Infrastructure
• Cloud Computing Architecture
• Performance Metrics by Workload
• Anatomy of Data Access
• Data Center Performance Bottlenecks
• Improving Query Response Time in OLTP
• Role of SSD in Improving I/O Perf. Gap
• SCM: A New Storage Class Memory SSDs
• Price Erosion & IOPS/GB
• Choosing SSD vs. Memory to Improve TPS
• New Storage Usage Hierarchy in NGDC & Clouds
• IO Bottleneck Mitigation in Virtualized Servers
• I/O Forensics for Auto Storage-Tiering
• Apps Benefitting from Improved I/O
• Key Takeaways
• Acknowledgements

3

Data Centers & Cloud Infrastructure IMEX
RESEARCH.COM

Public CloudCenter © Enterprise VZ Data Center
On-Premise Cloud

Vertical
Clouds Servers VPN Switches: Layer 4-7,
IaaS, PaaS Layer 2, 10GbE, FC Stg
SaaS

Supplier/Partners ISP
ISP Internet ISP FC/ IPSANs
ISP Core
Optical
ISP
ISP Edge
Caching, Proxy, Database Servers,
Remote/Branch Office ISP FW, SSL, IDS, DNS, Middleware, Data
LB, Web Servers Mgmt

Tier-1 Application Servers Tier-3
Edge Apps HA, File/Print, ERP, Data Base
Web 2.0 SCM, CRM Servers Servers
Social Ntwks.
Cellular
Facebook, Tier-2 Apps
Twitter, YouTube…
Cable/DSL… Directory Security Policy Management
Wireless
Home Networks Middleware Platform

Source:: IMEX Research - Cloud Infrastructure Report © 2009-11
4

IT Industry’s Journey - Roadmap IMEX
RESEARCH.COM

Cloudization
SIVAC ®IMEX
On-Premises > Private Clouds > Public Clouds
DC to Cloud-Aware Infrast. & Apps. Cascade migration to SPs/Public Clouds.

Automation
Automatically Maintains Application SLAs
(Self-Configuration, Self-Healing©IMEX, Self-Acctg. Charges etc)

Virtualization
Pools Resources. Provisions, Optimizes, Monitors
Shuffles Resources to optimize Delivery of various Business Services

Integration/Consolidation
Integrate Physical Infrast./Blades to meet CAPSIMS ®IMEX

Cost, Availability, Performance, Scalability, Inter-operability, Manageability & Security

Standardization
Standard IT Infrastructure- Volume Economics HW/Syst SW
(Servers, Storage, Networking Devices, System Software (OS, MW & Data Mgmt SW)

5

Cloud Computing Architecture IMEX
RESEARCH.COM

Cloud Computing
Private Cloud Hybrid Public Cloud Service
Enterprise Cloud Providers

SaaS Applications
SaaS

SLA

SLA
SLA
SLA

SLA
App App App App

SLA

SLA
App

SLA
SLA

SLA
App App App App App

Platform Tools & Services

Management
Python
Ruby

.Net
EJB

PHP
….. …..
PaaS

Operating Systems

Virtualization
IaaS
Resources (Servers, Storage, Networks)

Application’s SLA dictates the Resources Required to meet specific
requirements of Availability, Performance, Cost, Security, Manageability etc.

Performance Metrics by Workload IMEX
RESEARCH.COM

1000 K
OLTP
Transaction
Processing eCommerce
100 K
Business
IOPS* (*Latency-1)

(RAID - 1, 5, 6) Data Intelligence
(RAID - 0, 3)

Warehousing
10K
OLAP

1K
Scientific Computing HPC
Imaging
TP
100
Audio Web 2.0
HPC
Video
10
1 5 10 50 100 500
*IOPS for a required response time ( ms) MB/sec
*=(#Channels*Latency-1)


Anatomy of Data Access IMEX
RESEARCH.COM
Performance

For the time it takes to do each
Disk Operation:
r r I/O
r ve sso Gap - Millions of CPU Operations can be done
Se oce - Hundreds of Thousands of Memory Operations
Pr can be accomplished
/O
Disk I

1980 1990 2000 2010

Anatomy of
Data Access
Time taken by CPU,
Memory, Network, Disk
for a typical I/O
Operation during a
Data Access
A 7.2K/15k rpm HDD can do 100/140 IOPS


Data Center Performance Bottlenecks IMEX
RESEARCH.COM

Clients
Windows, User Bottlenecks
Applications Linux, Unix
Connectivity Timeouts,
Excessive Locking
Workload Surges
Data Contention
I/O Delays/Errors
LAN Access Networks
Network I/O
Network Congestion Servers Server Bottlenecks
Dropped packets Web Servers Lack of Srvr Power
Application Servers
Data Retransmissions Database Servers IO Wait & Queuing CPU
Timeouts Overhead I/O Timeouts
Component Failures
Storage I/O Access
Storage I/O Connect
Lack of Bandwidth Storage
Overloaded PCIe Connect Web, Application, Device Bottlenecks
Storage Device Contention Database Device I/O Hotspots
Cache Flush
Lack of Storage Capacity


Improving Query Response Time in OLTP IMEX
RESEARCH.COM

8
Query Response Time (ms)
HDDs HDDs Hybrid
6 14 Drives 112 Drives HDDs
$$ w short 36 Drives
stroking + SSDs SSDs
4
$$$$$$$$ $$$$ 12 Drives
$$$
2

Conceptual Only -Not to Scale
0
0 10,000 20,000 30,000 40,000

IOPS (or Number of Concurrent Users)

• Improving Query Response Time
• Cost effective way to improve Query response time for a given number
of users or servicing an increased number of users at a given response
time is best served with use of SSDs or Hybrid (SSD + HDDs)
approach, particularly for Database and Online Transaction Applications

Source: IMEX Research SSD Industry Report 2011
© 10

Role of SSD in Improving I/O Perf. Gap IMEX
RESEARCH.COM

Price CPU
SDRAM
$/GB
DRAM getting
NOR
DRAM Faster (to feed faster CPUs) &
Larger (to feed Multi-cores &
Multi-VMs from Virtualization)
NAND
SCM
PCIe
SSD Servers

HDD
SSD segmenting into
SATA PCIe SSD Cache
Tape SSD - as backend to DRAM &
SATA SSD
Hybr. Storage - as front end to HDD

HDD becoming
Cheaper, not faster
Source: IMEX Research SSD Industry Report 2011
©

Performance
I/O Access Latency

11

SCM: A New Storage Class Memory IMEX
RESEARCH.COM

• SCM (Storage Class Memory)
Solid State Memory filling the gap between DRAMs & HDDs
Marketplace segmenting SCMs into SATA and PCIe based SSDs

• Key Metrics Required of Storage Class Memories
Device - Capacity (GB), Cost ($/GB),
• Performance - Latency (Random/Block RW Access-ms); Bandwidth
BW(R/W- GB/sec)
• Data Integrity - BER (Better than 1 in 10^17)
• Reliability - Write Endurance (30K PE Cycles No. of writes before
death);
• - Data Retention (5 Years); MTBF (2 millions of Hrs),
• Environment - Power Consumption (Watts);
• Volumetric Density (TB/cu.in.); Power On/Off Time
(sec),
• Resistance - Shock/Vibration (g-force); Temp./Voltage Extremes
4-Corner (oC,V); Radiation (Rad)
12

SSDs - Price Erosion & IOPS/GB IMEX
RESEARCH.COM

600 8

HDD B SS
Ds
IO PS/G

M)
it s(
D Un

Units (Millions)
SS
IOPS/GB

300 4
Enter
prise
HDD
Units
(M)
SSD

IOPS/GB HDDs
0 0
2009 2010 2011 2012 2013 2014
Note: 2U storage rack, • 2.5” HDD max cap = 400GB / 24 HDDs, de-stroked to 20%, • 2.5” SSD max cap = 800GB / 36 SSDs

Source: IMEX Research SSD Industry Report ©2011


IMEX
Choosing SSD vs. Memory to Improve TPS RESEARCH.COM

2500

2000

1500
TPS

4.0 x
2.5 x
1000 Ie
SS D PC

500 A TA 0
4.0 x S SD S A ID
1
D R
2.5 x
HD
0
0 2 4 6 8 10 12 14 16 18 20
Buffer Pool (Memory) GB


Data Storage Usage Patterns –
Data Access vs. Age of Data IMEX
RESEARCH.COM

80% of IOPs 80% of TB
Performance Scale
Data Protection Cost
Data Reduction
Data Access

Storage Growth
SSDs

1 Day 1 Week 1 Month 2 Mo. 3 Mo. 6 Mo. 1 Year 2 Yrs

Age of Data


New Storage Hierarchy in NGDC &
Clouds
IMEX
RESEARCH.COM

I/O Access Frequency vs. Percent of Corporate Data

95%

75%
Cloud
65% FCoE/ Storage
SAS SATA
Arrays
% of I/O Accesses

• Back Up Data
SSD • Tables • Archived Data
• Logs • Indices • Offsite DataVault
• Journals • Hot Data
• Temp Tables
• Hot Tables

1% 2% 10% 50% 100%
% of Corporate Data
16

IO Bottleneck Mitigation in Virtualized
Servers
IMEX
RESEARCH.COM

vSphere ESX

VM Client 1 VM Client 2 VM Client n
XCL
Mgr.
I/O XCL I/O XCL I/O XCL
Reg. Driver Reg. Driver Reg. Driver

ESX Kernel
XCL
VLUN
Disk
Controller. SSD
Driver

Offloading IOPS from
Primary Storage
Primary SSD Drive
Both Applications &
Storage Storage Run Faster w/ESX Driver


I/O Forensics for Auto Storage-Tiering IMEX
RESEARCH.COM

Storage-Tiered Virtualization
Storage-Tiering at LBA/Sub-LUN Level

Physical Storage Logical Volume

SSDs Hot Data
Arrays

Cold Data
HDDs
Arrays

LBA Monitoring and Tiered Placement
• Every workload has unique I/O access signature
• Historical performance data for a LUN can identify
performance skews & hot data regions by LBAs

Source: IBM & IMEX Research SSD Industry Report 2011 ©IMEX 2010-11
18

Apps Benefitting from Improved I/O IMEX
RESEARCH.COM

Smart Mobile Commercial Bioinformatics Decision Support Entertainment-
Devices Visualization & Diagnostics Bus. Intelligence VoD / U-Tube
Data: IMEX Research & Panasas

19

Instant On Boot Ups Rendering (Texture & Polygons) Data Warehousing Most Accessed Videos
Rugged, Low Power Very Read Intensive, Small Block I/O Random IO, High OLTPM Very Read Intensive
1GB/s, __ms 10 GB/s, __ms 1GB/s, __ms 4 GB/s, __ms


Key Takeaways IMEX
RESEARCH.COM

• Solving I/O Problems
• I/O Bottlenecks occur at multiple places in the Compute Stack, the largest
being at Storage I/O
• SSD comes out cheaper/IOP for IO Intensive Apps
• To get of Reads – Improve Indexing, archive out old data
• Minimize the impact of writes – Get rid of temp tables/filesorts on slow
disks.
• Compress big varchar/text/blobs
• Data Forensics and Tiered Placement
• Every workload has unique I/O access signature
• Historical performance data for a LUN can identify performance skews &
hot data regions by LBAs
• Use Smart Tiering to identify hot LBA regions and non-disruptively migrate
hot data from HDD to SSDs.
• Typically 4-8% of data becomes a candidate and when migrated to SSDs
can provide response time reduction of ~65% at peak loads


Solving io bottleneck

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