Storage and The Cloud 1. What is driving IT / Businesses to Cloud 2. Traditional IT Organization Impact 3. Traditional vs. Design-for-Fail

© 2013 IBM Corporation
Storage and “The Cloud”
1. What is driving IT / Businesses to Cloud
2. Traditional IT Organization Impact
3. Traditional vs. Design-for-Fail, On-premise vs. Off-premise
4. IBM Big Data / Cloud Storage Products and Directions
IBM Cloud Storage Briefing - December 3, 2013
Provided by: John Sing, Executive IT Consultant, San Jose, California singj@us.ibm.com

IBM Cloud Storage Briefing – December 3, 2013
2
What is driving IT and Businesses to Cloud

3

Value delivered
Storage Provisioning
Continuous Access to data
From traditional
Weeks
To cloud
Minutes
For users
Reduced storage admin costs Up to 50% savings
For IT
Reduced energy costs Up to 36%
Increased storage utilization Up to 90%
From 50%
Localized, any time
any where
Dynamic (Elastic)
Centralized
Fixed
Storage Capacity
Modern 21st Century Cloud Business Value
Time-to-Delivery
Competitive Advantage
Revenue
“Time is Money”

4
Primary drivers for move to cloud = business reasons
http://www.kpmg.com/global/en/issuesandinsights/articlespublications/cloud-service-providers-survey/pages/service-providers.aspx
Competitive Advantage,
Revenue

5
Bandwidth availability is tipping point for adoption of “The
Cloud”………
 Worldwide broadband bandwidth availability is
becoming commonplace
 Facilitates a pervasive web services delivery model
– (i.e. “The Cloud”)
 Hosted in mega data centers with massive amounts:
– Processors, Storage, Network
 Today, when above 3 come together in a geo:
– We are seeing small, medium on-premise data
centers worldwide rapidly disappearing, off-
premise, into the cloud
 The real question:
– Is traditional IT re-capturing / replacing workloads
when they move off-premise to Cloud ?

6
Cloud Mega Data Centers = new modular IT implementation style…
 Internet-scale centers…..
 Data:
–10s / 100s petabytes
 Servers:
–100,000s ….
 Workloads:
–Require server clusters
of 100s, 1000s, 10,000,
more …..
Modular implementation

7
Amazon Web Services
Amazon Web Services 1Q12: 450,000 servers
Amazon Perdix Modular Datacenter
EC2 17K core, 240 teraflop cluster
42nd fastest supercomputer in world
1Q12:
450,000
Servers
estimated
1Q13: >
2 trillion
objects in S3
1Q13: 1.1 M
req/sec
http://aws.typepad.com/aws/2012/04/amazon-s3-905-billion-objects-and-650000-requestssecond.html
http://gigaom.com/cloud/how-big-is-amazon-web-services-bigger-than-a-billion/
http://aws.typepad.com/aws/2013/04/amazon-s3-two-trillion-objects-11-million-requests-second.html

8
Growth of
The Cloud
by 2016
 Mobile
 Geo-locational
 Real-time data
 Shift to cloud
mega-data centers
http://www.datacenterknowledge.com/archives/2012/10/23/cisco-releases-2nd-annual-global-cloud-index/
Source:
> 50% in
cloud
Cisco
already
knows
> 50%
workload
is in the
cloud

9
Cloud: No longer exploratory
Expectations: Cloud computing
will be "just computing" by 2018
•Cloud is at the end of its
beginning phase and has gotten
serious
•Private cloud is growing, but
giving way to hybrid cloud
•Service providers, VARs, SIs are
rising to the cloud opportunity
•Cloud adoption is strong across
large enterprise as well as SMB.

10
So, What is a Cloud, really?
Why does it impact Traditional On-Premise
IT organization so heavily?
Extracted from presentation: “Building a 21st Century Cloud Storage Service” by John Sing:
http://snjgsa.ibm.com/~singj/public/2013_Berlin_System_Storage_x_Pure_Symposium/sCS05_John_Sing_Building_21st_Century_Cloud_Storage_Service_Industry_Best_Practice.ppt

11
To users, cloud seems “easy”, “instant”, “self-service”.
So what has to happen in the background?
 Some would say that virtualization = cloud
 Some IT traditionalists would say that cloud
is nothing more than much better managed
centralized, automated data centers
 Unfortunately, such statements severely
undersize the essential organizational
element
 To provide true cloud services, you must
also execute a significant shift in:
– Organizational lines
– Processes
– Workflows
– Workload types
– Required skill sets Key message

12
This is the
cloud-enabled
data center
journey
1. Virtualized
2. Deployed
3. Optimized
4. Enhanced
5. Monetized
Cloud
adoption
maturity
levels
Level of cloud capability
(macropatterns)
http://www.redbooks.ibm.com/abstracts/redp4893.html
IBM
Redpaper

13
What’s most important: cloud macropattern workflows
1. Simple IaaS
4. ITIL Managed
IaaS
2. Cloud
Mgmt
3. Adv
IaaS

14
 Are you ready?
Cloud micro-pattern workflows
IBM Storwize V7000, SVC, XIV Tivoli Storage Manager
Tivoli Storage
Productivity Center
Smart Cloud
Storage Access
Problem! Traditional IT organization looks nothing like this workflow!

15
IBM Redpapers: Building Cloud Enabled Data Center / Service Provider
http://www.redbooks.ibm.com/abstracts/redp4912.html
http://www.redbooks.ibm.com/abstracts/redp4893.html http://www.redbooks.ibm.com/abstracts/redp4873.html

16
Example: IBM Storage products within the Cloud workflow
Non-Technical Users
P9: IBM SmartCloud Storage Access
P8: IBM Tivoli Storage Productivity Center
P0: IBM SVC / Storwize
V7000 U
Storage
Area
Network
(FC) Self Provisioning Requests for Windows or Linux
OS and end user consumption
Ethernet
Network
P0: IBM SONAS
File
P0: IBM XIV
Block
Virtualizes
IBM or 3rd party Storage
arrays(HP, NetApp, EMC, etc.)
CIFS / NFS
Provisioning Requests for LUNs to be
assign/consume by either to physical or Virtual
Servers
Server, Application Owners, Developers users, etc.
LUN
Physical or
Virtual
Servers
LUNs
LUN
LUN
LUN
eMail
DB2
SAP
ERPs
TPC/Storage Admin
16

17
Key Cloud organizational learning point:
Cloud involves major re-alignment of IT organization, skills
 Re-alignment of IT processes, to facilitate real-time, elastic management, monitoring,
delivery based on service catalog
– Aligned with the Lines of Business revenue generation / competitive advantage needs
(requires full-time liason positions)
 Creation of service catalog requires IT to invest different efforts into
design/automation of IT capability
– New, additional skill requirements, aligned along a very different organizational structure,
metrics, and speed criteria
 Provide governance that addresses risk of unauthorized or rogue access to services
– Only appropriate approvals and credentials, thus new emphasis on network + security
 Addressing resistance to change within IT organization is the biggest success factor
If the on-premise IT organizations is unable to change…..
– this is also a major off-premise cloud driver

18
This organizational shift is a main reason why “ready-to-go” cloud workflow
products (such as OpenStack) are so attractive:
Source: http://ken.pepple.info/openstack/2012/09/25/openstack-folsom-architecture/
OpenStack already has
all cloud workflows
ready for production

19
OpenStack is comprised of seven core projects that form a complete
Cloud Infrastructure as a Service (IaaS) solution
Compute (Nova)
Block Storage (Cinder)
Network (Neutron)
Provision and manage
virtual resources

Dashboard (Horizon)
Self-service portal
Image (Glance)
Catalog and manage
server images
Identity (Keystone)
Unified authentication,
integrates with existing
systems
Object Storage (Swift)
petabytes of secure,
reliable object storage
IaaS
Source: http://ken.pepple.info/openstack/2012/09/25/openstack-folsom-architecture/
IaaS
Understand OpenStack
to understand IBM
Cloud Storage directions
Horizon
Swift
Glance
Keystone
Nova
Cinder
Neutron

20
Did you know: two different types of IT architectures have emerged
Design-for-Fail IT implementation has some similarities,
but clearly isn’t the same, as Traditional IT architecture
Knowledge Check

21
Today there are two major types of IT Cloud
architectures and workloads:
Transactional IT
“Systems of Record”
Internet Scale
Workloads
“Systems of Engagement”
Cloud, High Availability,
Resiliency, Disaster
Recovery
characteristics
Can be adapted to Cloud “agnostic
/ after the fact”
Data Strategy Can leverage traditional
tools/concepts to understand /
implement cloud
Storage/server virtualization and
pooling
Automation End to end automation of server /
storage virtualization
Commonality Apply master vision and lessons
learned from internet scale data
centers

22
The other major type of IT Cloud architecture and
workload is:
Transactional IT
Internet Scale
Workloads
“Systems of Engagement”
Cloud, High Availability,
Resiliency, Disaster
Recovery
characteristics
Can be designed “Agnostic / after the
fact” using server or storage
virtualization, replication
Cloud capabilities are
“designed into software stack
from the beginning”
Data Strategy Use traditional tools/concepts to
understand / know data
Storage/server virtualization and
pooling
Proven Open Source toolset
used implement failure
tolerance and redundancy in
the application stack
Automation End to end automation of server /
storage virtualization and replication
End to end automation of the
application software stack
providing failure tolerance
Commonality Apply master vision and lessons
learned from internet scale data
centers
Apply master vision and
lessons learned from internet
scale data centers

23
Today: two different types of IT
Source: http://it20.info/2012/02/the-cloud-magic-rectangle-tm/
Internet scale wkloads
Transactional IT

24
Today’s two major IT workload types
Source: http://it20.info/2012/02/the-cloud-magic-rectangle-tm/ Transactional IT Internet scale wkloads

25
How to build these two different IT architectures
Transactional IT

26
What You (Consumer) Get with These different
approaches:
Transactional IT

27
Policy-based Clouds and Design-for-Fail Clouds are
workload optimized architectural choices
 Policy-based Clouds
• Purpose optimized for longer-lived virtual
machines managed by Server Administrator
• Centralizes enterprise server virtualization
administration tasks
• High degree of flexibility designed to
accommodate virtualization all workloads
• Significant focus on managing availability and
QoS for long-lived workloads with level of
isolation
• Characteristics derived from exploiting enterprise
class hardware
• Legacy applications
 Design-for-fail Clouds
• Purpose optimized for shorter-term virtual
machines managed via end-user or automated
process
• Decentralized control, embraces eventual
consistency, focus on making “good enough”
decisions
• High degree of standardization
• Significant focus on ensuring availability of
control plane
• Characteristics driven by software
• New applications
Transactional IT

28
Example: Traditional IT vs. Hadoop for Big Data
Traditional approach : Move data to program
Big Data approach: Move function/programs to data
Database
server
Data
Query Data
return Data
process Data
Master
node
Data
nodes
Data
Application
server
User request
Send result
User request
Send Function to
process on Data
Query &
process Data
Data
nodes
Data
Data
nodes
Data
Data
nodes
Data
Send Consolidate result
Traditional approach
Application server and Database
server are separate
Analysis Program can run on
multiple Application servers
Network is still in the middle
Data has to go through network
Designed to analyze TBs of data
•Big Data Approach
 Analysis Program runs where the
data is : on Data Node
Only Analysis Program has to go
through the network
Analysis Program is executed on
every DataNode
Designed to analyze PBs of data
Highly Scalable :
1000s Nodes
Petabytes and more
Thank you to: Pascal VEZOLLE/France/IBM@IBMFR and Francois Gibello/France/IBM for the use of this slide

29
29
Example: Traditional IT vs. Hadoop for Big Data
Database
server
Data
Query Data
return Data
process Data
Application
server
User request
Send result
Master
node
Data
nodes
Data
User request
Send Function to
process on Data
Query &
process Data
Data
nodes
Data
Data
nodes
Data
Data
nodes
Data
Send Consolidate result
Example: How many hours of Clint
Eastwood appears in all the movies he
has done?
Task: All movies need to be
parsed to find Clint’s face
•Traditional approach :
1)Upload a movie to the application server
through the network
2) The Analysis Program compares Clint’s
picture with every frame of the loaded movie.
3) Repeat the 2 previous steps for every movie
•Big Data Approach :
1)Send the Analysis Program and Clint’s
picture to all the DataNodes.
2) The Analysis Program in every DataNode
(all in parallel) compares the Clint’s picture
with every frame of the loaded movie.
3) The results of every DataNodes are
consolidated. A unique result is generated.
Traditional approach : Move data to program
Big Data approach: Move function/programs to data
Thank you to: Pascal VEZOLLE/France/IBM@IBMFR and
Francois Gibello/France/IBM for the use of this slide
Note: Hadoop typically uses direct attached storage

30
Hadoop principles: Storage, HDFS and MapReduce
 Hadoop Distributed File System = HDFS : where Hadoop stores the data
– HDFS file system spans all the nodes in a cluster with locality awareness
 Hadoop data storage, computation model
– Data stored in a distributed file system, spanning many inexpensive computers
– Send function/program to the data nodes
– i.e. distribute application to compute resources where the data is stored
– Scalable to thousands of nodes and petabytes of data
MapReduce Application
1. Map Phase
(break job into small parts)
2. Shuffle
(transfer interim output
for final processing)
3. Reduce Phase
(boil all output down to
a single result set)
Return a single result set
Result Set
Shuffle
public static class TokenizerMapper
extends Mapper<Object,Text,Text,IntWritable> {
private final static IntWritable
one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text val, Context
StringTokenizer itr =
new StringTokenizer(val.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWrita
private IntWritable result = new IntWritable();
public void reduce(Text key,
Iterable<IntWritable> val, Context context){
int sum = 0;
for (IntWritable v : val) {
sum += v.get();
. . .
Distribute map
tasks to cluster
Hadoop Data Nodes
Data is loaded,
spread, resident in
Hadoop cluster
Performance =
tuning Map Reduce workflow,
network, application, servers,
and storage
http://www.ibm.com/developerworks/data/library/techarticle/dm-1209hadoopbigdata/
http://blog.cloudera.com/blog/2009/12/7-tips-for-improving-mapreduce-performance/
http://www.slideshare.net/allenwittenauer/2012-lihadoopperf

31
Two different types of cloud tooling
Cloud storage tooling will most likely reside:
 In the external shared storage stack for policy-based traditional transactional IT:
– External IBM Smarter Storage hardware and software for block and file storage
 In the virtualized server, direct attach storage, application stack for design-for-fail:
– IBM SmartCloud software, IBM participation in Open Stack, IBM Softlayer
 Both are appropriate, match to proper environment
Transactional IT
http://www.slideshare.net/johnsing1/s-bd03-infinitybeyond2internetscaleworkloadsdatacenterdesignv6speaker

32
Read all about it. Google published this information into the public domain in
2009. 2nd Edition of this book published July 2013(includes Flash storage)
 By Google:
– Luiz Andre Barroso
– Uri Holze
 Available to all, free of
charge
Download original edition at: http://www.morganclaypool.com/doi/pdf/10.2200/S00193ED1V01Y200905CAC006
New! 2nd Edition published July 2013: http://www.morganclaypool.com/doi/abs/10.2200/S00516ED2V01Y201306CAC024
Video of Luis giving one of these lectures: http://inst-tech.engin.umich.edu/leccap/view/cse-dls-08/4903
http://www.barroso.org/

33
Size of Cloud Market:
Magnitude of On-premise vs. Off-premise

34
Size of Server, Storage, Networking aggregate marketplaces
Compound Growth Rate 2013-2017
Cloud Service Provider (CSP) 25%
Enterprise Private Cloud (EPC) 23%
Non-Cloud -7%
Total 3%
Source: IBM
2013 2017
$104B $117B
37% is for Storage

35
Cloud adoption continues acceleration through 2017
35 September 2013
On premise vs. off premise spend
EPC, $24B
23% CGR
CSP, $33B
25% CGR
Source: IBM
Enterprise
On-premise
Non-Cloud
Cloud IaaS
Cloud server,
storage,
networking
$57B, 24%CGR
48% of Total
Non-Cloud
$60B,
-7%CGR
52% of Total
Cloud
Services
Off
premise
On
premise
Off-premise is
clearly the growth
area

36
IBM Big Data / Analytics Storage Positioning

37
We are building real-time, integrated stream computing on massive scale
n d
Chart in public domain: IEEE Massive File Storage presentation, author: Bill Kramer, NCSA: http://storageconference.org/2010/Presentations/MSST/1.Kramer.pdf

38
Data in
Motion
Data at
Rest
Data in
Many Forms
Information
Ingestion and
Operational
Information
Decision
Management
BI and Predictive
Analytics
Navigation
and Discovery
Intelligence
Analysis,
Raw Data
Structured Data
Text Analytics
Data Mining
Entity Analytics
Machine Learning
Landing Area,
Analytics Zone, Archive
Video/Audio
Network/Sensor
Entity Analytics
Predictive
Real-time Analytics
Exploration,
Integrated Warehouse,
and Mart Zones
Discovery
Deep Reflection
Operational
Predictive
Stream Processing
Data Integration
Master Data
Streams
Information Governance, Security and Business Continuity
Batch parallel Big
Data processing
Real-Time
In-memory servers
Data Warehouse
Traditional IT
However, note there are multiple types of Big Data

39
Data in
Motion
Data at
Rest
Data in
Many Forms
Information
Ingestion and
Operational
Information
Decision
Management
BI and Predictive
Analytics
Navigation
and Discovery
Intelligence
Analysis
Raw Data
Structured Data
Text Analytics
Data Mining
Entity Analytics
Machine Learning
Landing Area, Analytics
Zone and Archive
Video/Audio
Network/Sensor
Entity Analytics
Predictive
Real-time Analytics
Exploration,
Integrated Warehouse,
and Mart Zones
Discovery
Deep Reflection
Operational
Predictive
Stream Processing
Data Integration
Master Data
Streams
Information Governance, Security and Business Continuity
IBM BigInsights
IBM
InfoSphere
Streams
IBM Data Warehouse
products
IBM end to end Big Data portfolio
IBM STG: x, p, PureSystems, Platform
Computing
IBM STG: x, p,
PureSystems, Platform
Computing
IBM SWG

40
IBM Big Data Storage positioning
Hadoop
oStorage for Hadoop
– IBM Big Data Networked Storage
Solution for Hadoop
oPureSystems
– IBM PureData System for Hadoop
with pre-installed IBM BigInsights
– Generally Available September 2013
Optimized Multi-Temperature Data
Warehouse
oAll Flash
– FlashSystem
oHybrid
– DS8000 EasyTier
– Storwize EasyTier
– FlashSystem Solution (VSC +
FlashSystem)
– XIV
oPureSystems
– PureFlex (Storwize w/EasyTier)
– PureData for Transactions (Storwize)
– PureData for Analytics (Netezza)
Customer disk GB cost expectation (USA):
10 to 15 cents/GB with
direct or SAS attach, extreme density
Customer disk GB cost expectation
(USA): 30 to 70 cents/GB

41
IBM Cloud Storage Directions

42
BLOCK
FILE
OBJECT
Data Growth Types in the Cloud
Worldwide File-based vs Block-based
Storage Capacity Shipments 2008-2015
Block
File
Object
 Block – Traditional data is structured and managed by OS i.e. Database
 File – High growth data is unstructured and managed by OS i.e. File System
 Object – Higher growth data is unstructured and managed by Application

43
43
Object Storage – fundamental type of storage for Cloud
Object Storage
Network “Best Case” delivery
Best usage = data that doesn’t
change
i.e. backups, archives, digital images,
virtual machine images….
Distance limited only to
acceptable network latency
Servers
Applications
 Object storage features are minimal compared to NAS or SAN:
– store, retrieve, copy, delete files
– control which users can do what
 Protocol usually HTTP interface Object Storage API (RESTful API)
– Can be in URL format for WWW access
 Application is responsible for tracking object unique IDs and supplying
that unique ID to retrieve data from object storage
 Typically longer response times than either NAS or SAN
– Slower throughput compared traditional file system means object storage
unsuitable for data that changes frequently
 Typical usages: great fit for data that doesn't change much:
– backups, archives, video and audio, VM images
– i.e. internet-scale repositories of data
– This is why it is so essential to Cloud
No concept of file system. Rather, application saves object (files + additional metadata) to the object store via PUT API cmd,
application gets a unique keyfor the saved file, application must provide that unique key to a GET API command to
retrieve files
Can imbed searchable
metadata directly into
object storage system

44
Objects are a natural fit to “born on cloud” data (mobile, social)
 Objects are written once and never modified (although they can be replaced)
– this describes most born on the cloud data
– Pictures, e-mails, movies, tweets, blog-posts, web pages, etc.
– This data is both consumer and enterprise
– Much of this data is accessed from mobile devices
 Hence Object Storage is essential to participate in Cloud Storage world
Pictures Collaboration Backup Archive
Rackspace
Consumer Apps Business Apps

45
45
Storage: SAN / NAS / Object
STORAGE
IP Network
APPLICATION
NAS
(Network Attached Storage)
CIFS, NFS, HTTP
FILE SYSTEM
File I/O
Block I/O
File I/O
STORAGE
APPLICATION
SAN
(Storage Area Network)
FICON, FC, iSCSI, FCoE
Fibre
Channel
SAN or
iSCSI
FILE SYSTEM
File I/O
Block I/O
STORAGE
Object Storage (HTTP)
OBJECT
CONTAINER
Block I/O
Object I/O
Object API
Object
APPLICATION
Object Storage
Object API
IP Network
Object API
Block I/O

46
IBM Cloud Storage – current products and future directions
Traditional IT:
 IBM Smart Cloud Storage Access - to provide P9 and P8 Self-Service Automation (storage)
 IBM Tivoli Storage Productivity Center – to provide P6 Storage Virtualization Management
 IBM Storwize Family and XIV – provide P0 storage virtualization including enterprise best-in-
class OpenStack exploitation
 IBM SONAS and V7000 Unified - provide P0 storage virtualization for file storage
Cloud Storage and Object Storage Directions:
 Exploitation of OpenStack Cinder for block storage
 Exploitation of OpenStack Swift for software-defined object storage approach
 Best-in-class OpenStack enterprise exploitation
 Design for Fail / Cloud Native / Internet scale IT :
 Exploit SoftLayer for Cloud Native
 Migrate IBM SmartCloud workloads into Softlayer workflow approach over time

47
OpenStack components; IBM Storage strategic exploitation
Horizon
Nova
Cinder
Swift
Neutron
Keystone
Glance
New in Havana
Metering (Ceilometer)
Basic Cloud Orchestration &
Service Definition (Heat)
Oslo
Shared Services
Software
Defined
Object
IBM
Storage
SVC / Storwize
XIV
Future
directions

48
OpenStack Object Storage component – “Swift”
 An open source, highly available, distributed, eventually consistent object store
– Two tier architecture consisting of client facing proxies and storage servers
– Information protected through three-way replication (by default)
– Supports geo-distribution
– The dominant design for scale-out object stores
 Swift was developed as pure software
disconnected from
hardware
– Typically implemented on
storage rich servers, e.g.,
– IBM x3630 M4
 Swift in production at Softlayer,
Rackspace, Korea
Telecom, Wikimedia,
 UCSD, Internap,
Sonian, MercadoLibre, . . .
Internet
or
Intranet
Private
Network
Clients send
REST
requests
Storage Servers (account,
container and object) store, serve
and manage data and metadata
partitioned based upon ring
Proxy Layer (public face)
authenticates and forwards to
appropriate storage server(s)
using ring

49
IBM Object Storage Cloud and IBM OpenStack directions
 2014 directions: a pure IBM Storage Software offering, based on OpenStack Swift,
with IBM value-add, providing object storage interface with highly available, cost
effective, scale out storage features.
– Leverage open source assets for a lightweight and flexible, interoperable foundation
 Target Markets
– Telco/CSP, MSP, HealthCare, FSS
 Scope
– Simple and Easy to use management
• Ease of Use XIV/Storwize GUI
• Build on community tools
• Smart Swift infrastructure management
• Cloud Support: Provisioning, Metering
– Multi-tenant security
• Authentication and management isolation
– Compliance
• Object Retention
– Architecturally able to scale
• To thousands of nodes
• Initial offerings much smaller
…
Private Network
…
Zone 1 Zone 2 Zone n
…
Object URL call:
http://<host>/<api versions>/<account>/<container>/<object>

50
IBM SmartCloud capabilities for major IT architectures
Scalable
Virtualized
Automated Lifecycle
Heterogeneous Infrastructure
Cloud Enabled
Elastic
Multi-tenant
Integrated Lifecycle
Standardized Infrastructure
Cloud Native
+
Existing
Middleware
Workloads
Emerging
Platform
Workloads
Compatibility with existing systems
Exploitation of new environments
“System of Engagement”
IBM SoftLayer
IBM SCE+
Traditional IT

51
SoftLayer provides world-wide services with a standardized modular
infrastructure; triple network architecture and powerful automation.
World-Wide Services
13 Data Centers
with 100,000 Servers and 22,000,000 Domains
in the US, Amsterdam and Singapore
19 Network Points of Presence
in 5 countries to facilitate response times
21,000 Customers
* Sold in US English, US $ Pricing
Tokyo
Hong Kong
Singapore
Seattle
San Jose
Los Angeles
Denver
Dallas (6)
Houston (2)
Chicago
New York City
Washington DC
Atlanta
Miami
London
Amsterdam
Frankfurt
Flexible, Automated Infrastructure
Data Center & Pods
• Standardized, modular hardware configurations
• Globally consistent service portfolio
Triple Network
• Public network for cloud services
• VPN for secure management
• Private network for communications and shared services
IMS (Automation Software)
• Bare metal provisioning
• Integrated BSS/OSS
• Comprehensive network management

52
Learning Points
 Cloud is being driven not only by cost,
but more importantly by:
– Time-to-market
– Elasticity
– Change business process
– Competitive imperatives
 Cloud is a significant shift in:
– Organizational lines
– Processes
– Workflows
– Workload types
– Required skill sets
 Cannot deliver true cloud services with
a traditional IT organization
– The workflow, process, responsibility,
reporting lines all different in cloud
– To provide elastic capacity, self-service E2E
automation
 Changing focus from on-premise
(traditional IT) to off-premise (cloud)
 IBM Cloud Storage products / directions
include:
– Traditional IT (on-prem or off-prem):
• Smart Cloud Storage Access, TPC,
Storwize, XIV
• OpenStack exploitation
– Object Storage
• Software defined object storage
– Design for Fail, Cloud Native IT:
• OpenStack + XIV/Storwize
• Softlayer

53
For more reading and reference, full decks by John Sing:
 “Building a 21st Century Cloud Storage Service – Industry Best Practices”
(external customer conference presentation):
– http://www.slideshare.net/johnsing1/building21stcenturycloudstorageservicejohnsingv4
 “State of the Cloud - Internet Scale Data Center Workloads – Comparison
to Traditional IT”: (external customer conference presentation):
– http://www.slideshare.net/johnsing1/s-ge01-
toinfinityandbeyond2012bigdatainternetscaleupdatev2johnsing-23463356
 “Disruptive Innovation in the Modern IT World”:
– http://www.slideshare.net/johnsing1/a-india-
csii2012disruptiveinnovationinthemodernitworldv3plenarypresentation
 “Hadoop – it’s not just Internal Storage”:
– http://www.slideshare.net/johnsing1/hadoopitsnotjustinternalstoragev14

54
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Grazie
Thank You
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55

56
Appendix: Disruptive Innovation

57
With all this opportunity……. Why is this Disruptive Change
flat-lining traditional consumer PC / desktop manufacturers?
 PC / laptop stalwarts
 Unsuccessful in shift
 To mobile
http://gigaom.com/2012/09/01/hp-dell-and-the-paradox-of-the-disrupted/
PC/laptop
market value
big decreases
Cloud / mobile
market value
*bigger increases*
Market
Capitalization

58
Observe: how fast mobile internet grows by 2014
 By 2014:
 Mobile will be
main way
 Of connecting to
Internet
Inter-
Disciplinary
http://www.digitalbuzzblog.com/2011-mobile-statistics-stats-facts-marketing-infographic

59
Disruptive Innovation
Definition:
 Create new
market and value
 Eventually
disrupts existing
 Displaces earlier
technology
Clayton Christensen
Harvard Business School
http://en.wikipedia.org/wiki/Disruptive_innovation

60
 Not “advanced
technologies”
 Inferior yet “good
enough”
 Novel combinations
 Starts low end
 Grows up-market
–“low end
disruption”
Clayton Christensen

61
 Learn lessons
 Watch today’s
world
Illustrative examples only

62
 “Consumerization”
 Not just technology
 Delivery models
(cloud)
 Business models
 Ecosystems
Clayton Christensen

63
Mobile has affected all business models…
Mobile =
Geo-locational superfood
Real-time analytics
http://www.digitalbuzzblog.com/2011-mobile-statistics-stats-facts-marketing-infographic

64
Cloud-scale Data Centers required for:
Data Supertransformagicability
TaxiWiz
HousingMaps
Source: http://mashable.com/2007/07/11/google-maps-mashups-2/
Weatherbug

65
By 2016, how much mobile data? What kind?
 2012:
–Mobile-connected
devices > # people
 2016:
–10 billion mobile devices
–(world population: 7.3 B)
http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.html
Smartphones
48%
Web data,
video
70%

66
 Big Data / Cloud on
disruptive path
 Traditional IT still
around but….
 Newer technologies
disrupt all platforms
Clayton Christensen
What will the effect be on
your IT organization?
Inter-
Disciplinary

67
Internet Scale Workload Characteristics - 1
 Embarrassingly parallel Internet workload
– Immense data sets, but relatively independent records being processed
• Example: billions of web pages, billions of log / cookie / click entries
– Web requests from different users essentially independent of each over
• Creating natural units of data partitioning and concurrency
• Lends itself well to cluster-level scheduling / load-balancing
– Independence = peak server performance not important
– What’s important is aggregate throughput of 100,000s of servers
i.e. Very low
inter-process
communication
 Workload Churn
– Well-defined, stable high level API’s (i.e. simple URLs)
– Software release cycles on the order of every couple of weeks
• Means Google’s entire core of search services rewritten in 2 years
– Great for rapid innovation
• Expect significant software re-writes to fix problems ongoing basis
– New products hyper-frequently emerge
• Often with workload-altering characteristics, example = YouTube

68
Internet Scale Workload Characteristics - 2
 Platform Homogeneity
– Single company owns, has technical capability, runs entire platform end-
to-end including an ecosystem
– Most Web applications more homogeneous than traditional IT
– With immense number of independent worldwide users
1% - 2% of all
Internet requests
fail*
Users can’t tell difference
between Internet down and
your system down
Hence 99% good enough
*The Data Center as a Computer: Introduction to Warehouse Scale Computing, p.81 Barroso, Holzle
http://www.morganclaypool.com/doi/pdf/10.2200/S00193ED1V01Y200905CAC006
 Fault-free operation via application middleware
– Some type of failure every few hours, including software bugs
– All hidden from users by fault-tolerant middleware
– Means hardware, software doesn’t have to be perfect
 Immense scale:
– Workload can’t be held within 1 server, or within max size tightly-clustered
memory-shared SMP
– Requires clusters of 1000s, 10000s of servers with corresponding PBs
storage, network, power, cooling, software
– Scale of compute power also makes possible apps such as Google Maps,
Google Translate, Amazon Web Services EC2, Facebook, etc.

69
Internet Scale data center power components…
Image courtesy of DLB Associates: D. Dyer, “Current trends/challenges in datacenter thermal management—a facilities perspective,”presentation at ITHERM, San Diego, CA, June 1, 2006.
“The Data Center as a Computer: Introduction to Warehouse Scale Computing”, figure 4-1, p.40 Barroso, Holzle

70
Breakdown of data center
energy overheads
Image courtesy of ASHRAE “The Data Center as a Computer: Introduction to Warehouse Scale Computing”, figure 5-2, p.49 Barroso, Holzle
Chiller alone is
33% of the cost
UPS alone is
18% of
construction
cost
Physical cooling,
UPS dominates the
electrical power cost

71
construction cost of Internet Scale Data Center is
Power / Cooling
Facebook’s
North Carolina
Data Center
Goes Live
Facebook:
Lulea,
Sweden -
290K sq ft
(27K sq
meters) by
late 2012
Facebook –
Prinville,
Oregon
Has spent
$1B on it’s
data
centers
Open
Compute
Project
? Reducing power profile
reduces
construction cost

72
Wow. Given that fact…..
Whose data centers are most
power efficient?
 Reducing power profile = lowers
initial CAPEX SIGNIFICANTLY
 Therefore, fundamental Internet
Scale Data Center goal is:
 Decrease Power Usage
Effectiveness (PUE)
 PUE =
http://gigaom.com/cloud/whose-data-centers-are-more-efficient-facebooks-or-googles/
Total Building Power consumed
---------------------------------------------
IT power consumed

73
Google claims its data centers use
50% less energy than competitors
 Power Usage Effectiveness
– PUE=1.14 means power overhead is
only 14%
– Industry average is around 1.8
http://venturebeat.com/2012/03/26/google-data-centers-use-less-energy/
Industry average
PUE is about 1.8
http://www.datacenterknowledge.com/archives/2011/05/10/uptime-institute-the-average-pue-is-1-8/

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