Cloud Slam Co D Presentation

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Confronting the Data Center Crisis: A Cost - Benefit Analysis of the
IBM Computing on Demand (CoD) Cloud Offering

Reducing TCO and Enabling New Capability, Faster Time to Results,
and New Business Models

April, 2009

Dr. Srini Chari
chari@cabotpartners.com
Phone: 203 205 0705

Cabot
http://www.cabotpartners.com Prepared for Cloud Slam 09
Partners
Sponsored in part by IBM

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Outline

Cloud computing for commercial enterprises
Data center challenges and solutions
Workload classification-making the case for
cloud computing
IBM Computing on Demand (CoD) cloud offering
A cost-benefit framework for IBM CoD using the
Uptime Institute model for TCO
Customer examples highlighting business value
Copyright © 2009 Cabot Partners Group, Inc.

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IT Clouds vs. Airline Transportation –
Transport IT to the Cloud!
Unlike typical analogies of cloud computing to commodity electric utilities, IT clouds are
more analogous to the airline transportation industry with a range of business models.

IT Industry Transportation Cost Model Business
Impact
In-House Own planes CapEx, OpEx, Total control and
Data Center long term responsible for
or Private entire infrastructure
Cloud (not- including service
outsourced) levels
Private Cloud Lease planes and CapEx and Control of
(outsourced) service OpEx in infrastructure but
medium term not fully responsible
lease for service levels
Public Cloud Commercial Pay as you use, Almost no control of
airlines very short infrastructure or
term/trip/user service levels
Hybrid Charter planes Pay for duration, Some control of
short to medium infrastructure and
term/trip depend on provider
for service levels for
duration

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Data center challenges

 Escalating energy costs – about 11% annually while new server
spend expected to be flat1
 Industry is defining new metrics for energy efficiency2
 Data centers account for about 25% of enterprise IT budgets and is
growing at 20% annually3
 Investment required to build a large-enterprise data center has
risen to $500M, from $150M, over the past 5 years and larger data
centers take 2 years or more to design and build and are expected
to last for 12 years3
 Server utilization typically tops off at 5%-15% and results in wasted
energy and unemployed capital3
 Difficult to forecast whether a 50% increase in demand would
require 25% or 100% more server and data center capacity3

1. Jed Scaramella, “Worldwide Server Power and Cooling Expense 2006-2010 Forecast”, September 2006.
2. John R. Stanley, Kenneth Brill, and Jonathan Koomey, “Four Metrics Define Data Center “Greenness””, White Paper, Uptime Institute.
3. McKinsey on Business Technology, Innovations in IT Management, Number 14, Winter 2008.

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Data center solution approaches

 McKinsey3 suggests a centralized governance model with the CIO
empowered by the CEO to manage data centers by:
 managing IT assets aggressively through virtualization,
 providing incentives to IT personnel to improve forecasting and minimize
deviations from real demand,
 treating data center resources as scarce resources and ensuring that business
units implement a total cost of ownership (TCO) model for new systems and
applications,
 implementing new metrics for data center efficiencies that account for energy,
utilization, and floor space.
 What’s also needed is a comprehensive approach especially for emerging
web and analytic workloads that includes
 energy-efficient and computationally dense systems,
 software for better systems and power management and utilization, and
 flexible delivery models such as cloud computing that adapt easily to computing
demands.

3. McKinsey on Business Technology, Innovations in IT Management, Number 14, Winter 2008.

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Some specific IT solutions from IBM

 Energy efficient systems and next generation data centers that reduce
TCO4, 5
 Improve asset utilization through
 virtualization
 workload management
 consolidation
 Significant recent IBM cloud computing6 and dynamic infrastructure7
announcements that include a comprehensive roadmap of
 systems
 software
 services
4. Srini Chari, “IBM System x iDataPlex: The Newest Economical Workhorse in the Computing Cloud for Next Generation HPC Data Centers”,
Cabot Partners White Paper, April, 2008, ftp://ftp.software.ibm.com/common/ssi/sa/wh/n/xsw03016usen/XSW03016USEN.PDF
5. Srini Chari, “A Total Cost of Ownership Study (TCO) Comparing the IBM Blue Gene/P with Other Cluster Systems for High Performance
Computing”, Cabot Partners White Paper, November 2008, http://www-03.ibm.com/systems/resources/tcopaper_finalfinal_2008.pdf
6. IBM Cloud Computing, http://www.ibm.com/ibm/cloud/
7. IBM Prepares to Take on 21st Century Infrastructure, http://www.networkworld.com/news/2009/020909-ibm-dynamic-infrastructure.html

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High level IT workload classification by application

Private

Hybrid

Public - Free Web Search

Web 2.0 and Web
Transactional
Analytics
Web Applications
Public – Pay HPC
Workload Variability VW

for Use
Virtualization Could
Help Business
Intelligence/Analytics
Workload Schedulers
Could Help

Traditional Transaction
IT workload
Processing and ERP
size
Bubble dimension indicative of
aggregate IT workload size
Compute Intensive/Job
measured in MIPS or FLOPS
Trends indicated by the arrow are qualitative based on several recent public announcements on
cloud computing and other industry research on workload characteristics


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Typical low variability workload graph –
common in enterprise business applications

Peak Capacity
Aggregate Workload

In House Capacity

Average Utilization

W(t)

Time
•Shaded area represents under provisioning of IT resources that could lead to dissatisfied
users and adverse business impacts
•In house capacity designed for peak results in some over provisioning and wasted
resources

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Pulsed (high variability) workload graph –
common in departmental or SMB HPC

Peak Capacity
Aggregate Workload

In House Capacity
Average Utilization

W(t)

Time
• In house capacity designed for peak results in excessive over provisioning and wasted
resources

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Many pulsed (medium variability) workload
graph – common in large enterprise HPC

Peak Capacity
Aggregate Workload

In House Capacity

Average Utilization

W(t)

Time
• In house capacity designed for peak results in some excessive over provisioning and wasted
resources but average utilization improves compared with the single pulse case

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IBM Computing on Demand (CoD) –
Infrastructure as a Service (IaaS)*

Computing on Demand Base Access
 One year annual membership  Server management node
 Virtual Private Network (VPN) access  Client scheduling GUI

 Variable / Dynamic
Computing on Demand Capacity Models – Reserve by the week/hour
 Dedicated – “Pay for use”
– Dedicated resources: 1-3 year – Utilize CoD Cloud
commitment infrastructure
– Custom Environment – choice of data – Highly-secure, virtual
center, servers, storage, dedicated clusters dedicated to one
client at a time
switch, WAN/LAN high availability
network, add-on services – Automated network and
– Leased by IBM Global Financing server provisioning
(Click to Enlarge in Screen Show)

CoD Cloud Centers
 Tokyo
 New York (POK)
– 200 CPU’s (System x)
– 7,000 CPU’s (System x, System p)
 Future locations based on
 London
customer need
– 7,800 CPU’s (System x); dedicated
only
* Obtained from IBM

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IBM Computing on Demand – IaaS/PaaS*

CoD – Platform as a Service Customer Cloud Services IBM Cloud Services [GTS/SWG]
Partner Cloud Services
Developer Cloud
SaaS with CoD Partners High Performance Cloud Business Client Test Cloud
( eg: Exa, Ingrain Rocks, … )
Test Drive Cloud (Dedicated, Variable, Desktop Cloud BCRS / IPS [GTS]
Cloud Broker – Resellers More to come…. Dynamic, …) LotusLive™ Cloud
( eg: Nimbis, GridCore, … )

CoD - Infrastructure as a Service [STG]
Operational Services Interfaces
Platform Products Business Services Add On Services
System x™ Aspera Hi Speed File Transfer
Order Management VPN Network Connectivity
Data Center Locations *
Reporting
BladeCenter® Data Center Security,HA 3D Visualization (DCV) Custom Internet access portal
Contracts Online
System p™ Storage Virtualization (GPFS)
Provisioning, Monitoring Client Scheduling GUI
Account Management
System Storage™ Infrastructure Management
SLA Management

* * Centers operating in London, Tokyo, New York. Additional Centers under consideration in US, Europe and Asia

* Obtained from IBM

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Cost – Benefit framework for IBM CoD
Through IBM client interviews across several industries and other industry
and workload analyses, the benefits and costs are examined.

 Benefits
 Business Value: e.g. customer revenues, new business models, compliance
regulations, better products, increased business insight, and new
breakthrough capability,
 Operational Value: e.g. faster time to results, more accurate analyses, more
users supported, improved user productivity, better capacity planning,
 IT Value: e.g. improved system utilization, manageability, administration,
and provisioning, scalability, reduced downtime, access to robust proven
technology and expertise.
 Costs
 Data Center Capital Purchases Avoided e.g. new servers, storage,
networks, power distribution units, chillers, etc.
 Data Center Facilities Not Built e.g. land, buildings, containers, etc.
 Operational Costs: e.g. labor, energy, maintenance, software license, etc.
 Other Costs: e.g. deployment and training, downtime, bandwidth, etc.


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Ideal flat HPC large enterprise workload

Peak Capacity =
T In-House Capacity
W(t)
Aggregate Workload

Time
•Ideal Flat HPC Workload Occurs When Many Equal Intensity Pulses Coalesce with Perfect
Sequencing and Scheduling of the Workload. Illustrates an Ideal Large Enterprise HPC Case.
• In house capacity = Peak Capacity and Utilization is Maximized

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TCO Analysis for IBM-CoD Dedicated vs. In-House8, 9

Dual Core x86 Cluster System with 840 CPUs (cores)
and 1TB of Storage.

3
2.68
30%
2.5 30%
TCO
Annual Cost ($M)

TCO
1.87 IBM CoD
2 Savings
Savings
Other OpEx
1.5 Site
IT CapEx
1 Energy

0.5

0
In-House IBM CoD ($.25/CPU Hr.)

8. Jonathan Koomey, “A Simple Model for Determining True Total Cost of Ownership for Data Centers”, White Paper, The Uptime Institute, 2007.
9. TCO model adapted by Cabot Partners

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Single Short Pulsed Workload Graph

Peak Capacity =
T In-House Capacity
Aggregate Workload

TP

Time
•Flat Pulsed HPC Workload Occurs When One Large Intensity Workload is Scheduled and
Perfectly Balanced
•In house capacity = Peak Capacity and Utilization is Approximately TP / T, where TP is the
Duration of the Pulse

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TCO analysis for IBM-CoD Variable or Dynamic vs.
In-House

Dual Core x86 Cluster System with 840 CPUs (cores)
and 1TB of Storage.
69% TCO
69% TCO
3
Savings with
2.68 Savings with
Dynamic CoD
61% TCO
2.5 Dynamic CoD
61% TCO
Savings with
Annual Cost ($M)

Savings with
IBM CoD
2 Variable CoD
Variable CoD
Other OpEx
1.5 Site
IT CapEx
1.0
1 Energy
.84
5
0.5

0
In-House IBM CoD - Variable IBM CoD - Dynamic

 CoD-Variable assumes 1 week commitment and used 12 times a year ($.6/CPU Hour assumed)
 CoD-Dynamic assumes 10 hour/day for 5 days commitment and used 24 times a year ($.8/CPU Hour
assumed)

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Customer cases highlighting business benefits-
Summary

 Top international wealth management savings company – New
Capability
 Major New York based financial conglomerate - Faster Time to
Results
 Ingrain Rocks-Solution provider for Petroleum E&P - New
Business Model


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Customer cases highlighting business benefits: Top international
wealth management savings company – New Capability

 Challenge: immediate solution for spike in 5 times the risk analysis resulting from new European MCEV regulations
 Applications Used: actuarial software for risk analysis for regulatory compliance, new products, pricing, valuation, etc.
 Alternatives Investigated:
 Upgrading/adding existing servers in their in-house compute grid. Entailed expensive capital acquisition. Physical space was
also a limiting factor.
 Using the services and infrastructure provided by the ISV. The ISV’s HPC infrastructure was not robust and scalable to address
the extreme computing needs for the added statistical analyses.
 A “container” type solution was also evaluated. No available land in the vicinity to deploy a container solution.
 Why IBM CoD was Chosen:
 provided flexible and variable access to over 100 IBM System x servers for 3 weeks in a month.
 pricing and terms were attractive and VPN access options made this environment secure, stable, and isolated from the existing
compute grid.
 additional savings of about 1PY were possible as IBM provided the support and service necessary to maintain this compute
capacity.
 IBM was able to collaboratively solve minor migration issues with application deployment on an IBM Windows HPC cluster at
the CoD center.
 Ongoing IBM CoD Value:
 the CoD solution with variable pricing terms has been consistently used over the last 6 months with over 75% utilization
 made feasible a previously “intractable” problem as alternative approaches did not satisfy the immediate business need
 implemented an IBM CoD solution in one subsidiary in the United States. They’re now able to get 5-7 times more work done
in the same timeframe or the same amount of work done 5 times faster.
 It is expected that this solution would also be considered in the corporate parent organization in Europe and at other subsidiaries
worldwide.
 access to almost “infinite” resources and the pricing elasticity offered by IBM CoD make this solution well poised for future
growth needs.

Customer case highlighting business benefits: Major New York based 20
financial conglomerate - Faster Time to Results

Challenges:

The internal IT group supports a wide range of internal clients and has to adhere to very stringent service level

expectations for risk analysis.
End-users demand simple, reliable, timely, secure, and “transparent” IT services.

End-of-day analyses must be turned around in 10 hours or less.

The current financial crisis, increased regulations, and market volatility have spiked the demand for more

complex stochastic risk analyses.
Applications Used: MG-ALFA from Milliman, Inc. on DataSynapse

Alternatives Investigated:

Adding more servers in the compute grid. This IT capital expansion was expensive and would be grossly

under-utilized when time critical risk analyses workload was not running.
Why IBM CoD was Chosen:

Provided a flexible and dynamic access to between 200 and 350 CPUs.

There have been 4-5 times in the last year that an urgent increase in capacity of up to 500 CPUs was needed.

IBM was able to respond in a matter of hours to these business critical requests. In every case, IBM exceeded
this firm’s expectations.
Ongoing IBM CoD Value:

The solution has been consistently used for time critical analyses over the last 2-3 years often with 75%-100%

utilization.
The firm expects increased scale and capacity use of CoD over the near future and is very pleased with IBM’s

exceptional service and support, rapid response to urgent requests, and deep expertise in HPC and grid
computing.
IBM’s deep relationships and expertise with software partners – Milliman and DataSynapse- were also

invaluable.


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Customer case highlighting business benefits:Ingrain Rocks-Solution
provider for Petroleum E&P - New Business Model

Challenges:

In 12-18 months, Ingrain desires to reduce end-to-end cycle time from months to days or less.

A key computational step is expected to become more critical as client-delivery time shrinks.

Ingrain’s business model is to avoid investing in IT in-house but they need global access to secure, flexible,

and “infinite” computing resources and must ensure that IT costs track client workload.
Needed a stable and trusted partner for the long haul.

Applications Used:

Proprietary high resolution imaging algorithms to compute physical properties of reservoir rocks and also

compute multiphase flow at the pore scale.
Parallel applications scale well into the 100s of processing nodes.

Alternatives Investigated:

Currently has cluster access through a local service to develop their applications. However, this model is

inadequate for anticipated future growth needs.
Examined other service providers but none could meet their needs of access to a range of equipment,

configurations, capacity, price points, and global capability. Flexibility in machine configurations, allows
Ingrain to optimize their application workload and tune the underlying algorithms.
Why IBM CoD was Chosen: IBM was the only provider that was global and satisfied all of Ingrain’s needs by

providing access to a range of systems, flexible configurations, almost “infinite” capacity, and attractive price points.
Mr. Stewart, CFO of Ingrain says “The IBM CoD solution is an absolute business necessity for Ingrain. It is a must
have solution. IBM has provided outstanding service and support to help Ingrain migrate and optimize their
applications on the CoD clusters. Ingrain has completed testing and quality assurance of their application in a very
short time. Ingrain has observed a 15%-20% improvement in performance and can now scale the workload to much
larger configurations”.
Ongoing IBM CoD Value: Ingrain plans to continue to use this CoD solution for all their future client engagements

and expect their use of the CoD solution to grow in the future. This will allow them to tackle more challenging
projects. In 12-18 months, Ingrain expects to have a very efficient end-to-end process that will enable them to deliver
results to their clients in days or hours instead of a couple of months.


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Cost-Benefit Analysis of IBM CoD - Summary

 For a sample configuration using IBM CoD, TCO savings
ranged from 30% to 69%

 Top international wealth management savings company – New
Capability
 Major New York based financial conglomerate - Faster Time to
Results
 Ingrain Rocks-Solution provider for Petroleum E&P - New
Business Model


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More Information

To learn more about the IBM Computing on Demand (CoD) solution, contact
your IBM representative or visit http://ibm.com/deepcomputing/cod.

To test drive IBM Computing on Demand, please visit
http://ibm.com/systems/deepcomputing/cod/testdrive.html.

To learn more about IBM Cloud Computing Solutions, please visit
http://ibm.com/cloud.

To learn more about IBM solutions for High Performance Computing, please
visit http://ibm.com/deepcomputing.

Copyright ® 2009. Cabot Partners Group. Inc. All rights reserved. Other companies’ product names or trademarks or service marks are
used herein for identification only and belong to their respective owners. All data used in this study were obtained from public sources or
from IBM. Cabot Partners does not guarantee the accuracy or currency of this information and the subsequent analyses. Changing market
conditions and other factors could alter the conclusions of this study. An objective analysis is required and strongly encouraged for
specific and custom deployments.


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