State of the Cloud presentation from Interop 09 Enterprise Cloud Summit

State of the Cloud
Enterprise Cloud Summit
New York, NY
November 17, 2009

Wednesday, November 18, 2009

Good morning, and welcome to ECS
Some introductory thoughts on clouds, how we got here, where we’re going.

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A bit about Bitcurrent

Analysis and research of emerging technologies
Cloud computing, web performance, human/computer
interaction, emergent communications technology


Peak of inﬂated
Visibility

expectations

Plateau of
productivity

Slope of
enlightenment
Technology Trough of
trigger disillusionment

Time
http://www.gartner.com/pages/story.php.id.8795.s.8.jsp

You’re probably all familiar with Gartner’s “Hype curve.” I’m sorry to say that, according to
them, we’re at the apogee -- the peak of inﬂated expectations. Disillusionment awaits us.
Then, of course, clouds will become a part of our lives.

The stages of grief
The loss of traditional IT.


I like to look at a slightly different curve. It’s the stages of grief, as IT loses its traditional
environments. This loss comes from a number of things:
- An inability to compete on cost versus the single-mindedness of cloud providers
- The changing patterns of data, storage, and computation that put users everywhere and
make workloads bursty
- A newfound desire for agility and faster pace of change and experimentation

Visibility

Bargaining

cept ance
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Anger

Denial Depression

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2005 2006 2007 2008 2009

http://developer.amazonwebservices.com/connect/thread.jspa?messageID=150461
http://www.google.com/insights/search/#q=%22Cloud%20computing%22&cmpt=q

Here’s a rough timeline of cloud computing’s growth in recent years.
First: Mentions on Google Insight
Then: Gartner’s Hype Curve, which they claim is a 2-5 year cycle
Then: The introduction of various services from Amazon
Then: Big computing companies that have inked deals with Amazon
Then: A history of some of the vendors and their initial cloud products

What we agree on:


In the last year, we’ve reached agreement on several things.

We have a taxonomy.


For starters, we have a taxonomy -- which is good. Sadly, however, we abandon it at a
moment’s notice.

Private Public

w a nt
you k SaaS
If t al ck
PaaS to , pi PaaS
u d s t.
IaaS c
lo fi rs IaaS
on e
Managed
hosting


If someone wants to have a conversation with me about clouds, they need to pick a tier, and
a private or public model. Then we can compare facts.

We (sort of) agree on how to
classify things.


Word processing SaaS
(Google Apps, Ofﬁce
Standard app, Copy content
Live, Basecamp,
no differentiation Freshbooks, Wufoo)

Support ticketing “Flavored” PaaS
(Quickbase, Bungee,
Custom app, key Rewrite process
Force.com, Webex
business process Connect)

Rewrite code Agnostic PaaS
Intranet site (App Engine, Heroku,
Application code Reasonablysmart,
Port code Azure)

JBoss Server Infrastructure
Application Port to VM/AMI cloud
(ECS, Joyent,
instance Rackspace, Azure)

What you want to move into the cloud will affect how you do it and what you move to.

Cloud responsibilities:
Who owns what layer?
IT user Cloud

?
App logic

APIs This is why
clouds are new
Operations

Architecture

Hardware

This is managed hosting
IT user Cloud
User deﬁnes it, writes
App logic code

Users talk to
APIs components directly

Operations User runs the machines

User designs how things
Architecture ﬁt together

Hardware Service provider owns it

This is strategic outsourcing
IT user Cloud
App logic code

Users talk to

User runs VMs, not
Operations physical ones

User designs how things
Architecture ﬁt together

Hardware User owns it

This is an IaaS cloud
IT user Cloud
App logic code

Users talk to

User runs VMs, not

User chooses from
Architecture predeﬁned menu


This is a PaaS cloud
IT user Cloud
App logic code

Users only talk to well-
APIs deﬁned services

User runs VMs, not

User chooses from


This is SaaS
IT user Cloud
Service provider writes
App logic and maintains it

Users only talk to well-
APIs deﬁned services

User runs VMs, not

User chooses from


This is a private cloud
Internal clients Internal IT
App logic code

Users talk to Users only talk to well-
APIs components directly deﬁned services

User runs VMs, not

User chooses from


We’ve stopped denying it.


Denial: Just timesharing all over
Insulates components Amazon S3 turns
SOA from functionality storage into a
through consistent APIs service
Reduces minimum order
Buy a slice for
Virtualization quantity; turns physical
things into logical ones just an hour

Means users are OK with
Standardization a menu of predefined LAMP, Rails, etc.
configurations

Increases the human-to-
Automation 10x enterprise
machine ratio & drives
marginal cost towards 0 efficiency ratios

This is the ranting of luddites and server-huggers
Of SOA, the insulation of components by consistent APIs
Of virtualization,which
- Reduces the minimum order quantity
- Makes automation possible by making the physical logical
Of platform standardization

Denial: just for startups
“[There are] 60,000 different customers across
the various Amazon Web Services, and most of
them are not the startups that are normally
associated with on-demand computing.
Rather the biggest customers in both number and
amount of computing resources consumed are
divisions of banks, pharmaceuticals companies
and other large corporations who try AWS once
for a temporary project, and then get hooked.”
http://www.techcrunch.com/2008/04/21/who-are-the-biggest-users-of-amazon-web-services-its-not-startups/


Even as early as last year, Amazon reported that the majority of its users and its compute
cycles were consumed by enterprise customers.

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Cloud Encounters, Peter van Eijk, digitalinfrastructures.nl

We have decent evidence that they can be relied on. Peter van Eijk is presenting this data at
CMG next month, but gave us an early look at some performance benchmarking he’s done on
Watchmouse, a European testing platform.

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Denial: they’re slow
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5%''%,!6%%,($7!0$48#,!9%''%,!,#3'($7:
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Peter’s data also shows that Amazon is making signiﬁcant headway with infrastructure
upgrades that improve performance.

Reality is setting in.


Reality:
Cloud operators have an
unbeatable cost advantage.


At this point, it’s hard to argue that cloud operators will win on a cost basis alone.

How to think about costs
800,000 Variable
Fixed
Upfront
600,000
Cost

400,000

200,000

0
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4


Any cost model consists of three kinds of spending: Upfront money spent to kick things off;
ﬁxed spending that doesn’t change whether you sell one or a billion units; and variable
spending associated with the amount you sell.

IT costs: Upfront

Capital investment (often, “capex”)
Don’t overlook rewriting, retooling, retraining, data
migration
For many enterprises, this is just the cost of periodic
upgrades. They already have equipment.


IT costs: Fixed

Happen no matter what; a measure of leanness
May be shared with other activities, and therefore not
eliminated (this is often invoked in defense of jobs)


Then there are the ﬁxed IT costs that you can’t avoid. Clouds can drop these, but if you have
IT running internal systems, they won’t magically evaporate when things move to the cloud.
What’s more, clouds mean new tasks for IT -- things like provisioning, managing policy, and
so on.

IT costs: Variable
Tied to delivery; a
measure of efﬁciency 500

Needs to be less

Servers per sysadmin
375
than the resulting
revenue or you’ll be 250
called a cost center
125
Enterprises
underestimate the true 0
costs of service delivery Enterprise Cloud provider

Barry Lynn of 3Tera


The variable costs are where clouds are really strong. This stuff is the costs that increase with
service delivery volumes. Cloud operators can handle 500-1000 servers per person (they
have to!) and completely automate everything. They also focus on cost measurement and
accounting, which is a luxury for many enterprises but a necessity for clouds. Management
software is an afterthought for many IT departments; but it’s a competitive advantage for
cloud operators.

Clouds might seem pricey today
£30,000,000

£22,500,000 Final score:
DC: £15M
Cloud: £26M
£15,000,000 After year 3,
cloud costs
exceed DC

£7,500,000 Even with 3-year
refresh cycles of 30%
DC remains cheaper
£0
Start up cost Year 2 Year 4 Year 6 Year 8 Year 10

Data Centre Cloud

2009 IDC analysis of running 100% of a big enterprise’s IT in-house vs on-demand
Used with permission. Copyright (c) IDC

A naive look at clouds (intentionally naive, we should point out) from IDC says that clouds are
expensive

But we’re deluded
£50,000,000 Year 6 requires build-
out for new facility +
expensive refresh due
DC reaches space to limited space
£37,500,000
capacity in year 3,
50% refresh to high-
end servers needed
£25,000,000

£12,500,000 Cloud costs are dynamic
so even if bad decisions
are made initially, capacity
can be ramped up linearly
£0
Start up cost Year 2 Year 4 Year 6 Year 8 Year 10

Data Centre Cloud

2009 IDC analysis of running 100% of a big enterprise’s IT in-house vs on-demand
Used with permission. Copyright (c) IDC

Remember: Google gets 38% server utilization, with insane effort. So this is likely
unattainable. What’s more, cloud providers are competing, and may (assuming
interoperability of some kind) reduce their costs, too.

http://www.oncloudcomputing.com/en/2009/07/fronde-back-to-proﬁt-by-cloud-computing/

Just how big are clouds? Consider that in July 2008, Microsoft revealed that it had 96,000
servers at the Quincy facility, consuming "about 11 megawatts"
More than 80% dedicated to Microsoft's Live Search and the remaining for Hotmail
In August, a really good discovery was posted to a blog called "istartedsomething.com": a
screen shot of a software dashboard that illustrates power consumption and server count at
each of Microsoft's ﬁfteen data centers, caught in a Microsoft video posted to their web site.

Are you negotiating with
cities & power companies?

“...Microsoft pays an annual utility bill just north of $13
million, which translates to just over 3.8 cents/kwh as
opposed to 5.7 cents/kwh for the ELP rate...”

http://www.virtual-strategy.com/Features/Microsoft-and-Google-Cloud-Computing-Dominance-Through-Renewable-Energy/-5.html


Consider a San Antonio, Texas facility from Microsoft.
http://ccr.sigcomm.org/online/ﬁles/p68-v39n1o-greenberg.pdf
if the data center takes the full load of 44 megawatts at a 90% load factor, Microsoft pays an
annual utility bill just north of $13 million, which translates to just over 3.8 cents/kwh as
opposed to 5.7 cents/kwh for the ELP rate. To prove that these assumptions are in the
ballpark, public documents from another SLP customer in the San Antonio area reveal that its
overall utility rate is 3.7 cents per kwh.

Three laws


http://www.galleries.com/minerals/elements/silicon/silicon.htm

First, consider silicon. Look at the cost/capacity tradeoff of computing, as described by
Moore’s Law.

http://www.ﬂickr.com/photos/monstershaq2000/2162386152/

Then think about another form of sand – glass. Then look at the cost/capacity tradeoff of
networking. Netﬂix pays $0.06 to send a movie over the Internet today, and will pay $0.03
next year.

http://www.ﬂickr.com/photos/spacepleb/801902842/

Finally, think about iron. And consider storage – which is dropping just as quickly.

The cloud trifecta


This trifecta of computing, bandwidth, and storage are driving costs down dramatically. Every
time Google builds a data center, it can do more than the last one did.

Everything will be free.*

*Some restrictions apply.


Cloud computing is on a breakneck ride to zero marginal costs because of sand, iron, and
glass. This means the raw materials of clouds will be free -- or too cheap to bill -- for many
of us. (if you want to know more about this, see Chris Anderson’s Free)

So you won’t be building
your own data centers
70% of the Global 1000 must
“Modify their data center facilities signiﬁcantly” by 2012
Increase energy from 35 to 70 watts/sq. ft (sometimes up
to 300 watts)
Gartner says to
Monitor energy use
Quantifying all capital and operation changes needed
Deploy virtualization and workload management tools

http://www.virtual-strategy.com/Features/Microsoft-and-Google-Cloud-Computing-Dominance-Through-Renewable-Energy.html

Energy is a huge issue. Even Gartner’s recommendations for saving energy will only
temporarily solve the problem at hand, because energy costs will have to be cut by more than
50% in order to keep up


This is a GM prototype of a car that drives itself. It’s actually green technology. Know why?
Because in the end, the greenest thing you can do for a car isn’t fuel: It’s making it not crash
as much. If cars didn’t crash, we could get rid of most of their weight, which in turn would
make them efficient. It turns out that IT is the key to efficiency.

If you’re using others’
servers, you’ll get VMs.


Let’s resign ourselves to the fact that we’ll get hardware from someone else (there’s a reason
Intel is investing in cloud companies like Joyent, remember.) So how will that work? You’re
going to get virtual machines, because that’s how the operators keep the costs low. IT and
management, not cheaper machines, is the key to efficiency.

Reality:
Clouds are part of the IT
toolbox



Clouds let IT focus on things that actually add business value. Very few companies have a
competitive advantage because of their hardware infrastructure.


And they eliminate many of the tasks you really didn’t want to do anyway.

Reality:
Security is a pro and a con.


New kinds of attack The best infosec people
Third-party access More automation
Traveling across wires High-end tools
Shared infrastructure Billing to catch use spikes


With hypervisors, other people involved, wires to cross, and so on, there are new vectors for
attack. Those have to be compared to the more rigorous standardization that a cloud is likely
to subject things to.

Reason to avoid clouds
23%
Reason to move to clouds
43%

No opinion
34%

http://www.thewhir.com/web-hosting-news/102309_IT_Firms_Skeptical_About_Cloud_PEER_1_Study

In a study commissioned by PEER 1, users reported security as a big impediment to cloud
adoption -- and a reason for doing so!"

Reality:
It’s about services, not
machines


While virtual machines were easy to understand and embrace, we’ve ﬁnally realized that it’s
the services, not the machines, that matter.

Embracing clouds
means giving up
architectural opinions.


SOA may matter more than
virtualization I used to
think here...
SimpleDB
RDB

Elastic MapReduce EC2

SQS Loadbalance

CloudFront S3
...now I think
out here

http://www.techcrunch.com/2009/04/16/mckinseys-cloud-computing-report-is-partly-cloudy/

What started out as pay-by-the-drink storage (S3) and computational processing (EC2), now
includes a simple database (SimpleDB), a content delivery network (CloudFront), and
computer-to-computer messaging (SQS). Most recently, Amazon added a web-scale data
processing engine with Amazon Elastic MapReduce. (It is a framework for accessing data
stored in ﬁle systems and databases). It allows developers leverage Amazon’s cloud
computing power by creating applications which process huge reservoirs of data
(conveniently stored in Amazon S3) in parallel.
Developers become systems integrators

Reality:
Clouds are ubiquitous.


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Reality:
He who owns the storage,
owns the computation.


It’s all about the data


Data is the most important part of a cloud. MS fellow Jim Gray, in his 2003 analysis, said that
compared to the cost of moving bytes around, everything else is effectively free.
The economics of storage services like Flickr don’t hold up well to churn.

Moving data’s not easy
Speed Rent $/TB
Context $/Mbps Time/TB
Mbps $/month Sent
Home phone 0.04 40 1,000 3,086 6 years
Home DSL 0.6 50 117 360 5 months
T1 1.5 1,200 800 2,469 2 months
T3 43 28,000 651 2,010 2 days
OC3 155 49,000 316 976 14 hours
OC 192 9600 1,920,000 200 617 14 minutes
100 Mpbs 100 1 day
Gbps 1000 2.2 hours
Source: TeraScale Sneakernet, Microsoft Research, Gray et. al
58


One dirty secret of cloud computing is that from a cost perspective, everything’s pretty much
free compared to the price of moving bytes around. This means you can no more build an
app that’s “half cloud” than you can be “half pregnant.”

Reality:
The big guys are here.


Legitimacy, at the cost of FUD and a slow-down of experimentation because big vendors can
promise.

IBM

Replacing Global Services
Architecture deﬁnes clouds


Microsoft

SaaS cannibalizes existing software addiction


AT&T

It’s about data centers and connectivity


We’re stalling.


What’s the consensus?
(the no clear direction
problem)


No straight answer
38% 47%
ITI “Unsure about adopting “Won’t consider the cloud in
cloud services” next 12 months”

F5 Networks 82%
“In trial, implementation, or use of public clouds”

“Implementing
cloud services”

60% 8%
CIO.com 29% “Actively researching (cloud on
“No interest in the cloud”
radar)”

0% 25% 50% 75% 100%


What’s included?
(the roofrack problem)


http://www.thule-car-roof-boxes.co.uk/pictures/roof-box-with-roof-rack.jpg

Too much choice
(the wait it out problem)


http://www.ﬂickr.com/photos/jumphigh/1565967960/


Jim Sivers reminded me recently of the paradox of choice. http://sivers.org/jam
Sheena Iyengar has been studying choice. For her research paper, “When Choice is
Demotivating”,They set up a free tasting booth in a grocery store, with six different jams. 40%
of the customers stopped to taste. 30% of those bought some.
A week later, they set up the same booth in the same store, but this time with twenty-four
different jams. 60% of the customers stopped to taste. But only 3% bought some!

60

45

30

15

0
Stopped to taste Actually bought some

6 jams 24 jams

http://sivers.org/jam


Both groups actually tasted an average of 1.5 jams. So the huge difference in buying can’t be
blamed on the 24-jam customers being full. Lessons learned:
Having many choices seems appealing (40% vs 60% stopped to taste)
Having many choices makes them 10 times less likely to buy (30% vs 3% actually bought)
Surgeon Atul Gawande found that 65% of people surveyed said if they were to get cancer,
they’d want to choose their own treatment. Among people surveyed who really do have
cancer, only 12% of patients want to choose their own treatment.

General population Cancer patients

13%

35%

65%

87%

Choose their own treatment
Have others choose

http://sivers.org/jam


Surgeon Atul Gawande found that 65% of people surveyed said if they were to get cancer,
they’d want to choose their own treatment. Among people surveyed who really do have
cancer, only 12% of patients want to choose their own treatment.

Striking a balance
How we move ahead


Focus on service
architectures


Composed designs replace
component architectures
High performance
Compliant
“Embarrassingly distributed”
Bursty/seasonal
Resilient & highly available
Scalable but eventually consistent


Have a hybrid/private
strategy


“Private” and “hybrid”
concepts emerge
Private cloud: On-premise infrastructure with cloud-like
properties
Hybrid cloud: Policy-driven combination of on-premise
and on-demand components
Virtual private cloud: On-premise privacy on someone
else’s machines


Compute task
(service cloud)

Virtual machine
(infrastructure cloud)

Always on Can be done Always in
premise anywhere cloud

Load/pricing engine
Private
Partner access
Compliance- Testing
enforced Proximity to cloud
Training services (storage,
Policy engine

Need to track and
Prototyping CDN, etc.)
audit
Batch processing Massively grid/
Legislative
Seasonal load parallel (genomic,
Data near local modelling)
computation


Going forward, we’ll see hybrid on-premise/on demand hybrid clouds that can intelligently
move processing tasks between private an public infrastructure according to performance
requirements, pricing policies, and security restrictions.

Reconsider what’s possible


Better economics Developer empowerment
Pay-per-use pricing Self-service portal
No capital investment Infrastructure managed by
cloud provider
No long term contracts
Developer-ready framework
Ideal for spiky applications
For all levels of developers
Optimized for Web 2.0
apps Cheap test and dev
Scales easily* One button deploy

* Easy scaling may not be included
James Staten, Forrester


Clouds promise a lot: James Staten of Forrester loves clouds, not only for the economies of
scale they offer, but also for the way in which they empower developers to build and
experiment by speeding up the IT cycle time.

Enterprise tech Disruptive tech
Minimize maintenance costs Do new things
Cooling Perform mission tasks that were not
Electricity able to achieve otherwise
Servers maintenance, backups, etc. New tools like Hadoop and Map
Elasticity and scalability Reduce allows for amazing processing
Massive scale leads to true Speed up the organization
economies of scale Faster, cheaper innovation
Eliminate need to build for Transform how gov does business
infrequent peaks Prototyping enablement
Make capacity available on demand Publish databases
Ops cost reduction Reduce start-up
Flat data sets Work differently
Streamlined data management Realtime collaboration
Data availability - enabling Ubiquitous access to unlimited
information generation amount of computing power
DR cost reduction Ubiquitous access to unlimited
amount of storage

Rod Fontecilla, Booz, Allen, Hamilton

• 60 seconds per page
Desktop EC2 • 200 machine
Pages 17,481 17,481 instances
Minutes/page 1 1 • 1,407 hours of virtual
# of machines 1 200 machine time
Total minutes 17,481 • Searchable database
Total hours 291.4 26.0 available 26 hours
Total days 12.1 1.1 later
• $144.62 total cost

One of the most interesting uses of cloud computing is time dilation. Okay, not really, but close: The Washington Post, needed to get
all 17,481 pages of Hillary Clinton’s White House schedule scanned and searchable quickly. Using 200 machines, the Post was able
to get the data to reporters in only 26 hours. In fact, the experiment is even more compelling: Desktop OCR took about 30 minutes
per page to properly scan, read, resize, and format each page – which means that it would have taken nearly a year, and cost $123
in power, to do the work on a single machine.

Two kinds of data center
Really big data centers for really big Requires lots of communication
problems between servers, so network
propagation affects computation
Tens of thousands or more servers
speed
Tens of Mega-Watts of power at
Micro data centers for “embarrassingly
peak
distributed” applications
Aimed at massive data analysis
Thousands of servers
applications (search indexes, social
media, genomics) 100s of kilowatts.
Variety of workloads Aimed at highly interactive apps
(Interactive, ofﬁce productivity apps)
Huge amounts of fast RAM
Placed close to populations to
Massive numbers of CPU cycles
minimize network transit impact
High-volume disk I/O bandwidth

The Cost of a Cloud: Research Problems in Data Center Networks
Albert Greenberg, James Hamilton, David A. Maltz, Parveen Patel Microsoft Research, Redmond, WA, USA

Think about risk in the
context of openness


Sharing > Protection

Drew Bartkiewicz, The Hartford, quoted in Unseen Liability

According to Drew “Bartievitz” of the Hartford, there’s a shift in the value of information
assets underway.

Embrace cloud technology
even if you don’t use clouds


An example:
eventual
consistency


Clouds as Clouds as
peripherals IT strategy


Most of the enterprises I’ve spoken with use clouds as peripherals. In the same way we used
to plug peripherals into our computers, enterprises plug clouds into their IT. They might have
it for backup, or messaging, or content delivery, or for a speciﬁc business process. But to
really harness the power of cloud computing, enterprises need to embrace it as more than
just a bunch of things to plug into the organization. It needs to become part of their strategy.

Support
Contracts
UI
Language

Computing
Storage
Delivery

Protocol

API
Policies
Onboarding

You can target a vertical. There are always ways to specialize within a specific industry. This
isn’t about the computing -- as we’ve seen, this is a commodity. But you can <click> focus
on a specific language or protocol, <click> UI or API, <click>, set of contracts and policies,
<click>, or even support and onboarding. Every industry or target customer has specific
needs. Maybe it’s the AMQP protocol, or HTML 5 optimization, or JavaScript code, or long
contract terms, or high-touch support for small businesses.

Worry about user
experience, billing


What user experience can
you afford?


Trafﬁc (requests/sec)
Delay (in seconds) =
Capactity (# of machines)


There’s a basic equation in computing. Performance equals traffic divided by capacity. Put
another way, more users and something gets slower. More machines and something gets
faster.


This is an example of that relationship. As usage grows, performance gets worse.


Normally, IT adds capacity to a system and things get better.

Trafﬁc (requests/sec)
Delay (in seconds) =
Capactity (# of machines)

∞

But when if the capacity is inﬁnite?


Then you set user experience (“under 1 second”) and the elastic platform adds capacity as
needed. The only problem? The bill at the end of the month!

100

75

50
ROI, TCO, Designs &
Taxonomies Business Policy &
business best
& layers strategy standards
cases practices
25

0

2008 2009 2010 2011 2012
What is Why How do I What new What must
the cloud? should I use it? things are I still run
use it? possible? in-house?
http://developer.amazonwebservices.com/connect/thread.jspa?messageID=150461
http://www.google.com/insights/search/#q=%22Cloud%20computing%22&cmpt=q

Here are my predictions for the next few years, and what you’ll see at conferences, in the
press, and in the boardroom.

Different Clouds for Different Folks
Ian Knox (Skytap), Lew Moorman (Rackspace), Sesh Murthy (IBM),
Scott Ryan (Asankya)

The Risks of On-Demand Computing
Anthony Arnott (Trend Micro), Drew Bartewicz (The Hartford), Marc
Lindsey (Levine, Blaszak, Block & Boothby LLP)

What's Working, What's Not: A Report from
Cloud Adopters
Colin Hostert (Grooveshark), Geir Magnusson (Gilt), Dominic Preuss
(FiLife), Vince Stephens (Taser)

Cloud Interoperability: Do We Need It? What
Would it Look Like?
Chris Brown (Opscode), Jason Hoffman (Joyent), John Willis (Zabovo)

Cloud Computing Roadmaps
Ken Comee (Cast Iron Systems), Morris Panner (OpenAir), Randy Bias
(Cloudscaling)


State of the Cloud presentation from Interop 09 Enterprise Cloud Summit

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