Green (low carbon) Data Center Blog

Bill Gates says better Software Modeling is key to solve complex problems

About two years ago, I started down the path of studying software modeling methods that work for data centers.  Over the last 6 months I've seen more growth in this area. 

With Bill Gates making the calling for software modeling for complex problems, there is a good chance there will be even more growth over the next 6 months.

CNET's Ina Fried reports on Bill's conversation on software modeling.

 

August 6, 2010 12:35 PM PDT

Bill Gates: Better software modeling is a key

by Ina Fried

LAKE TAHOE, Calif.--A key to many hard problems, from using nuclear power to combating diseases, is better software modeling, Bill Gates said on Friday.

While it's not surprising that he's a fan of using software to help solve hard problems, it is somewhat surprising that there aren't already good models for some diseases.

"There's no disease-modeling software," he said, speaking at the end of the three-day Techonomy conference here. "There is none. Why is flu seasonal? We don't know."

Why model? Bill says.

He conceded that the models never get things right exactly, but he said, "You are a hundred times smarter doing it that way than just going out there (without such models)."

"What you are doing is constantly tweaking them to match the finite data set you have," he said.

Bill has figured out the modeling concept as part of the nuclear power project he has funded Terrapower.

For example, Gates said that it takes a really long time to get a new nuclear power plant technology to market, longer even than the patent that might allow a successful invention to be profitable.

"You are not going to have a lot of people putting down money when the length of the project is longer than the length of the patent," Gates said, speaking at the Techonomy conference here.

It's not even certain that one could do it in their lifetime and he said a lot of people like to focus on projects that will happen in their lifetimes, "particularly if you aren't that religious," a category in which he said he falls.

Although some areas need help, Gates said creating new forms of energy will lead to new wealth. "A few somebodies will get very rich making those breakthroughs."

If we knew a particular solar or nuclear technology were a sure thing, he said, our power issues would be complete. But, he said, "boy are there a lot of problems that need to be solved."

There are some people thinking of data center modeling, but only a very few are looking at the complexity of the total system, including social and environmental in addition to the economics and technical issues.  With time and the growth of modeling in other areas, more people will discover what Bill is talking about.

HP Butterfly Flexible Data Center, Part 2 - 20 year NPV 37% lower than traditional

I just posted about HP's Butterfly Flexible Data Center.  Now that HP has official announced the solution, there is more on the HP Press Room.

Economical, Efficient, Environmental is a theme for HP's video presentation.

 

Here are numbers HP uses to demonstrate a 50% lower CAPex

And lower OPex.

And HP discusses yearly water usage. Yippee!!!

 

Typically, data centers use 1.90 liters of water
per kWh of total electricity. A one-megawatt data
center with a PUE of 1.50 running at full load for
one year is expected to consume 13 million kWh
and will consume 6.5 million U.S. gallons of water
annually. FlexDC uses no water in some climates and
dramatically reduces the consumption of water in
others. Actual amounts can vary depending on system
selection and local weather patterns.

HP has a white paper that is a must read for anyone designing a data center.

Introduction
If we briefly go back to the late 1960s and the advent
of transistor, efficiencies and cycles of innovation in
the world of electronics have increased according to
Moore’s law. However, data center facilities, which
are an offshoot of this explosion in technology, have
not kept pace with this legacy. With the magnitude of
capital required and costs involved with the physical
day-to-day operations of data centers, this existing
paradigm could impede the growth of data center
expansions, unless a new group of innovative solutions
is introduced in the market place.

With sourcing capital becoming more difficult to
secure, linked with potential reductions in revenue
streams, an environment focused on cost reduction
has been generated. The pressure to reduce capital
expenditure (CAPEX) is one of the most critical issues
faced by data center developers today. This is helping
to finally drive innovation for data centers.

The key contributors which can reduce CAPEX
and operational expenditure (OPEX) are typically
modularity, scalability, flexibility, industrialization,
cloud computing, containerization of mechanical and
electrical solutions, climate control, expanding criteria
for IT space, and supply chain management. All these
factors come into play when planning a cost-effective
approach to data center deployment. Every company
that develops and operates data centers is attempting
to embrace these features. However, businesses
requiring new facilities usually do not explore all the
strategies available. Generally, this is either due to
lack of exposure to their availability or a perceived
risk associated with changes to their existing
paradigm. Emerging trends such as fabric computing
further exacerbate the silo approach to strategy and
design, where “what we know” is the best direction.

The Four Cooling system alternatives are:

This adaptation of an
industrial cooling approach includes the following
cooling technologies: air-to-air heat exchangers with
direct expansion (Dx) refrigeration systems; indirect
evaporation air-to-air heat exchangers with Dx assist;
and direct evaporation and heat transfer wheel with
Dx assist.

Reducing fan power.  Fan power is a hidden inefficiency in the data center whether in the mechanical systems or IT equipment.  HP discusses how it reduces fan power.

To obtain the maximum use of the environment, supply
air temperature set points need to be set at the highest
temperature possible and still remain within the
warranty requirement range of the IT equipment. The
next critical component is to control the temperature
difference between the supply and return air streams
to a minimum range of 25° F. This reduces the amount
of air needed to cool the data center, thus reducing
fan energy. The configuration of the data center in
general must follow certain criteria in order to receive
greater benefits available through the use of this
concept, as follows:
• Server racks are configured in a hot aisle
containment (HAC) configuration.
• There is no raised floor air distribution.
• The air handlers are distributed across a common
header on the exterior of the building for even air
distribution.
• Supply air diffusers are located in the exterior wall,
connected to the distribution duct. These diffusers
line up with the cold aisle rows.
• The room becomes a flooded cold aisle.
• The hot aisle is ducted to a plenum, normally
created through the use of a drop ceiling. The hot
air shall be returned via the drop ceiling plenum
back to the air handlers.
• Server racks are thoroughly sealed to reduce the
recirculation of waste heat back into the inlets of
nearby servers.
• Server layout is such that the rows of racks do not
exceed 18 feet in length.
The control for the air handlers shall maintain
maximum temperature difference between the supply
and return air distribution streams. The supply air
temperature is controlled to a determined set point
while the air amount is altered to maintain the desired
temperature difference by controlling the recirculation
rate in the servers.

Electrical distribution techniques are listed as well.

Traditional data centers have electrical distribution
systems based on double conversion UPS with battery
systems and standby generators. There are several
UPS technologies offered within FlexDC, which
expands the traditional options:
• Rotary UPS—94% to 95% energy efficient
• Flywheel UPS—95% energy efficient
• Delta Conversion UPS—97% energy efficient
• Double Conversion UPS—94.5% to 97% energy
efficient
• Offline UPS—Low-voltage version for the 800 kW
blocks, about 98% energy efficient
FlexDC not only specifies more efficient transformers
as mandated by energy standards, it also uses best
practices for energy efficiency. FlexDC receives power
at medium voltage and transforms it directly to a
server voltage of 415 V/240 V. This reduces losses
through the power distribution unit (PDU) transformer
and requires less electrical distribution equipment,
thus, saving energy as well as saving on construction
costs. An additional benefit is a higher degree of
availability because of fewer components between the
utility and the server.

And HP takes a modeling approach.

HP has developed a state-of-the-art energy evaluation
program, which includes certified software programs
and is staffed with trained engineers to perform a
comprehensive review of the preliminary system
selections made by the customer. This program
provides valuable insight to the potential performance
of the systems and is a valuable tool in final system
selection process. The following illustrations are typical
outputs for the example site located in Charlotte,
North Carolina. This location was chosen due its very
Figure 4: Shows state-of-the-art data center annual electricity consumption
reliable utility infrastructure and its ability to attract
mission critical type businesses. The illustrations
compare a state-of-the-art designed data center using
current approaches and HP FlexDC for the given
location.

Comparing two different scenarios.

Scenario A: Base case state-of-the-art
brick-and-mortar data center.
A state-of-the-art legacy data center’s shell is typically
built with concrete reinforced walls. All of the cooling
and electrical systems are located in the same shell.
Traditional legacy data center cooling systems entail
the use of large central chiller plants and vast piping
networks and pumps to deliver cooling air handlers
located in the IT spaces. Electrical distribution systems
typically are dual-ended static UPS system with good
reliability but low efficiencies due to part-loading
conditions. PUE for a traditional data center with tier
ratings of III and above are between 1.5–1.9.

Scenario B: HP FlexDC
The reliability of the system configuration is equivalent
to an Uptime Institute Tier III, distributed redundant.
The total critical power available to the facility is
3.2 MW. The building is metal, using materials
standard within the metal buildings industry. The
electrical distribution system is a distributed redundant
scheme based on a flywheel UPS system located in
prefabricated self-contained housings. The standby
generators are located on the exterior of the facility
in prefabricated self-contained housing with belly tank
fuel storage.
The mechanical cooling systems are prefabricated
self-contained air handlers with air-to-air heat
exchangers using Dx refrigerant cooling to assist
during periods of the year when local environment is
not capable of providing the total cooling for the data
center IT space.
The IT white space is a non-raised floor environment.
The IT equipment racks are arranged in a hot aisle
containment configuration. The hot return air is
directed into the drop ceiling above and returned to
the air handlers.
The following life-cycle cost analysis matrix quantifies
the CAPEX and OPEX costs and the resultant PV
dollars for the base case and the alternative scenario.

Which feeds this summary.

And a NPV cost savings of 37%.

Besides HP sharing Flexible Data Center design approach, they have published a set of documents that anyone building their own data center can use.

Kfir thanks for taking a step to share more information in industry and show them a better path to green a data center, being economically, efficietly, and environmentally.

Near miss science accounts for Human Factors, when will Data Centers do the same?

WSJ reports on the science of studying near misses and human factors that contribute to system failures.

Near Misses Are a Hit in Disaster Science

• By CARL BIALIK

While there never has been an oil spill in the Gulf of Mexico quite as large as the current disaster, there have been other terrible mishaps and, as in every industry, near misses.

These close calls are what Scott Shappell, professor of industrial engineering at Clemson University, looks for when he works with airlines on quantifying their risk from human errors.

"All you hear about are crashes, but it's the near misses that are telling," Prof. Shappell says. "If you only knew how many near misses there are in aviation, you would never fly again."

Near misses can be studied by statisticians to estimate the probability of an event that hasn't occurred before. Estimating the probability of unlikely disasters has become standard practice for nuclear and space regulators. Such an exercise, experts say, could help companies involved in deep-sea drilling evaluate risks and possibly prevent catastrophes like the Gulf oil spill.

Human Factors science is a common practice in Nuclear Power Plants, Aviation and complex programs like the Space Shuttle.

The following can just as be easy be stated about the data center industry.

There also is some danger in producing risk assessments that are too precise. "Most laypeople want a single number," says Todd Paulos, chief technology officer for Alejo Engineering Inc. in Huntington Beach, Calif., but "we can't predict anything to that accuracy."

The wild card in all these estimates is human error. Nuclear-power companies for years have sought to calculate and reduce risk by evaluating how people respond to simulations of potentially dangerous situations.

One of these days we'll see a data center consulting company offer Human Factors analysis.  Here is the company that has a bunch of Human Factors people who specialize in the.

Wiegmann, Shappell and Associates (WSA) are an Internationally recognized team of experts in system safety and human performance. As the authors of the Human Factors Analysis and Classification System (HFACS) and the Human Factors Intervention matrix (HFIX), WSA provides a variety of services to help incorporate these tools in to your organization to improve safety, quality and efficiency.

We're confident you'll find that Wiegmann, Shappell and Associates is not your typical safety consulting company. Renowned scientists with real world experience in the field, WSA bridges the gap between theory and practice to effectively integrate human performance and system safety principles and tools into your organization.

World Cup Soccer, assessment of unrest and violence

Today is the start of World Cup Soccer and I was watching this screen, and it reminded of some work done by some really smart people.

Below is the setup for the South Africa first goal.

The movement that allows South Africa to get in position.

 

And the goal

It is easy to do a post-analysis on what caused the goal.  But, it is much harder to do a pre-analysis on the factors that affect winning or losing.

Now let me show you what the smart people I know are able to do at the World Cup Soccer to understand the potential dynamics that can cause unrest and violence.  Here is the PDF I will be referring to.

The following is a demo showing the use of Thetus's Savanna product.

In the upper left is the assumption, "Demographics and migration patterns may influence stability during the world cup.  Immigration is a primary issue"

Which then supports, "What are the potential dynamics that can cause unrest or violence during the World Cup?"

How many systems have you seen that accept questions as input?  Almost all other systems are focused on data you have already set up monitoring, looking only within your system.  Can you ask questions that gets you looking for information beyond your data feeds?

You can browse the PDF to see more of the events and relationships.

Thetus Savanna allows an analysis process to see relationships and how events occur.  With this understanding you can mitigate undesirable action and invest in areas that are win conditions.

People actually do this activity all the time, but almost no one has a tool like Thetus Publisher.  (Disclosure: I am working with Thetus to figure out how their technology can be applied to data center scenarios, because they are some of the smartest people I've found to figure out how to model the complexity in the data center.)

There are only a handful of people at this time who can understand this technology and apply it.  And, luckily after two years figuring out who the right people are and what are the right scenarios, this method is closer to being deployed.

Writing this blog entry was the easiest way to tell the less than 10 people i know who will understand this approach "here is a cool graphic you can use to illustrate the potential use to your co-workers and team."

And, it is time to start sharing this approach.  I'll continue to post on this modeling method as it explains how I am using my blog posts.  In general, I am blogging about public facts that fit in modeling data centers.

As a few of my business associates and clients know, there is logic to my posts, and they can read the relationships between the posts.  This does not apply to all posts, but they know how to parse what I write.  One benefit of using this blogging approach is I can meet with people, and we immediately can drop into details as they have been reading what I have been blogging.

As my wife just told me last night, I wish you could tell me how to use technology as well as you write things.  I told her well, I spend more time thinking about what to write, than I do thinking about what to say.  :-)

In the same way it is sometimes hard to understand the exact movements in a soccer match that support a goal, once you recognize the patterns that you want to repeat, you start to score more.

Enjoy the World Cup Soccer. 

Freedom to think of things others don't, accepting different belief structures - Human Factors and the Data Center

Last week I blogged about some big thinking I participated in Portland with a cloud computing director and ten others.  One of the things I do in big meetings is drop into an analyst mode watching the conversations, saying little, listening and watching the dynamics in the meeting.  Normally, I would be discussing big ideas, but with 12 people in the room and plenty of brain power going and an extremely smart guy presenting I could be quiet.   I frequently find I learn more and figure out things being quiet and watching the dynamics between the people.  Isn't it funny how your brain stops listening when you want to talk.

One of the entertaining moments is when a VC came into the meeting late and spent 2 minutes telling the group how important he is and how much influence he has.  I didn't say one word to him, even though he was the man with the money.  I had more important conversations, and for a person like this, it is many times difficult to explain my role, and that this meeting wouldn't be happening if  I hadn't been architecting the solution.

I was sitting next to my friend and we scribbled notes and whispered ideas during the presentation, taking the time to highlight important concepts. One of the concepts that was big is to model different belief structures to interpret data differently which allows you to put data in context of the user. 

A side story, I worked with some data center construction guys and I found their belief and value system was totally different than what I had assumed.  The construction guys thought I was not that smart the more I worked with them.  What I understood is their value and belief system was brittle when exposed to openness and transparency which is requirement for building a knowledge model for the data center.  Luckily I escaped that project.  In the process, I learned a valuable lesson why it is so hard to bridge thinking across data center design, construction and operation.  Most people are fixed in their belief and value system, they can't translate what others do into their beliefs, and vice versa.  Openness and transparency is not compatible with many existing approaches in data centers where keeping things secret is a standard practice.  Also, keeping secrets maximizes control and profits for the suppliers as the customers are mystified by the black magic skills to build a data center.

Note: I have met other data center construction guys who don't exhibit this behavior, so don't think I mean all data center construction is this way.  And, I have met data center designers who believe data center efficiency can be achieved with simpler designs that are easier to operate and maintain when the black magic is not part of the design.

After this lesson, I've spent more time analyzing people and companies for how well they fit in open approaches like we intend to use in the "Open Source Data Center Initiative."

While most people in the meeting were down in the details reviewing ideas presented, I was watching the people and their beliefs, trying to figure out if they accept other people's belief systems.  The more arrogant a person is the less they accept another person's view as being right in their context.

How many different belief systems do you accept as valid in the data center system?

Executive & CIO

Business Unit VP

Facility Operations

IT Operations

Application/Services Operations - Dev and Test

Enterprise Architect

Security

Networking

Database & Storage

Finance

Public Relations

Environmental Impact & Sustainability

Customers

Partners

Suppliers

Government, Finance, and Compliance Regulations

One view I haven't heard is Human factors.  I wrote the above yesterday, but knew it was not finished to post, this morning at 6a it clicked.  One view that touches almost all of  the above, but is not discussed is the holistic view from Human Factors.  I studied Human Factors in college and believed it was key to be a better Industrial Engineer. When I interviewed at IBM, one of the questions was "How do you know what to change?"  Being young and naive, I said you have to care about the people.  The IBM engineers probably thought I was a leftist tree-hugging radical thinker  as I was graduating from UC Berkeley.

What is Human Factors?

Human factors involves the study of all aspects of the way humans relate to the world around them, with the aim of improving operational performance, safety, through life costs and/or adoption through improvement in the experience of the end user.

An area where Human Factors shows up in most data centers is in facilities due to the maturity of the equipment used, regulations like OSHA, and safety requirements around large mechanical and power systems.  But, the application of Human Factors in data centers is relatively new.  In talking to Mike Manos, he described how Microsoft designed its data centers to make it easier to receive fully assembled racks and deploy the heavy racks to their location.

In software and hardware, User Interface design is a more popular term.

In the industrial design field of human-machine interaction, the user interface is (a place) where interaction between humans and machines occurs. The goal of interaction between a human and a machine at the user interface is effective operation and control of the machine, and feedback from the machine which aids the operator in making operational decisions. Examples of this broad concept of user interfaces include the interactive aspects of computer operating systems, hand tools, heavy machinery operator controls. and process controls. The design considerations applicable when creating user interfaces are related to or involve such disciplines as ergonomics and psychology.

Human Factor and User Interface is discussed in isolated areas within the data center, but I don't think I have heard any one discussing human factors and data centers in the same breath.

The #1 risk to data center operations are human related.  How much did it cost Microsoft and Hitachi Data Systems for the T-Mobile data loss disaster that was a human error.

While users will be relieved that their information looks likely to be recovered, the episode poses several questions over the competence of Danger’s staff; the technical ability of contractor Hitachi Data Systems; and the inherent stupidity of the Cloud concept.

While we are unlikely ever to be told the full story, it looks very much as if Hitachi’s attempts to upgrade Danger’s Storage Area Network failed big time and that the data was put at risk not by hardware failure, but by good old-fashioned human error.

This one event that had a multiple human errors did hundreds of millions of dollars of damage to Hitachi and Microsoft.  Can Hitachi sell a storage system?  Can Microsoft sell its Smartphones?

This problem was caused by people who didn't spend the time to think how the people are interacting with the data center systems.

This is one of my more rambling posts, there are some good ideas here, I need to think about them a bit more though.

Can Data Centers benefit from Supply Chain Management concepts?

Currently, I am studying data center site selection, and have been asking the question what is wrong with data centers having 1% of the cost being in the land when other commercial real estate will typically have land 20-25% of the cost.  One big thing most miss is land is not a cost, it is a non-depreciable asset. 

Capital assets that are inexhaustible or where the useful life does not diminish or expire over time, such as land and land improvements. Infrastructure assets reported using the modified approach to depreciation are also not depreciated.

Land is not an expense, it is an investment.  So, land should be looked evaluated on its ROI, not it's overall cost, including land improvements. 

Which then led me to think why is it data centers don't use more supply chain management concepts which would address issues like land cost in the overall solution and most likely save you much more than the cost of the land?

Supply Chain Management is defined as.

Supply chain management (SCM) is the management of a network of interconnected businesses involved in the ultimate provision of product and service packages required by end customers (Harland, 1996).^[1] Supply Chain Management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption (supply chain).

Another definition is provided by the APICS Dictionary when it defines SCM as the "design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measuring performance globally."

Can't you think of all the different groups and vendors involved in providing data center and IT services as a supply chain management problem?  Is the CIO in charge of the supply chain? Maybe.

Here is a piece of irony from a CIO.com article on supply chain management.  Supply chain management SW is a mess.

Supply chain management software is possibly the most fractured group of software applications on the planet. Each of the five major supply chain steps previously outlined is comprised of dozens of specific tasks, many of which have their own specific software. Some vendors have assembled many of these different chunks of software together under a single roof, but no one has a complete package that is right for every company. For example, most companies need to track demand, supply, manufacturing status, logistics (i.e. where things are in the supply chain), and distribution. They also need to share data with supply chain partners at an ever increasing rate. While products from large ERP vendors like SAP's Advanced Planner and Optimizer (APO) can perform many or all of these tasks, because each industry's supply chain has a unique set of challenges, many companies decide to go with targeted best of breed products instead, even if some integration is an inevitable consequence.

So, if a bunch of people who focus only on supply chain management can't get the software right, how can the data center industry get the right software to run data centers like a supply chain?

I think I have an answer on how to approach supply chain management for data centers.  The first step is to identify the problem, then test what approaches solve the problem best. The fragmentation and silos is the opportunity to address.  How do you pull all the pieces together?  My ideas are based on using social networking and memetics.

More to come.

Alternative to Google's hiring Renewable Energy Systems Modeling Engineer

I am spending more time researching the Low Carbon Data Center ideas and I ran across Google's job posting on Renewable Energy System Modeling Engineer.

The role: Renewable Energy System Modeling Engineer - Mountain View

RE<'C will require development of new utility-scale energy production systems. But design iteration times for large-scale physical systems are notoriously slow and expensive. You will use your expertise in computer simulation and modeling to accelerate the design iteration time for renewable energy systems. You will build software tools and models of optical, mechanical, electrical, and financial systems to allow the team to rapidly answer questions and explore the design-space of utility-scale energy systems. You will draw from your broad systems knowledge and your deep expertise in software-based simulation. You will choose the right modeling environment for each problem-from simple spreadsheets to time-based simulators to custom software models you create in high-level languages. The models you create will be important software projects unto themselves. You will follow Google's world-class software development methodologies as you create, test, and maintain these models. You will build rigorous testing frameworks to verify that your models produce correct results. You will collaborate with other engineers to frame the modeling problem and interpret the results.

It's great Google see the need for this person, but I was curious if anyone else has done Renewable Energy System Modeling.  Guess what there is, since 1993 in fact.  NREL has this page on Homer.

New Distribution Process for NREL's HOMER Model

Note! HOMER is now distributed and supported by HOMER Energy (www.homerenergy.com)

To meet the renewable energy industry’s system analysis and optimization needs , NREL started developing HOMER in 1993. Since then it has been downloaded free of charge by more than 30,000 individuals, corporations, NGOs, government agencies, and universities worldwide.

HOMER is a computer model that simplifies the task of evaluating design options for both off-grid and grid-connected power systems for remote, stand-alone, and distributed generation (DG) applications. HOMER's optimization and sensitivity analysis algorithms allow the user to evaluate the economic and technical feasibility of a large number of technology options and to account for uncertainty in technology costs, energy resource availability, and other variables. HOMER models both conventional and renewable energy technologies:

I signed up for the Homer Energy site which has 510 users, non apparently Google engineers.

I hope to make contact with the Homer Energy team as we are trying to have a session at DataCenterDynamics Seattle on a Low Carbon Data Center.

Maybe Google doesn't have to hire the Renewable Engineering System Modeling engineer after all.  :-)

Flaw in Data Center Site Selection, one number vs. range of performance approach

I was just talking to some folks about data center site selection and the method to create a long list of criteria, create weightings, multiple the numbers, add the scores, then select the highest score as criteria site selection is flawed.

The flaw? Thinking that the weightings are the right numbers and the criteria can be counted as independent factors.

First one, weightings help prioritize those factors that are more important to the business.  This creates a single number.  Problem is business changes, and not all businesses can be represented by a single number.

Right approach, data center sites should be characterized by a range of performance that support the range of business now and in the future.  The sites that should be scored highest are the ones that best suit the range of performance for the business, not the highest score.

The error of a single number view vs. the range can be illustrated by the "Flaw of Averages."

The Flaw of Averages

A common cause of bad planning is an error Dr. Savage calls the Flaw of Averages which may be stated as follows: plans based on average assumptions are wrong on average.

As a sobering example, consider the state of a drunk, wandering around on a busy highway. His average position is the centerline, so...

Second one.  The criteria listed are assumed to be independent factors, but most criteria have relationships to other things, and the interaction of criteria creates good and bad conditions that experienced people know, but the site selection so-called gurus think they can solve the problem with enough criteria and weightings.

For the amount of money spent on data centers over the lifecycles, data center models should be built.  The trouble is few companies know how to do this as it requires a holistic view,bridging site, data center building, IT hardware, and software. This is problem worth solving.

Air Force and IBM partner to prove Cloud Computing works for Defense and Intelligence services

One of the top concerns about Cloud Computing is security of the data in the cloud.  IBM has a press announcement on the partnership here.

U.S. Air Force Selects IBM to Design and Demonstrate Mission-Oriented Cloud Architecture for Cyber Security

Cloud model will introduce advanced cyber security and analytics technologies capable of protecting sensitive national data

ARMONK, N.Y. - 04 Feb 2010: The U.S. Air Force has awarded IBM (NYSE:IBM) a contract to design and demonstrate a secure cloud computing infrastructure capable of supporting defense and intelligence networks. The ten-month project will introduce advanced cyber security and analytics technologies developed by IBM Research into the cloud architecture.

There are press articles too.

CNet News

Air Force taps IBM for secure cloud

by Lance Whitney

IBM has a tall order from the U.S. Air Force--create a cloud network that can protect national defense and military data.

Big Blue announced Thursday a contract from the Air Force to design and demonstrate a cloud computing environment for the USAF's network of nine command centers, 100 military bases, and 700,000 personnel around the world.

The challenge for IBM will be to develop a cloud that can not only support such a massive network, but also meet the strict security standards of the Air Force and the U.S. government. The project will call on the company to use advanced cybersecurity technologies that have been developed at IBM Research.

and Government Computer News.

What I find interesting is how few authors reference the IBM press release.  The goal of the project is a technical demonstration.

"Our goal is to demonstrate how cloud computing can be a tool to enable our Air Force to manage, monitor and secure the information flowing through our network," said Lieutenant General William Lord, Chief Information Officer and Chief, Warfighting Integration, for the U.S. Air Force. "We examined the expertise of IBM's commercial performance in cloud computing and asked them to develop an architecture that could lead to improved performance within the Air Force environment to improve all operational, analytical and security capabilities."

Which is cut and pasted into the CNet news article as well.

On the other hand, there are some good insights by Larry Dignan on his ZDnet blog.

What’s in it for IBM? Cloud computing has a lot of interest, but security remains a worry for many IT buyers. If Big Blue can demonstrate cloud-based cyber security technologies that’s good enough for the military it would allay a lot of those worries.

The advanced cyber security and analytics technologies that will be used in the Air Force project were developed by IBM Research (statement).

According to IBM the project will show a cloud computing architecture that can support large networks and meet the government’s security guidelines. The Air Force network almost 100 bases and 700,000 active military personnel.

and Larry continues on the key concepts of what will be shown.  Models!!! yea!

The model will include autonomic computing;

Dashboards will monitor the health of the network second-by-second;

If Air Force personnel doesn’t shift to a “prevention environment” in a cyber attack the cloud will have automated services to lock the network down.

Is Elastra one of Amazon’s Cloud Computing infrastructure tools? An awesome PDF to understand a better approach to infrastructure management

I plan on having a meeting with Elastra next week when I am in the bay area.  I wrote about their tools last week.

Elastra’s Cloud Computing Application Infrastructure = Green IT with a Model approach

Elastra connects the power use in the data center to the application architects and deployment decision makers.

Plan Composer function lets customers set their own policies based on application needs and specific power metrics (such as wattage, PUE, number of cores, etc.). Therefore, if an application requires 4GB of RAM and two cores for optimal performance, and if the customer is concerned with straight wattage, Elastra’s product will automatically route it to the lowest-power 4GB, dual-core virtual machine available.

Gigaom has a post on Elastra’s Cloud Computing infrastructure addressing greener services.

Elastra Makes Its Cloud Even Greener

By Derrick Harris Jan. 12, 2010, 2:51pm 1 Comment

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Checking out the investors, look what I found.

Amazon.com, Inc.

(NASDAQ:AMZN), a Fortune 500 company based in Seattle, opened on the World Wide Web in July 1995 and today offers Earth's Biggest Selection. Amazon.com, Inc. seeks to be Earth's most customer-centric company, where customers can find and discover anything they might want to buy online, and endeavors to offer its customers the lowest possible prices. Amazon.com and other sellers offer millions of unique new, refurbished and used items in categories such as books, movies, music & games, digital downloads, electronics & computers, home & garden, toys, kids & baby, grocery, apparel, shoes & jewelry, health & beauty, sports & outdoors, and tools, auto & industrial.

Amazon Web Services provides Amazon's developer customers with access to in-the-cloud infrastructure services based on Amazon's own back-end technology platform, which developers can use to enable virtually any type of business. Examples of the services offered by Amazon Web Services are Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3), Amazon SimpleDB, Amazon Simple Queue Service (Amazon SQS), Amazon Flexible Payments Service (Amazon FPS), and Amazon Mechanical Turk. www.amazon.com

Then digging more I found this architecture PDF by Stuart Charlton.

I like this picture from his personal site.

better han the corporate one from Elastra.

Back to his PDF.  Much of the information in the pdf is on the Elastra technology site, but I found it easier to read the PDF to understand Stu’s thinking.

The introduction targets the use of Elastra for the architect, stating the problems.

Reference Architecture Introduction

In today’s age of on-demand access to applications, compute, storage, and networks, modern IT applications and service management has many complications:

• Applications can be deployed across organizationally & geographically distributed data centers. The technology in these data centers, from virtualization platforms, to host, storage, and network infrastructure, is typically heterogeneous, and not necessarily managed with uniform policies and interfaces.
• The performance, scalability, and availability characteristics of an application are due to a complex combination of design and operational decisions. The greatest impacts on these factors are due to decisions in the architecture and development of the application, before configuring the data center infrastructure.
• Application and infrastructure management is complex and inter-disciplinary. It’s unlikely a system can be diagnosed and maintained by one person to keeping the system design & configuration in their head. Application design, administration, and management typically is a collaborative activity across specialists; there is no “one-size fits all” design tool, management tool or application platform.

The design goals are right on.

Three design goals for an end-to-end cloud design approach include:

Separated Applications from Infrastructure, through modeling the application in terms of its architecture and infrastructure requirements, without tying the application to a specific set of underlying infrastructure

Enabling Computer-Assisted Modeling and Control Automation, provided by a set of control agents and user-guided by graphical design tools. This could help IT architects and operators determine design constraints on the application, match the design to the underlying infrastructure, and enable goal-driven automation to deploy, scale, or recover their IT systems on demand.

Explicit Collaboration To Enact Changes, through models that codify, relate and analyze the constraints and preferences that are appropriate to stakeholders across enterprise IT: from architects and developers, through operators, administrators, and managers.

The document has many great ideas including the use of models.

Declarative models are useful ways to drive complexity out of IT application design and configuration, in favor of more concise statements of intent. Given a declaration of preferences or constraints, an IT management system can compose multiple models together much more effectively than if the models were predominantly procedural, and also formally verify for conflicts or mistakes. On the other hand, not everything can be declarative; at some point, procedures are usually required to specify the “last mile” of provision, installation, or configuration.

Here is a diagram showing VMware Virtual Center (Private Cloud Inventory)  and Amazon EC2/EBS (Public Cloud Inventory).