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EDIToR’S LETTER

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DATA

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THE NExT BIg THINg

The Analog Revolution

Water Concerns

Are RisingWill drought dry up

the digital economy?

modern infrastructure • september 2015 2

My bACk stIll hurts from shoveling all the snow that landed in Boston over the winter of 2015, so I look out to the coming fall with mixed emotions. But honestly, there’s a lot to look forward to.

For one thing, California could finally get some rain. According to the National Oceanic and Atmospheric Ad-ministration, this year’s El Niño weather pattern is gearing up to be one of the strongest on record, and is likely to bring a lot of warm, wet weather to the region. But data center operators shouldn’t let that fool them into thinking they shouldn’t worry about water conservation. In a guest article from the Uptime Institute, we learn that data cen-ters are major water hogs, right up there with farms and golf courses. Luckily, there are new cooling technologies that use a fraction of the water consumed by traditional chillers, and anyone considering a new data center build should give them some serious thought.

In the short term, we’re gearing up for the fourth an-nual AWS re:Invent conference in a couple of weeks. In short order, the show has gone from a niche event to the

major IT event of the season, with tens of thousands of attendees, hundreds of sessions and even more sponsors. For those of you going to the show—or who just wish you were—we’ve put together a preview highlighting product categories and sessions of particular relevance to infra-structure and operations teams.

Looking out, this may be the year that hyped technolo-gies start to hit your data center—if they haven’t already. Production use is always when the fun really starts. In ‘The Trouble with Big Data,’ for instance, I explore some of the very real, very vexing issues plaguing nascent big data an-alytics initiatives. And as I write in ‘Brace for Containers,’ while developers may already be all in with Docker, its ef-fects are just beginning to be felt among operations teams.

If nothing else, the coming year probably won’t be boring! n

Alex bARRett is editor in chief of Modern Infrastructure. Email her at [email protected].

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EDIToR’S LETTER

Never a Dull Moment

mailto:[email protected]

Water Concerns

Are RisingWill drought dry up

the digital economy? By KEITH KLESNER, RyAN oRR

AND MATT STANSBERRy, UPTIME INSTITUTE


IN ReCeNt DeCADes, the American West has experienced a prolonged and systemic drought. Currently, over 70% of California is in “extreme” drought, with nearly half of the state in “exceptional” drought, according to the U.S. Drought Monitor.

Population growth and climate change will create ad-ditional global water demand. So the problem of water scarcity is not going away.

In the current media cycle around the severe California drought, the majority of focus has been on residential and agricultural users. Industrial users such as data centers have largely been ignored, until now.

A recent Wall Street Journal article called out the data center industry, claiming that a midsize data center consumes 130 million gallons of water annually, or the equivalent of 100 acres of almond trees, three hospital buildings, or two 18-hole golf courses.

The Journal’s concept of a “mid-sized” data center (15MW) is ludicrously out of scale. According to the Uptime Institute’s survey data, an average data center deployment is about 1MW, and would consume approx-imately 7-8 million gallons of water annually. That’s still six acres of almond trees, five holes of golf or a little less than a third of a hospital.

DATA CENTER fACILITIES

hoMeAEyA/ISToCK

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Regardless of the accuracy of the Journal’s claims, the spotlight is on the data center industry’s water usage: Let’s examine how data centers consume water, the design choices that can limit water use, and the IT industry’s awareness of and appetite to address this issue.

hoW Do DAtA CeNteRs Use WAteR?

The primary way data centers use water is for heat rejec-tion (i.e., cooling IT equipment).

The traditional method of cooling a data center utilizes a water-cooled chilled water system. In these systems, cool water is distributed to the computer room cooling units. A fan blows across the chilled water coil, providing cool, conditioned air to the IT equipment. That water then flows back to the chiller and is re-cooled.

Water-cooled chiller systems rely on a cooling tower to reject heat from this system. A cooling tower is a large box-like unit that cools the warm water (or condenser water) from the chiller by pulling in ambient air from the sides and blowing hot, wet air out of the top of the unit by fan. The cooled condenser water then returns back to the chiller to again accept heat to be rejected.

These cooling towers are the main culprit for water consumption in a traditional data center design.

Let’s assume a 1MW data center pumps 1,000 gallons of condenser water per minute through a cooling tower. The cooling tower will lose between 1-2% of that water to evaporation and drift—water that is blown away in a fine mist by the fan or wind.

That comes out to about 6.7 million gallons of water consumed annually.

An additional 1.3 million gallons of water per year are lost in blowdown, or the replacement cycle. As the con-denser water is repeatedly evaporated and exposed to the atmosphere, it picks up minerals, dust and other contam-inants. That water must be treated, and/or dumped out at regular intervals.

In total, a 1MW data center using traditional cooling methods uses about 8 million gallons of water per year.

ChIlleR-less AlteRNAtIves

Today, many data centers adopt new cooling methods that are more energy efficient and use less water than traditional chillers and cooling towers combinations. These cooling methodologies are able to reduce annual water consumption by integrating evaporative cooling technologies and an economizer that utilizes outdoor air. In Uptime Institute’s experience certifying data centers

n Water is the lesser-known evil of data center consumption, because we always talk about power.

n The primary way data centers use water is for heat rejection (i.e., cooling IT equipment).

n Many data centers adopt cooling methods that are more energy efficient and use less water.

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around the globe, about 1/3 of new builds use some form of cooling system that does not utilize traditional chilled water and cooling tower combinations.

There are some data centers that use direct air cooling. Just open the windows and let the atmosphere wash over all that sensitive IT equipment. Christian Belady, Micro-soft general manager for data center services, proved it could be done, running servers for long periods in a tent. This unusual approach is limited by climate, and more importantly an organization’s willingness to accept risk of IT equipment failure due to fluctuating temperatures and airborne particulate contamination. The majority of organizations that use this method do so in combination with other cooling methods.

With direct evaporative cooling, outside air is blown across a water-saturated medium or via misting and cooled by evaporation. This cooled air is circulated by a blower to cool the servers. This approach, while more common than direct outside air cooling, still imposes risk to the IT equipment due to outside contaminants from external events like forest fires, dust storms, agricultural activity or construction, which can impair server reliability. These contaminants can be filtered, but many organizations will not tolerate a contamination risk.

Some data centers use what’s called indirect evaporative cooling. This process uses two air streams: one closed-loop air supply for IT equipment, and an outside air stream that cools the primary air supply. This outside (scavenger) air stream is cooled by direct evaporative cooling. The cooled secondary air stream goes through a heat exchanger, where it cools the primary air stream. The cooled primary

Where Does Data Center Water Come From?n Municipal: The majority of data centers rely on

water from a municipal source. Throughout the

hundreds of data center certifications Uptime

Institute has conducted, the vast majority use

municipal water, which typically comes from

reservoirs.

n Groundwater: groundwater is precipitation

that seeps down through the soil and is stored

belowground. Many data center operators drill

wells on their site to access this water. Worldwide,

groundwater tables are falling. The United States

geological Survey has published a resource to

track groundwater depletion.

n Rainwater: Rainfall provides an unreliable, vari-

able water source for data center usage. Some

data centers collect rainwater and use it as a sec-

ondary or supplemental water supply.

n body of water: A handful of data centers around

the world access water directly from lakes, riv-

ers or the ocean. In these cases, a data center

operator pumps the source water through a heat

exchanger. A data center may also use a body of

water for an emergency water source for cooling

towers or evaporative cooling systems. n

http://blogs.msdn.com/b/the_power_of_software/archive/2008/09/19/intense-computing-or-in-tents-computing.aspx

https://journal.uptimeinstitute.com/solving-air-contaminant-problems-data-centers/


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air stream is circulated by a fan to the servers. Also, there are systems that require no water—“dry

coolers” that use pumped refrigerant instead of water evaporation to cool the air supply. There are even air-cooled chilled water systems, which do not utilize evapo-rative cooling towers to reject heat.

Why Not All DAtA CeNteRs?

Why wouldn’t every data center use these new cooling designs, if they can provide significant energy and water usage reductions?

For starters these cooling systems mandate a 50-100% cost premium over traditional cooling. For an in-depth financial analysis, read Compass Datacenters’ study on the potential negative return on investment (ROI) for an adiabatic (i.e., evaporative) cooling system. Fundamen-tally, we currently operate in an era of cheap power and water. Someday the price for our resource consumption will come due, but until that time, the economics are such that the ROI on these more expensive systems can take years to achieve, if ever.

These systems also tend take up significant amount of space. For many data centers, water-cooled chiller plants make more sense because an owner can pack in capacity in a relatively small footprint without modifying building exteriors.

There are also implications for data center owners who want to achieve Uptime Institute’s own Tier Certification. Achieving Tier III Constructed Facility Certification requires the isolation of each and every component of

the cooling system without impact to design day cooling temperature. This means an owner needs to be able to tolerate the shutdown of cooling units, control systems, makeup water tanks and distribution, and heat exchang-ers. Tier IV Fault Tolerance requires the system to sustain any single but consequential event without impact to the critical environment. While many data centers using the new cooling designs have been Tier-Certified, it does add a level of complexity to the process.

Organizations also need to factor temperature consid-erations into their decision. If you’re not prepared to run your server inlet air temperature at 72 degrees Fahren-heit, there is not much payback on the extra investment.

An average data center deployment is about 1MW, and consumes approximately 7-8 million gallons of water annually—the equivalent of five holes of golf.

IMAgINEgoLf/ISToCK

http://www.compassdatacenters.com/trading-data-center-tco-for-pue/

https://uptimeinstitute.com/TierCertification/


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Also, companies need to start with good computer room management, including optimized airflow for efficient cooling, and potentially containment, which can drive up costs. Additionally, in hot and humid climates, some of these cooling systems just won’t work.

Also, as with any newer technology, alternative cooling systems present operations challenges. Organizations will likely need to implement new training to operate and

maintain unfamiliar equipment configurations. Compa-nies will need to conduct particularly thorough due dili-gence on new, proprietary vendors entering the mission critical data center space for the first time. Caveat emptor.

And lastly, there is significant apathy about water conservation across the data center industry as a whole. Uptime Institute survey data shows that less than one third of data center operators are tracking water usage or using the Green Grid’s Water Usage Effectiveness (WUE) Metric. And according to Uptime Institute’s 2015 “Data Center Industry Survey,” in a question asking operators about the most important data center metrics, water usage ranked near the bottom of priorities. The only thing data center managers said they care about less than water is carbon dioxide emissions.

But the volumes of water or power used by data centers

make them an easy target for public finger wagging. There are a lot of good reasons to choose traditional chilled water systems, especially when dealing with existing buildings.

leading by examplesoMe pRoMINeNt exAMples of data centers using

alternative data center cooling methods include:

n vantage Data Centers’ site in Quincy, WA uses

Munters Indirect Evaporative Cooling systems.

n Rackspace’s data center in London and Digi-

tal Realty’s profile park site in Dublin use roof-

mounted indirect outside air technology coupled

with evaporative cooling from ExCool.

n In a first phase, Facebook’s Prineville, oR data

center used direct evaporative cooling and hu-

midification, with small nozzles attached to wa-

ter pipes that sprayed a fine mist across the air

pathway, cooling the air and adding humidity. In a

second phase, it used a dampened media.

n yahoo’s Chicken Coop data center design in

upstate New york uses direct outside air cooling

when weather conditions allow.

n Metronode, a telecommunications company in

Australia, uses direct air cooling (as well as direct

evaporative and Dx for backup). n

CoMpANIes NeeD to stARt WIth GooD CoMpUteR RooM MANAGeMeNt.

http://www.thegreengrid.org/~/media/WhitePapers/WUE

http://www.thegreengrid.org/~/media/WhitePapers/WUE

https://www.munters.com/en/industries/data-center-and-telecoms/

http://excool.com/news.php?template=2&id=80

http://www.datacenterknowledge.com/archives/2012/08/14/data-center-water-use-moves-to-center-stage/

https://gigaom.com/2010/09/19/now-online-yahoos-chicken-coop-inspired-green-data-center/

http://www.metronode.com.au/metronode-launches-nsw-government-next-generation-data-centre/


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For new data center builds, however, owners should evaluate alternative cooling designs against overall busi-ness requirements, which might include sustainability factors.

Uptime Institute has invested decades of research toward reducing data center resource consumption, and, unfortunately, there is still work left to do. The water topic, while currently a serious issue in California, needs to be assessed within a larger context of a holistic

approach to “Efficient IT.” With this framework, data cen-ter operators can learn how to better justify and explain business requirements, and demonstrate that they can be responsible stewards of our environment and corporate resources. n

keIth klesNeR, RyAN oRR and MAtt stANsbeRRy work for Uptime Institute, The Global Data Center Authority. Stansberry is the director of content and publication, Klesner is the vice president of strategic accounts and Orr is a senior consultant.

http://journal.uptimeinstitute.com/holistic-approach-reducing-cost-resource-consumption/

http://journal.uptimeinstitute.com/holistic-approach-reducing-cost-resource-consumption/


sINCe Its stARt in 2012, AWS re:Invent, Amazon Web Ser-vices’ annual conference, has offered attendees a chance to dive into all things AWS public cloud. The sold-out event features a slew of workshops, hackathons, boot camps and expert Q&A sessions—all geared toward help-ing existing customers and the AWS curious gain a better understanding of working in, and with, Amazon’s public cloud services.

As AWS products and services continue to grow and change, so does the solar system of vendors and partners that revolve around it. During the inaugural re:Invent conference, the expo floor housed row upon row of ven-dors—spanning categories such as security, monitoring and management, data analytics, storage, application development, partners and integrators, and many more. Those numbers have increased each year, which can make it difficult for attendees to keep track of who does what or to map out a course of action while on the show floor.

In this AWS re:Invent 2015 show guide, we spoke with industry experts, analysts and AWS users to offer you insights into the AWS market in four key areas: security, monitoring and management, data analytics, and applica-tion development. We look at how AWS’ presence in those areas has grown and how it could evolve in the coming

AWS’ biggest event is just around the corner.

Here’s what you need to know.

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oCToBER 6-9 , 2015 AT THE VENETIAN IN LAS VEgAS, NV

hoMe


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years. We also delve into the need for third-party tools that fill certain gaps or help make public cloud services more seamless and robust. And we also outline some AWS re:-Invent 2015 sessions that you may want to check out while you’re in Vegas (see page 12). —Michelle Boisvert

CloUD MANAGeMeNt

As cloud adoption continues to grow in the enterprise, so does demand for management systems that pro-vide tighter control and visibility into cloud environments. In fact, global

spending on cloud management software and software as a service soared 29.9% in 2014, totaling $2.3 billion, according to industry analyst IDC.

And while all cloud users should prioritize cloud man-agement and monitoring, AWS customers, given the sheer size of the company’s portfolio, should take an especially proactive approach.

“The underlying [management] principles are always the same across clouds,” said Dan Sullivan, independent consultant and TechTarget contributor. “But it may be more difficult in AWS, given that they have so many ser-vices and different uses.”

For full reign over their environments, AWS shops should monitor and manage not only AWS configurations and performance, but also costs, security and software development lifecycles, Sullivan said. And there’s a range of AWS and third-party tools to meet each of these needs.

For AWS performance management, for example,

there’s CloudWatch, a native AWS tool that monitors met-rics for major AWS utilities, including Elastic Compute Cloud (EC2) and Redshift. The public cloud giant also offers a cost calculator that helps users project cloud costs. But third-party tools, such as those from Cloudability and Cloudyn, are often needed for more detailed monitoring and reporting.

While the AWS Management Console, for instance, is a helpful tool for managing AWS resources, third-party AWS management tools offer more granularity, said Alex Witherspoon, vice president of platform engineering at FlightStats. The company, based in Portland, Ore., pro-vides global flight data.

What’s more, for organizations that use AWS alongside other cloud deployments, third-party tools help track workloads as they move across different environments—a capability you wouldn’t necessarily get from AWS tools alone, Witherspoon said.

“We can actually see the point-in-time cost of an exact transaction in a system—what it costs to service it and what we charged for that transaction,” Witherspoon said, noting that his company uses a third-party monitoring tool from New Relic. “That gives us the ability to make a lot of judgments about whether we should use Amazon differently, or not use Amazon at all, or vice versa.”

To streamline AWS management, organizations should first automate their cloud infrastructure, Sullivan said. Third-party cloud management tools, such as RightScale and Puppet, can help automate AWS deployments.

Organizations should look to gain full visibility and control over their AWS environment, even if it takes time.


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For sandbox and testing environments, management and monitoring is less critical. But for AWS production environments—especially those that users want to rep- licate—a robust management strategy and toolset is a must. —Kristin Knapp

seCURIty

AWS introduced its public cloud 10 years ago. And since then, the number of associated products and services has exploded at breakneck pace. Still, despite all its developments and evo-

lution, many enterprises continue to list security among their major concerns with public clouds like that from AWS.

AWS maintains a “shared responsibility” stance to public cloud security. The cloud provider secures the in-frastructure, while enterprise IT teams are responsible for securing workloads, data and applications that run on the infrastructure. This is no easy task.

“Shared security is really incumbent upon the tenets in infrastructure as a service (IaaS) offerings like AWS—that they continue to carry a fair amount of the responsibility,” said Jim Reavis, co-founder and CEO of the Cloud Secu-rity Alliance. “That also gives [public cloud providers] the flexibility to have a fairly vanilla offering that you can do a lot with.”

Enterprises need an independent viewpoint and layered defense in their cloud strategies and architectures. Relying on a single cloud-specific vulnerability assessment from

your IaaS provider isn’t a sound decision, as that provider may not be objective. Thus, third-party security tools are the way to go.

Entire segments of the market, such as security as a service tools and cloud access security brokers, have de-veloped to help enterprises lock down AWS workloads. Within the AWS Partner Network alone, there are ap-proximately 176 technology partners aimed specifically at security and compliance within AWS public cloud. While certain companies are comfortable building in-house se-curity tools for workloads running on AWS, most turn to third-party tools from vendors such as SumoLogic, Alert-Logic, Pertino, CloudPassage and Evident.io.

Eliminating the appliance and using service delivery for security is attractive to enterprises. While the security ap-pliance approach forced companies to make architectural decisions and often route traffic inefficiently, security as a service tools are “faster, cheaper and more agile,” Reavis said.

The best way to handle security in the public cloud is to “let large IaaS vendors handle the virtual private cloud and virtual machine management, then layer third-party tools on top of that,” Reavis said. —Michelle Boisvert

ApplICAtIoN DevelopMeNt

Application development for the pub-lic cloud is more complicated than ever. Gone are the days when a devel-oper just uploaded a workload to one

(Continued on page 13)


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ARC302

Running lean Architectures: how to optimize for Cost efficiencyThis session reviews cost planning, monitoring and optimization to help save money. Attendees can learn strategies for spinning up instances, Auto Scaling, using multiple availabil-ity zones and using CloudWatch.

ARC305

self-service Cloud services: how J&J is Managing AWs at scale for enterprise WorkloadsThis advanced-level course explains how Johnson & Johnson used an Amazon VPC to simplify its architecture.

ISM305

AWs Cloud Adoption Framework: Create your Cloud strategy and Accelerate time to ResultsThis session features a prescriptive roadmap for enterprises building a cloud and using the AWS Cloud Adoption framework.

SEC302

IAM best practices to live byBreaking down the difference between Identity and Access Management users and roles, this event reveals best practices for managing users and their security credentials.

SEC303

Architecting for end-to-end security in the enterpriseIn this technical walkthrough, IT teams from fortune 500 companies explain their security architecture choices, including security strategy, configurations, end-to-end architec-ture and service composition.

SEC310 -

splitting the Check on Compliance and security: keeping Developers and Auditors happy in the CloudLearn how to find a middle ground for developers and auditors dealing with security, including discussion on shared responsibility.

DEV201

AWs sDk For Go: Gophers Get Going with AWsIntroducing AWS SDK for go’s architecture, configuration and other features, this session teaches developers how to build productive cloud applications that use microservices and other AWS products.

DVo302

Devops at Amazon: A look at our tools and processesAmazon’s change to a service- oriented architecture a decade ago will be explained, breaking down the processes and tools used. AWS CodeCommit, CodePipeline and CodeDeploy will also be discussed.

DVo305

turbocharge your Continuous Deployment pipeline with ContainersThis session explains how containers allow developers to test code on the same environment in which it will run. Docker Compose, Jenkins, AWS CodePipeline and AWS Elastic Container Service and how they ease continuous deployment.

BDT205

your First big Data Application on AWsLearn how to build your first big data application by watching one built in real time, including when to use Elastic Map Reduce, Redshift, Kinesis, DynamoDB and Simple Storage Service.

BDT310

big Data Architectural patterns and best practices on AWsWith several products to break down big data and an increasing demand for real-time big data processing, ex-perts simplify the process as a data bus with various stages.

CMP403

AWs lambda: simplifying big Data WorkloadsMake sense of all that data by using AWS Lambda. This session explains how to use the service to provide real-time streaming output.

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Must Attend re:Invent sessions


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of a few basic cloud instances. Today, public cloud provid-ers like Amazon provide a bewildering array of features and services—many with overlapping capabilities—and the menu of public cloud services is still growing.

A proliferation of services has put cloud application developers into a bind, making it increasingly difficult to design and optimize the architecture for public cloud workloads.

“It seems like every few months there’s a new product offering that’s almost identical to one they’re already offering but different just enough to make you re- think your entire architecture,” said Chris Moyer, vice president of technology at ACI Information Group The burgeoning list of AWS instance types and ser-vices also complicates service optimization and pric-ing, making it difficult to achieve optimum workload performance, predict costs and understand billing changes.

The speed, flexibility and scalability available in the public cloud have had a profound impact on applications development. A decade ago, it might have taken up to two years before a new application was even ready for testing. Developers can now take short-term, incremental ap-proaches and experiment with designs in ways that would have been impractical with traditional development methods. “For every success there’s going to be a thousand failures, so it’s much better if there’s little to no up-front costs,” Moyer said.

In the past year, public cloud providers have begun supporting platform-as-a-service offerings like Docker

or AWS Lambda event-driven computing services. These services have reduced the need for low-level operating system knowledge, and developers can simply focus on building and deploying code. The system administrator role is changing too; moving away from basic OS know-how and emphasizing cloud OS features like load balanc-ing, auto-scaling, clusters, virtual private cloud (VPC) configurations and so on.

But the growing array of public cloud features and changes in application development have changed the demand for tools. Application developers are turning to tools that track costs, suggest strategies to optimize work-load performance, and streamline integration with AWS.Third-party tools such as CloudCheckr add another layer of monitoring beyond native AWS tools like Trusted Advi-sor, helping enterprises with cost management, security, resource reporting, monitoring and policy management. Then there are tools such as Codeship that integrate with AWS CodeDeploy to assist with application testing, deployment and workflow organization for public cloud applications. Utilities such as Jenkins give developers an open source platform to automate code tests and builds.

While third-party tools are not essential, they can help make applications more cloud-agnostic. “Codeship works on bare metal or AWS, so you could have your same tool even if you decided to drop or migrate away from AWS,” Moyer said, adding that third-party code repository host-ing like GitHub or Bitbucket can also be helpful. Although AWS now has its own repository, it can be helpful for developers to maintain their own in a redundant external location. —Stephen Bigelow

(Continued from page 11)


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bIG DAtA

Big data analytics have been part of the industry conversation for years, but over the last 6-12 months, big data analytics platforms in the cloud have truly taken off.

Nearly half of 375 IT and business professionals rep-resenting large, midmarket and enterprise-class orga-nizations in North America, surveyed by the Enterprise Strategy Group, are interested in new big data projects this year.

Among those companies, 30% said their primary de-ployment model would be cloud-oriented, according to “Enterprise Data Analytics Trends,” issued by Enterprise Strategy Group (ESG) in 2014.

A later survey of 601 IT professionals representing midmarket and enterprise-class organizations in North America and Western Europe, ESG’s 2015 “IT Spending Intentions Survey,” found that younger companies and employees are in favor of big data and analytics projects in the cloud at a rate of almost 2:1 versus companies that have been around for 50 years or more, or employees 45 years or older

“When you ask that younger set, ‘who do you see as the most strategic IT vendor in any area today?’ Amazon comes up as number one,” said Nik Rouda, senior analyst with ESG.

Amazon offers many services in big data and analytics, which include Elastic MapReduce Hadoop as a service, the

DynamoDB NoSQL database, the Redshift data warehouse and Amazon Machine Learning.

AWS also benefits from the fact that Hadoop manage-ment products such as those from Cloudera and Horton-works can be run independently by users on the EC2. Competitors include the Hadoop as a service player Alti-Scale, which offers managed services, Microsoft’s Azure Machine Learning service, and IBM’s Watson machine learning APIs, available on its BlueMix PaaS. Google, inventors of MapReduce, also offers Google Cloud Data- flow.

Within Amazon, partners say Elastic MapReduce and Redshift have come on very strong among enterprise cli-ents in the last year or so.

“With the big data services and capabilities in the cloud, we’ve seen customers streamline their data pro-cessing,” said Randall Barnes, principal data architect for 2nd Watch Inc., an Amazon Premier Partner in Liberty Lake, Wash.

These days, enterprise customers have become more apt to provision big data analytics environments on-de-mand rather than having static clusters waiting to crunch data, Barnes said.

“We’ve definitely seen a shift in the last six months to that approach to big data, and that brings the costs down,” he said. “Enterprises are finally looking at having that elastic scalability on their back office or data applications the same as they would on a simple Web tier.”

—Beth Pariseau

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#hashtag Twitter on #AWS

tim Unwin

@tpunwin

Happily deploying a #Rails app to #AWS from the car in the middle of nowhere... and to think how little time ago mobile browsing was novel.

iFollowoffice

@ifollowoffice

A world without #AWS is like bread without butter, or Tom Selleck without the moustache. A scary place indeed.

Rhodie hinkens

@zbozi

#aws support is getting really crappy these days

Rémy sanchez

@xowap

The fact that even #AWS’s login form is still basically #Amazon’s first login form amazes me.

Avron olshewsky

@avrono

Has anyone out there had a successful experience with #AWS #glacier, is it the most convoluted pricing model you have ever seen?

Dave Jennings

@davejjennings

“Amazon EC2 Instance scheduled for retirement”—oh I do love these emails. There goes my evening! #AWS #EC2

ben Newton

@BenENewton

More #AWS Lambda savings today. Eliminating the need for servers left and right.

tony

@TonyDMags

Love it when #AWS restarts an instance BEfoRE sending me a notice that they need to restart an instance.

https://twitter.com/tpunwin

https://twitter.com/hashtag/Rails?src=hash

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Ask 10 DIFFeReNt companies about what infrastructure they need to run their big data workloads and you’ll get 10 very different answers. There are few rules, and even fewer best practices.

Big data analytics can be a drain on infrastructure—both in resources and expertise. As the name implies, the data sets that big data analytics tools work against can be large and require significant amounts of compute, stor-age and network resources to meet performance goals. The toolsets, meanwhile, are not well understood by mainstream IT and were often developed by hyperscale companies, without the same level of concern for secu-rity and high availability that enterprises demand. Add in uncertainty regarding big data return on investment, and it’s a miracle businesses are doing big data at all.

Still, among organizations that have dabbled in run-ning big data clusters on Hadoop, Spark and the like, a few themes about the technical and business challenges of big data infrastructure have emerged.

bIG DAtA, bIG QUestIoNs

A large telecommunications provider is building a new digital service that will launch later this year, and plans to

hoMe

BIg DATA

ERHUI1979/ISToCK

the trouble with big Data Infrastructure

There are lots of ways big data doesn’t fit with existing infrastructure. Here are a few.

By ALEx BARRETT


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use Hadoop to analyze content, usage and monetization (advertising) data generated by the service. But because this service is brand new, it’s hard to know what kind of big data infrastructure to put in place, said the vice president of technology responsible for the build-out.

“It’s impossible to do any kind of capacity planning on a product that hasn’t launched yet,” he said.

Indeed, the emerging quality of most big data initiatives is actually pervasive. “The nature of most big data deploy-ments is much more nascent than I thought it would be,” said Andrew Warfield, CTO at Coho Data, a provider of scale-out storage infrastructure.

But that doesn’t mean organizations shouldn’t pay a lot of attention to big data initiatives. Even if an organization only dabbles in big data, “it runs the big risk of this stuff becoming important,” Warfield said, behooving them to think about infrastructure upfront.

For the telecommunications provider, that meant taking an incremental approach. It used software from BlueData Software to run big data clusters on top of com-modity (inexpensive) hardware that can access data from existing storage systems.

DAtA heRe, theRe AND eveRyWheRe

If data is born in the cloud, it makes sense to analyze it there. If data is all on premises, supporting big data infra-structure should be there too. But data that is scattered all over the place complicates the big data infrastructure equation.

The telecommunication provider’s service will use data from both the cloud and on premises. It’s important for any big data solution to support both, for compliance reasons and to save time and network bandwidth. “Repli-cating production data is tough,” the VP said. “We want to allow all instances to point to a single source.”

Alternately, information that data scientists want to analyze is available, but they can’t use it because it resides on storage infrastructure that is not accessible by its big data compute farm, Coho’s Warfield said. One solution is storage hardware that exposes data via big data-friendly protocols such as HDFS, or with a RESTful API.

n There are few rules, and even fewer best practices on how to best run big data workloads.

n Big data analytics can be a real drain on infrastructure—both in resources and expertise.

n A few themes about the technical and business challenges of big data infrastructure have emerged.

HIgHLIgHTS

It’s hARD to kNoW WhAt kIND oF bIG DAtA INFRAstRUCtURe to pUt IN plACe.


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look oUt FoR lAteNCy

The time it takes to move data from the storage array to the compute farm is a performance killer for a certain subset of big data analytics. What if you could avoid that latency by leaving the data where it is, and bring the application to it, rather than schlep the data across a network to the compute farm?

The notion of bringing compute to the data isn’t really new, but there is a new twist: Docker. Coho Data, for in-stance, did a proof of concept along with Intel at a large financial services company to run Hadoop workloads directly on its compute nodes, packaged in the form of Docker containers.

The idea behind running Docker containers directly on the storage array is to run ad hoc analytics closer to the data, without having to move data over the network, and take advantage of any available compute resources. “The platform has always been CPU-heavy relative to other storage platforms,” Warfield said. “All the more so when you put flash into the system. The question then becomes, ‘How do I get more value out of this resource?’ ”

Running Dockerized applications directly on a storage array is interesting, but the workload needs to be carefully evaluated to see if it’s a good fit, said Bubba Hines, a vice president at Signature Tech Studios, which offers a docu-ment management service for the construction industry. The service is built on top of Amazon Web Services and uses dedicated storage as a service from Zadara Storage. The firm recently began evaluating the new Zadara Con-tainer Service, in which Dockerized apps run directly on the storage array, with direct access to local drives. According to Hines, there are several plausible use cases: running a containerized version of its disaster recovery service on the storage array to continually monitor for changes in customer data or jobs that modify or verify primary storage data.

But it wouldn’t make sense to use the Zadara Container Service for all of its data processing needs. Signature Tech Studio’s bread and butter is performing data transforma-tions on construction blueprints, which it has already largely Dockerized. But “we’re probably not going to move all those [Docker containers] in to the [Zadara] Container Service because the size and scale just doesn’t make sense,” said Hines. “We have to look for workloads where we can really benefit from low-latency.”

tURF WARs

Technical challenges aside, perhaps the most difficult thing to navigate in a big data initiative is politics between data scientists, lines of business and central IT. Indeed, IT teams are wary of investing in infrastructure for big data

RUNNING DoCkeRIZeD ApplI-CAtIoNs DIReCtly oN A stoR-AGe ARRAy Is INteRestING, bUt the WoRkloAD NeeDs to be CAReFUlly evAlUAteD to see IF It’s A GooD FIt.


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initiatives, said Coho’s Warfield, and pitching them dedi-cated big data infrastructure can be difficult.

“IT tends to have an enormous amount of suspicion about whether this [big data] stuff is going to be success-ful,” said Warfield. As a result, individual lines of business often circumvent IT altogether, and buy, build and manage any big data infrastructure themselves.

Any department that opts to go-it-alone may get what’s coming to them, but savvy IT professionals know that no good can come of this. “I’ve talked to really terrified IT groups seeing 10- to 12-node clusters out there that are to-tally out of their control,” Warfield said. “Without a strong systems administration competency, organizations just dipping their toes in the big data waters have a lot of trou-ble just even getting these tools up and running—getting

the data in, launching workflows, backing it up,” Warfield said, to say nothing of the operational and financial ben-efits of an IT-centric approach.

Among savvy businesses, the goal should be compro-mise, said the telecommunications provider VP.

“Our main goal was to have an infrastructure that al-lows us to have some control over it, rather than having to go back to IT every time we need to make a change,” the VP said. “IT sets up the hardware, and then my team had administrative control.” That way, “we can control our assets on a virtual level, and let IT focus on the infra-structure.” n

Alex bARRett is editor and chief of Modern Infrastructure. Contact her at [email protected].


w


survey says Desktop virtualization technologies expand Home

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The Next Big Thing

In the Mix

D What are your primary reasons for expanding desktop virtualization technology?*

D Which of the following desktop virtualization approaches will you implement within the next year?*

SoURCE: TECHTARgET DESKToP VIRTUALIzATIoN SURVEy; BASED off RESPoNSES fRoM 512 IT AND BUSINESS PRofESSIoNALS.

*MULTIPLE SELECTIoNS ALLoWED oN ALL QUESTIoNS

56D Percentage of respondents who are switching hypervisors due to cost factors*



71%

55%

53%

50%

39%

39%

27%

27%

22%

13%

13%

Simplify management

Enable users to work from anywhere

Increase security

Save money

ByoD

Enable users to work with tablets / phones

Legacy application support

Support oS migrations

Enable contract employees

offshore developers

Mergers / acquisitions

78%

VDI

43%

App virtualization

34%

Client VMs

27%

Terminal Services /

RDSH


WhAt’s thAt RUMblING noise you hear? That’s the sound of millions of Docker application containers being generated by developers, barreling right this way, straight into the enterprise data center.

Indeed, developers have seized upon Docker with a fervor that hasn’t been seen in a while. The application container project and company only launched in 2013, but it is already valued at over $1 billion. Docker Container has been downloaded more than 400 million times, and there are over 100,000 “Dockerized” applications in Docker Hub. Growth shows no sign of abating, whether it occurs via Silicon Valley startups, or brick and mortar enterprises.

For infrastructure and operations teams, the growth of application containers is both good news and bad. On the bright side, containers are a lightweight form of virtualization that make very effective use of underlying infrastructure—container adopters report that the den-sity with containers is more than ten times that of virtual machines running on a hypervisor. And because a lot of container implementations are open source, it’s also a cost effective—sometimes even free—form of virtualization.

But containers’ strengths can also be their weaknesses. Containers are small, stateless and ephemeral—they can

brace for Containers

for infrastructure and operations teams, the growth of application containers

is both good news and bad. By ALEx BARRETT

CoNTAINERS

hoMeALExLMx/ISToCK


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come online in seconds and disappear just as fast. Mon-itoring provider New Relic recently analyzed the data it collected as part of its new Docker Monitoring service, and found that the vast majority of Docker containers have a lifespan of less than five minutes. This sets us up to think of containers as a new class of servers.

“We’re used to thinking about servers as pets and serv-ers as cattle,” said Abner Germanow, New Relic senior director of enterprise marketing. “Here, we have a new category—servers as bacteria.”

Containers are particularly copious when they are de-ployed as part of a microservices architecture, said Dustin Kirkland, Ubuntu product manager at Canonical, the sponsor of the Ubuntu Linux distribution, which supports Docker application containers as well as more traditional operating system containers. Using application containers in a microservices configuration, “the ethos is to put a single process in to the application container, and then orchestrate them in to a big complex service,” he said.

But when you have a lot of single-process application containers, “things can get hard in a hurry,” Kirkland said. “Dockerizing the first few applications is easy, but when you get to the point where you’ve Dockerized everything, that’s when you get in to the realm of needing a container orchestration and management layer.” As such, Kirkland

prefers to think of containers as precious babies rather than expendable bacteria. “The smaller they are, the more attention, care and feeding they need.”

heRDING CoNtAINeRs

The open-source community has been working on plat-forms and tools to manage and orchestrate dynamic microservices environments for years now, and those projects are gaining steam with the emergence of Docker application containers.

The Apache Mesos project, for example, is a distributed systems kernel build that works across a set of data center resources such as compute, network, storage and applica-tions, and arbitrates access to those resources.

The problem with the legacy data center is that “some human says ‘I want to run on that machine,’” said Matt Trifiro, senior vice president with Mesosphere, which sells a commercial version of Mesos that it describes as a “data center operating system.” Through a combination of elements such a resource scheduler and Linux init systems, Mesosphere abstracts the hardware and works to give applications “most of what they want” in terms of resources, Trifiro said.

CoreOS too, is working to extend the benefits of con-

n Docker has been downloaded more than 400 million times, and there are 100,000 ‘Dockerized’ apps.

n Containers are often a cost effective—sometimes even free form of virtualization.

n Containers are small, stateless and ephemeral—they come online in seconds, and disappear just as fast.

HIgHLIgHTS


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tainers, with “Googled infrastructure for everyone else,” said Alex Polvi, CoreOS CEO. Starting with a container and Google’s Kubernetes container orchestration system, CoreOS Tectonic adds tools such as Fleet, a cluster man-agement tool that presents the cluster as if it had a single init system, and Flannel, for container-to-container net-working across disparate hosts.

“You need more than just Docker to build a useful system, the same way you need more than that a nail to

build a house (even though your house is full of nails),” said Polvi.

Docker, meanwhile, has sought to capitalize upon its leadership position as the de facto format of the appli-cation container, and is developing a series of tools that can help with container orchestration and management. Examples include Docker Swarm, Docker Networking and Docker Compose, all regrouped under the Project Orca initiative. And vendors that have traditionally offered platform as a service (PaaS) are working to increase their container management capabilities, including Red Hat, Deis and Jelastic, to name a few.

There are also closed-source approaches to the con-tainer management problem. StackEngine, for instance, recently released its Container Application Center, which provides configuration management, application deploy-ment, orchestration and operations management, and is designed for use by both developers and operations teams. “Mesos and Kubernetes are complex and have a lot of building blocks,” said Bob Quillin, StackEngine founder and CEO. “We solve the same problems, but coming from the enterprise.”

oNe MAN’s tRAsh

But how necessary are container management tools? It depends on who you ask.

Built.io is a service provider that offers a back end for organizations developing mobile applications. It also allows customers to upload custom code to its service in the form of a Docker container.

the Container standards pusheFFoRts to stANDARDIZe containers and their man-

agement have ramped up in recent months. In

June, Docker announced that it would donate

the code for its application format and runtime

to the new open Container Project (oCP) under

the Linux foundation, to avoid fragmentation.

CoreoS, which had been promoting its own Rocket

container format, signed on as a member of oCP.

Then, in July, google announced that its Ku-

bernetes project had achieved version 1.0 status,

and then donated the code to the new Cloud Na-

tive Computing foundation, also under the Linux

foundation. At launch, the foundation included 22

members, including google, Docker, CoreoS and

Mesosphere. n


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Built.io started working with Docker back in the pre-historic days of 2013, when there were very few (if any) management tools available for the platform. As such, it created its own API-based management layer that performs tasks such as starting and stopping containers and restarting them in the event of a failure.

But the ephemeral nature of the containers that Built- .io runs also limits its need for elaborate workload orches-tration and placement tools such as Google Kubernetes or Mesosphere, said Nishant Patel, Built.io CTO. Instead, the team simply monitors the queue of containers that it needs to process, and if the queue fills up, it launches another cluster on AWS. “Scaling up and down is pretty easy,” said Patel.

But at GE Appliances in Louisville, Ky., a good container management system is paramount to container success. Last year, the firm developed a self-service test and dev private cloud based on Docker and Mesosphere, to im-prove the time between a developer submitting code, and getting it up and running. An initial infrastructure as a service implementation whittled that process down from six weeks to an underwhelming three.

The old system, “had an atrocious rate of adoption,” said Brett Luckabaugh, GE Appliances enterprise software architect. “The barrier to entry was just too high for a lot of developers,” for instance, requiring them to learn Puppet to automate infrastructure builds. “It’s not like in the hy-perscale market where they have thousands of nodes and a few apps. For us it’s just the opposite—we have thousands of apps on a few nodes.”

The combination of Docker plus Mesosphere has largely

been a winning one for GE. Docker provides high porta-bility, and developers love it. “If you can use a shell, you can grasp a Dockerfile,” Luckabaugh said. Mesosphere, meanwhile, provides fast deployments and scheduling of tasks, scaling, management of containers, self-healing/fault tolerance and overall simplification of data center management. A year in to the project, GE was running 350 applications across 800 containers with Docker, with many more in the pipeline.

But this is by no means a done deal. For instance, GE Appliances uses a scheduler called Marathon in conjunc-tion with Mesos resource management, and integration with Docker is still nascent. That led the team to go for-ward with building its own system, an internal Web app it calls Voyager that provides automated Docker builds, service discovery and load balancing, plus a user interface and API access. Going forward, the team will also keep its eye on orchestration and management tools from Docker proper, to see what value they bring to the table.

MIND the GAp

Enterprise IT shops are interested in containers, but there’s a lot that needs to happen before they adopt them whole-hog, said Andi Mann, business technology strate-gist at Sageable, an independent technology consulting firm.

“I’m hearing a lot of enterprises talk about whether they should or shouldn’t adopt containers, which usually means that they will,” said Mann. “We’re already seeing a lot of traction in agile development environments, where


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there are strong processes in place for open source tools, but enterprise-wide adoption is a different story, and “the difference is the management layer.”

“Sure, there are a lot of orchestration tools, for example, but we’re still missing a lot of the more mundane man-agement features such as test automation, provisioning, security and performance monitoring,” Mann said.

Take container monitoring, for example. Getting in-formation and performance metrics about the container itself has been made easier as of late with the introduction of the Docker Stats API, said Raj Sabhlok, president at ManageEngine, a management software vendor. But, “in order for operations teams to feel totally confident with the manageability of containers, container management data will have to be correlated with the underlying Linux operating system and the application itself,” he said.

Indeed, the whole field of containers is so nascent, it’s hard to predict how it will play out in the enterprise, said Canonical’s Kirkland.

“At a large scale, it absolutely makes sense,” he said—the Netflixes and Googles of the world “absolutely need that stack.” But for others, “it’s a slippery slope.”

“You have a 25-year old developer in the Valley sitting at a Starbucks running Docker on his laptop. [With Docker], he writes a piece of code, and the next thing you know, he’s moved it into production at Amazon. He tells his supervisor and the next thing you know, they’re putting everything in Docker,” Kirkland said.

But that strategy, while it appears to be easy, isn’t always necessarily the way to go, he said. “It may take a CTO to take a step back and ask—‘Are the problems I really have

solved by this solution?’ ” The answer may be yes, but then again, it may not be. n

Alex bARRett is editor in chief of Modern Infrastructure. Email her at [email protected].

Application vs. operating system ContainersopeRAtING systeM CoNtAINeRs have a history that

goes back decades, but application containers à

la Docker are a relatively new phenomenon. op-

erating system containers such as Solaris zones,

BSD Jails and Linux LxC share a single kernel, but

each container can run multiple processes and

services.

“With [LxC], you can run an entire oS,” said

Dustin Kirkland, Ubuntu product manager at Ca-

nonical; “we boot the init system”—Linux-speak

for the first process to run once the kernel is

loaded. Application containers, meanwhile, run

a single process per container, and interaction

with the underlying operating system and kernel

is handled by an intermediary such as Docker

Engine, which is responsible for building, distrib-

uting and running application containers. n



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ARe We ReADy for artificially intelligent infrastructure in the enterprise data center?

We are always driven to try to do smarter things faster. It’s human nature. In our data centers, we layer machine learning algorithms over big and fast data streams to cre-ate that special competitive business edge (or greater so-cial benefit!). Yet for all its processing power, performance and capacity, today’s digital-based computing and storage can’t compare to what goes on inside each of our very own, very analog brains, which vastly outstrip digital architec-tures by six, seven or even eight orders of magnitude. If we want to compute at biological scales and speeds, we must take advantage of new forms of hardware that transcend

the strictly digital.Many applications of machine learning are based on

examining data’s inherent patterns and behavior, and then using that intelligence to classify what we know, predict what comes next, and identify abnormalities. This isn’t terribly different from our own neurons and synapses, which learn from incoming streams of signals, store that learning and allow it to be used “forward” to make more intelligent decisions (or take actions). In the last 30 years, AI practitioners have built practical neural nets and other types of machine learning algorithms for various applica-tions, but they are all bound today by the limitations of digital scale (an exponentially growing Web of intercon-nections is but one facet of scale) and speed.

Today’s digital computing infrastructure, based on switching digital bits, faces some big hurdles to keep up with Moore’s law. Even if there are a couple of magni-tudes of improvement yet to be squeezed out of the tradi-tional digital design paradigm, there are inherent limits in power consumption, scale and speed. Whether we’re evolving artificial intelligence into humanoid robots or more practically scaling machine learning to ever-larger big data sets to better target the advertising budget, there simply isn’t enough raw power available to reach biological scale and density with traditional computing infrastructure.

Ultimately, power is the real shortcoming. “Message

THE NExT BIg THINg

the Analog RevolutionEmerging memristor technology could pave the way for computing systems on par with the human brain. By MIKE MATCHETT


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passing” or communicating a signal (data) back and forth between components is one of the key wastrels. At the fundamental level of digital design an awful lot of I/O between CPUs and everything else must happen for even the smallest of data processing tasks. Even as we increase densities, forge smaller chips or add flash closer to the CPU, it will still take significant energy and time to move bits around the digital architecture.

In our brains, memory, storage and processing are all intimately converged. Unlike digital systems, we don’t need megawatts of power to get out of bed in the morning because our brains run a low power analog-based archi-tecture. Analog circuitry, if custom built for the problem at hand, gets to the point at the speed of light directly, rather than requiring a large number of instruction cycles. And with continuously valued output, it could calculate with arbitrary precision. Further, if persistent storage is inherent in the circuit versus stored digitally as bits on some remote device, there would also not be any stagger-ing I/O waits.

sAy hello to MeMRIstoRs

Of course silicon devices are fundamentally analog, but we’ve built them up into complexly connected digital logic gates and bit storage. But what if we could go “back to the future” and design silicon for analog computing circuitry at today’s silicon chip level densities? The new breakthrough here is exploiting the analog properties of the emerging new class of memristive devices.

A memristor is a device that can change its internal

resistance based on electrical signals fed into it—and that persistent resistance can be measured and used as non-volatile memory. Memristors are fast silicon devices like DRAM—at least 10 times faster than NAND-based NVRAM (flash) and so can be used as main memory. HP for one has been researching newer memristive

technologies for persistent digital memory, but has not yet quite been able to bring this to market. If someone can, it could possibly usher in a whole next generation of digital computing architectures that converge storage and memory.

But now we’ve seen at least one startup, Knowm Inc. pioneering a brilliant new form of computing that lever-ages memristive technology to not only persist data in fast memory, but to inherently—and in one operation—calcu-late serious compute functions that would otherwise re-quire the stored data to be offloaded into CPUs, processed and written back. Knowm claims to leverage the analog properties of small memristor circuits—a “synapse” that comes with an inherent adaptive learning capability. Feed it a signal and it can directly learn—and at the same time persistently store—the pattern it finds in that signal.

Theoretically, by building up from this basic functional

A MeMRIstoR Is A DevICe thAt CAN ChANGe Its INteRNAl ResIstANCe bAseD oN eleCtRICAl sIGNAls.


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unit, pretty much any machine learning algorithm could be tremendously accelerated. While Knowm is in its early days, it already offers a full stack of technologies—discrete working synapse chips to play with, scalable simulators, defined low-level APIs and higher-level machine learning libraries, plus a service that can help layer large quantities of its synapses directly onto existing CMOS (Back End of Line or BEOL) designs.

With apologies to AI buffs and Terminator aficionados, we here at Taneja Group think the opportunity for disrup-tion is much larger than machine learning acceleration. A new hardware solution, what Knowm has termed a

“Neural Processing Unit” or NPU, that intelligently har-nesses analog hardware functions for extremely fast, low-power, dense and storage-converged computing would represent a truly significant change and turning point for the whole computing industry. I look forward to finding out who will be able to take advantage of this type of com-puting solution first, and potentially cause a massively disruptive shift in not just machine learning, but in how all computing is done. n

MIke MAtChett is a senior analyst and consultant at Taneja Group. Contact him via email at [email protected].



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We oFteN IDolIZe people who make big gestures: individu-als on the nightly news who braved dangerous conditions to save others, or superstar athletes who made the big play in a game, such as hitting a home run or scoring a clutch goal. Hollywood reinforces this with action movies; he-roes running around saving people and stopping villains in big ways. As a result we’ve become accustomed to the idea that big gestures stop the bad guys, help get the girl or guy and generally allow one to succeed in life.

What the nightly news and Hollywood never show, though, is all the training those brave people endured to prepare for the dangerous conditions. They never show the time and monotonous training that went into

becoming a sports great, a member of a SEAL team, a firefighter or a superhero. Would you go to a movie that accurately represents how long it takes IMF agent Ethan Hunt to move around the globe to find the bad guys?

The nightly news and Hollywood also never do any type of root-cause analysis. Why did we need a brave fire-fighter in the first place? Because someone failed and set a building on fire. Why did we need a SEAL team? Because diplomacy was completely ineffective. And what’s the to-tal cost of ownership for a superhero? Those folks cause way more damage than the villains do when solving the problem or saving the day.

IT heroes must have a critical eye trained on them. Why did they need to be heroic? Did they need better training instead? Who screwed up? Sometimes you discover that the cause of the problem was the “hero” themselves. When the root problem is found it needs to be fixed, so nobody ever needs to be heroic like that again. Heroism in IT is a failure of IT.

The same is true for IT’s equivalent of power hitters. It’s exciting to hit a home run, but when you’re always swinging for the fences you fly out and strike out way more than you get on base. It’s the folks who play what baseball calls “small ball” who get things done in IT. Small ball gets people on base, and advances people methodically until they start to score.

This is how things get done in effective IT organizations,

IN THE MIx

A small ball Approach to ItIt’s fun for IT to be the hero, but a low-key approach may be the most effective way to get things done. By BoB PLANKERS


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too. You can effect large changes in an organization by changing roles just a little bit each day or week. There is way less overhead because you don’t have to deal with fear of change. And it’s usually easier to deal with little problems, caused by little changes, than big ones caused by big changes. Nobody can anticipate all problems, so by intentionally making them smaller you’re doing yourself a favor.

Second, many small successes don’t lead to organiza-tional inertia. Whereas the home run hitter needs just

the right conditions to do their work, a base hitter gets things done every day. Eventually they start helping their teammates score runs. I’d much rather be working with a team that gets things done every day than with a group of individuals who are waiting for just the right moment to shine. So stop idolizing IT heroes and stop letting IT swing for the fences; aim for lifetime achievement instead. n

bob plANkeRs is a virtualization and cloud architect at a major Midwestern university.

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Modern Infrastructure is a SearchDataCenter.com e-publication.

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