Friday, January 9, 2009

Predicting vitality of 'SOA' completely misses the point -- legacy IT is dead

While the software market gnashes its teeth over how alive service oriented architecture (SOA) is, the much more important opportunity -- and perhaps unique in the history of IT -- is being overlooked.

There's never better a better time to kill off your legacy IT systems.

The next two years presents the architects and strategists of enterprise IT an unprecedented and probably not to be repeated chance to re-factor the way they do business. Microsoft CEO Steve Ballmer is mulling over the implications of a "reset," rather than a recession, and it is the correct way to look at this period.

Here's why: It's long been an uncomfortable reality that the means of computing for the past 20 years have piled up inside of data centers, expensive and outdated but too complex and costly to replace. Nobody has wanted to rip and replace because of the transition pain and uncertainty. The old guard has been presented with a lot of good excuses for simply bearing the load of aging systems' costs while still piling on more new systems.

We now have the unique option of lowering the tolerance for the ongoing cost of the old, while finding far fewer excuses for putting off the pain of change. In other words, now is the time for rip and replace. But it's more than that, it's time for executing on IT transformation writ large, of moving beyond physical systems and into the hybrid pool of myriad services ... with the end goal of finding the right combination of systems and services for each and every IT problem set.

And defining IT differently needs to be done, too, while we're at it. We need to stop thinking of IT as an attached appendage of each and every specific and isolated enterprise. Yep, 2000 fully operational and massive appendages for the Global 2000. All costly, hugely redundant, unique largely only in how complex and costly they are all on their own.

Instead, IT should be seen as a set of problems to be solved by the best means, and common means should be sought for a great deal of the load. Rather than an IT appendage at each enterprises' actual locations, solutions should be brought to the IT problems by any best means. It means catching up to reality. The reality is that the boundaries of IT are permeable, malleable and dynamic. Being caught in the old world of on-premises and monolithic systems for each application and data set is at odds with what is available efficiently as services -- internal, external and hybrid. Corporations have long sought these methods for procuring other business services, and IT is no different.

As Moore's Law and other modern IT productivity improvements have drastically cut the cost of newer IT solutions and technologies, it has made the costs of maintaining the legacy systems all the higher. The personnel and maintenance arsenals these systems require simply to keep producing a static productivity benefit are in a word ... wasteful. We have been through a long period of spending a lot of money at integration capabilities to extend the value of aging systems by trying to conjure a multiplier effect of 1 system integrated to 1 system equals 3 systems value. More often than not the math does not return a high enough rate of return.

These effects have given enterprise IT the stigma of black box cost centers. The business strategists feel IT is an extortion racket. They fear more of the same, but now with less corporate revenues and therefore less IT budget to work with.

And that's where SOA's death comes in. If the ROI on the money spent to achieve SOA benefits is not overwhelming, or transparent, the logic goes, then the effort is mute, dead, not worth the pain. Actually the opposite is true, and now more than ever.

The analogy of trying to change the wings of an airplane while keeping it flying is often used to describe the quandary of re-architecting your IT universe to an appreciable level while also meeting the SLAs and expanding the capacity and reliability of the older systems. In other words, the relentless pressure of keeping up with growth in the use of and demand on IT has handicapped the task of modernization. With a tight budget since 2001, many IT departments are far too busy putting out the fires of meeting demand to be much involved with resetting the way in which IT is conducted.

One of the chief pain points in avoiding the rip and replaces necessary to move aggressively to modern IT services -- services for integration/interoperability, for infrastructure resources, for app dev, for data management, for software as a service (SaaS), for cloud, for hybrids, for business process modeling, for many aspects of the IT lifecycle -- is that IT has been too busy, too stretched. It's consequently perceived as too risky ... Too hard to sell to the bean counters. Conventional logic holds that this only get worse in a recession.

Increasing demand for IT performance has been a convenient excuse to stay the legacy course, keep investments in new technology modest -- after all, we don't have the time to absorb SOA properly. Let's just get more Band-Aids. This all, like a giant Ponzi scheme, works quite well when the economy and profits are growing. We now know those days are over for some considerable period of time.

And so here we may find the silver lining, for IT strategists at least, in the rapid and severe economic contraction now upon us. A confluence of variables should tip the scales to make IT transformation more practical and attainable than ever. But you may only have a year or two to capitalize on this opportunity.

I suggest that IT organizations look to a new breed of triage for their existing IT universe, and cull out and rip out as much as possible. Kill it. Seek the newer -- dare I say it -- vital SOA and SaaS/cloud alternatives. Examine how open source software and models make sense. Liberally deploy virtualization. Look at how virtual desktop infrastructure (VDI) makes sense for more workers. Examine how a netbook or mobile device can fill the needs of more users in more places in more ways.

And here's the key: Actually define the business processes you need to support first, then identify the resources that the users and customers need to act on these processes, and procure and integrate the IT services (however best available) to fulfill the model. Repeat. Reuse assets as appropriate. Govern the whole she-bang centrally with policy and automation as the goal. This is SOA. It is not dead.

At the same time, boot up the next generation data centers that can play in hybrid services models and at lean total costs -- and inject as much logic and data from the older systems as possible. Use the SaaS and cloud options available now to replace the older systems, and then decide the best medium-term means to produce or procure those services. Find a governance model that allows you to manage the services and resources regardless of where they reside or how they are procured. Rip out and kill the remaining legacy systems that cost you dearly and provide static or declining productivity. You can do this. Now is the time. Be brave.

Many companies will slowly go under, go bankrupt or plunge into a new ownership form that produces the ultimate reset for IT. It is the off button. Just turn the IT off and sell the hardware on eBay. Those firms -- or remaining valued elements of the old firms -- that emerge from the ashes of these drastic business restructurings will also get an abrupt IT reset. They may be able to begin anew with services as central, with SaaS as pervasive, and with cloud-based app dev the norm. But that's some tough sledding to get to more productive and agile IT.

Many more companies will see a period of reduced demand on the IT systems. If you lay off 15% of the workforce, there's bound to be more slack in the demand on applications (once you've provisioned the employees off properly). If your revenues decline by 30%, there will be more slack on the demand on applications and data servers. If you merge with another company, there's a lot of IT redundancy to remove.

You no longer have the excuse of being too busy and too capacity-strained to entertain those ultimately productivity-rich systems resets and to embrace SOA. And you can attain the IT modernization benefits now at far lower capital costs because you can bargain stridently and successfully with the integrators, hardware vendors, software providers, and all the rest. You find qualified employees to hire. You can seek out more IT services on a per-user, per-month subscription model. No need to pay for the IT behind those gone 15% of laid-off employees until you rehire them, and then watch your IT costs become far more commensurate to your actual needs. You won't need the huge capital outlays first, and the productivity later.

Those companies that make this transition now will be powerfully more agile, with lower total IT costs and the ability to swiftly exploit new SOA, SaaS and cloud innovations over the coming years. You need to both survive the recession and position yourself to dominate afterward, in the brave new world. You'll need the right IT mentality and models to do it.

So the actual costs of meaningful change in IT for the next few years will come at an historically low real cost, with very high rates of return after the transition. And the portion of IT spend devoted to capital outlays will decline. And you can bargain (perhaps even push out payments for 6 months or a year) on the professional services, integrators, outsourcers, and other transitional expenses. Other aspects of the global economy are facing a reset, as are governaments, and IT should be a leader not a laggard -- both as an example and as an enabler to the larger transitions.

Now is the time to rip and replace your thinking about IT, and so you'll want to replace your legacy systems and obsolete IT solution models with vital and efficient SOA processes and hybrid IT resources acquisition models.

Actually, now to think of it, high-cost and lock-in legacy IT is what is really dead, finally. RIP.

Wednesday, January 7, 2009

Webinar: IT analysts delve into desktop as service/VDI cloud opportunities for enterprises and telcos

Read a full transcript of the webinar discussion. Listen and watch.

Many of us expect that delivery of the full PC desktop experience and applications as a service will grow in use and value. For many users inside of enterprises, at call centers and for point of sale uses, their requirements can be me with a low-cost thin client and desktop as a service (DaaS) approach. The technology is largely here today.

But stark economics may end up driving the adoption. The value and cost savings from virtualization techniques escalate as they extend from servers to applications to the PC experience itself.

We're also seeing a lot of churn in the concept of the client device itself. The notion of a full-fledged tower PC for every desktop use scenario -- and the associated costs of maintenance, support, security, and upgrades -- is giving way to the right device for the use case. Why not the right software service mix for the right use case too?

For hosting organizations, telcos, cloud services providers and software as a service providers, the allure of providing a complete PC desktop and the required applications as a subscription is enticing. But this is a big subject, and many people are still just wrapping their heads around the implications.

Consequently, I recently participated in a webinar with virtual desktop infrastructure vendor Desktone that I think really gets to some core issues and insights on VDI and the models that support it. Joining me in the discussion are Jeff Fisher, senior director of strategic development at Desktone; Rachel Chalmers, research director of infrastructure management, at The 451 Group, and Robin Bloor, analyst at Hurwitz & Associates.

Here are some excerpts:
Fisher: Clearly, everyone is talking about cloud computing. You can’t look anywhere within IT and not hear about it. It’s amazing to see it surpassing even the frenzy around virtualization. In fact, most of the conversations people are having today are around virtualization and how it can take place in the cloud. Everyone wants to focus on all the benefits, including anytime/anywhere access and subscription economics.

However, like any other major trend that unfolds in IT, there are a number of challenges with the cloud. When people talk about cloud computing with respect to the enterprise, in most cases they’re talking about virtualizing server workloads and moving those workloads into a service provider cloud.

Clearly, that shift introduces a number of challenges. Most notable is the challenge of data security. Because server workloads are very tightly-coupled with their data tier, when you move the server or the server instance, you have to move the data. Most IT folks are not really comfortable with having their data reside in a service provider or external data center.

For that reason Desktone believes that it’s actually going to be virtual desktops, not servers, that are the better place to start and are going to be what jump starts this whole enterprise adoption of cloud computing.

The reason is pretty simple. Most fixed corporate desktop environments -- those are desktops that have a permanent home within your enterprise – already probably have their application and user data abstracted away from the actual desktop. The data is not stored locally. It’s stored somewhere on the network, whether it’s security credentials within the Active Directory (AD), whether it’s home drives that store user data, or it’s the back end of client-server applications. All the back end systems run within your data center.

The other interesting thing is this notion of the service-provider cloud, which is that it actually can traverse both the enterprise and the service provider data centers.

So, depending on the use case, service providers can either keep the virtual infrastructure and the racks powering that virtual infrastructure in their data center or they can, in certain cases, put the physical infrastructure within the enterprise data center, what we call the customer premise equipment model. The most important thing is that it doesn’t break the model.

There is flexibility in the location of the actual hosting infrastructure. Yet, no matter where it resides, whether it’s in a service provider data center or an enterprise data center, the service provider still owns and operates it and the enterprise still pays for it as a subscription.

Chalmers: There are three sensible places to run a desktop virtual machine. One is on the physical client, which gives you a whole bunch of benefits around the ability to encrypt and lock down a laptop and manage it remotely. One is to run it on the server, which is the very similarly tried-and-tested VM with VDI or Citrix XenDesktop method. That’s appropriate for a lot of these cases, but when you run out of server capacity or storage in the server-hosted desktop virtualization model a lot of companies would like elastic access to off-site resources.

This is particularly appropriate, for example, for retailers who see a big balloon in staffing – short-term and temporary staffing around the holiday seasons, although possibly not this year -- or for companies that are doing things off-shore and want to provide developer desktops in a very flexible way, or in education where companies get big summer classes, for example, and want to fire up a whole bunch of desktops for their students.

This kind of elastic provisioning is exactly what we see on the server virtualization side around cloud bursting. On the desktop side, you might want to do cloud bursting. You might even want to permanently host those desktops up in the cloud with a hosting provider and you want exactly the same things that you want from a server cloud deployment. You want a very, very clean interface between the cloud resources and the enterprise resources and you want a very, very granular charge back in billing.

And so, we see cloud-hosted desktop virtualization as a special case of server-hosted desktop virtualization.

Gardner: I think we’re entering a new era in how people conceive of compute resources. ... What’s happening now is that organizations are starting to re-evaluate the notion that a one-size-fits-all PC paradigm makes sense.

We have lots of different slices of different types of productivity workers. As Rachel mentioned, some come and go on a seasonal basis, some come and go on a project basis. We’re really looking at a slice-and-dice productivity in a new way, and that forces the organization to really re-evaluate the whole notion of application delivery.

When you start moving toward virtualization and you start re-thinking about infrastructure, you start re-thinking the relationship between hardware and software. You start re-thinking the relationship between tools and the deployment platform, as you elevate the virtualization and isolate applications away from the platform, and you start re-thinking about delivery.

If you take the step toward terminal services and delivering some applications across the wire from a server-based host, that continues to tip this a little bit toward, “Okay, if I could do it with a couple of apps, why not look at more? If I could do it with apps, why not with desktop? If I can do it with one desktop, why not with a mobile tier?”

If we look at the cost pressures that organizations are under, recognizing that it’s maintenance and support, and risk management and patch management that end up being the lion’s share of the cost of these systems, we’re really at a compelling point where the cost and the availability of different alternatives has really sparked sort of a re-thinking.

So we’re really going through this period of transformation, and I think that virtualization has been a catalyst to VDI and that VDI is therefore a catalyst into cloud. If you can do it through your servers, somebody else can do it through theirs.

When we start really seeing total costs tip as a result, the delta between doing it yourself and then doing it through some of these newer approaches is just super-compelling. Now that we’re entering into an economic period, where we’re challenged with top-line and bottom-line growth, people are not going to take baby steps. They’re going to be looking for transformative, real game-changing types of steps.

This is particularly relevant if they’re commodity level types of applications and services. It could be communications and messaging, it could be certain accounting or back office functions. It just makes a lot of sense to start re-evaluating. What we haven’t seen, unfortunately, is some clear methodologies about how to make these decisions and boundaries inside of organizations with any sort of common framework or approach.

It’s still a one-off company by company approach -- which workers should we keep on a full-fledged PC? Who should we put on a mobile Internet device, for example? Who could go into a cloud-based applications hosting type of scenario that you’ve been describing?

It’s still up in the air and I’m hoping that professional services and systems integrators over the next months and years will actually come up with some standard methodologies for going in and examining the cost-benefit analysis, what types of users and what types of functions and what types of applications it makes sense to put into these different ... environments.

Bloor: One of the things that is really important about what’s happening here with the virtualization of the desktop is just the very simple fact that desktop costs have never been well under control.

The interesting thing is that with end users that we’ve been talking to earlier this year, when they look at their user populations, they normally come to the conclusion that something like 70 or 80 percent of PC users are actually using the PC in a really simple way. The virtualization of those particular units is an awful lot easier to contemplate than the sophisticated population of heavy workstation use and so on.

With the trend that’s actually in operation here, and especially with the cloud option where you no longer need to be concerned about whether your data center actually has the capacity to do that kind of thing, there’s an opportunity with a simple investment of time to make a real big difference in the way the desktop is managed.

... From the corporate point of view, if you’re somebody that’s running a thousand desktops or more it’s a problem. It is a problem in terms of an awful lot of things but mostly it’s a support issue and it’s a management issue. When you get an implementation that involves changing the desktop from a PC to a thin client and you don’t put anything into the data center, it improves.

You’ve now got a situation where you don’t need cages in the data center running PC blades or running virtualized blades to actually provide the service. You don’t need to implement the networking stuff, the brokering capability, boost the networking in case it’s clashing with anything else, or re-engineer networks.

All you do is you go straight into the cloud and you have control of the cloud from the cloud. It’s not going to be completely pain free obviously, but it’s a fairly pain-free implementation.

It absolutely stunned me that a cloud offering became available earlier this year because that meant that somebody would actually have had to have been thinking about this two years ago in order to put together the technology that would enable such an offering like that.

Gardner: This is a much more lucid and rational architecture. We’ve found ourselves, over the past 15 or 20 years, sort of the victim of a disjointed market roll out. We really didn’t anticipate the role of the Internet, when client-server came about. Client-server came about quickly just after local area networks (LANs) were established.

We really hadn’t even rationalized how a LAN should work properly, before we were off and running into bringing browsers in TCP/IP stacks. So, in a sense, we've been tripping over and bouncing around from one very rapid shift in technology to another. I think we’re finally starting to think back and say, “Okay, what’s the real rational, proper architectural approach to this?”

We recognize that it’s not just going to be a PC on every desktop. It’s going to be a broadband Internet connection in every coat pocket, regardless of where you are. That fundamentally changes things. We’re still catching up to that shift.

Bloor: From an architect’s point of view, if nobody had influenced you in any way and you were just asked to draw out a sense of a virtualization of services to end users, you would probably head in this direction. I have no doubt about it. I’ve been an architect in my time, and it’s just very appealing. It looks like what Desktone DaaS has here is resources under control, and we’ve never had that with a PC.
Read a full transcript of the webinar discussion. Listen and watch.

Monday, January 5, 2009

A technical look at how parallel processing brings vast new capabilities to large-scale BI and data analysis

Listen to the podcast. Download the podcast. Find it on iTunes/iPod. Learn more. Sponsor: Greenplum.

Read a full transcript of the discussion.

Internet-scale data collecting, swarms of sensors outputs, and content clouds from the mobile device fabric -- as well as enterprises piling up ever more kinds of analytics metadata to analyze -- have stretched traditional data-management models to the breaking point.

Yet advances in parallel processing using multi-core chipsets have prompted new software approaches such as MapReduce that can handle these data chores at surprisingly low total cost. The technical response to oceans of data is something that has been building for some time. But the time now seems ripe to bring the technical solutions of lower-cost parallel computing advances into play with the economic imperatives of huge data crunching requirements.

And so just what are the technical underpinnings that support the new demands being placed on, and by, extreme data sets? What economies of scale can we anticipate? How will these advances spur the movement of data to Internet cloud models?

BriefingsDirect's Dana Gardner put these and other questions to a panel of new data architecture experts, to plumb into how parallelism, modern data infrastructure, and MapReduce technologies come together. He spoke with Joe Hellerstein, professor of computer science at UC Berkeley; Robin Bloor, analyst at Hurwitz & Associates, and Luke Lonergan, CTO and co-founder at Greenplum.

Here are some excerpts:
Data growth has been following and exceeding Moore's Law over time. What we've been seeing is that the data sets that people are gathering and storing over time have been doubling at a rate of even faster than every 18 months. ... We're going to see all kinds of large organizations gathering data from all sorts of automated sources.

... What's changed in the last few years is that clock speeds on processors have stopped doubling every 18 months. ... Instead, what they are doing is putting more processing cores on every chip. You can expect the number of processors on your chip to double every 18 months, but they're not going to get any faster.

So data is growing faster, and we have chips basically standing still, but you're getting more of them. If you want to take advantage of that data, you're going to have to program in parallel to make use of all those processors on the chips. That's the confluence that's happening.

There are very many people storing and analyzing more data. We're very encouraged that most of our customers are finding new uses for data that are earning them more money. Consequently, the driver to analyze more and more data continues to grow. As our customers get more successful, this use of data is becoming really important.

It's easy to parallelize the data. You break it up into little chunks and you throw it out to different machines. What can we do cleverly in computing with that kind of a framework? There are a lot of ideas for how to move forward ... where you are taking this massively parallel data-flow approach.

One thing that's kind of invisible is that there is a lot of data out there that's not being analyzed fast enough to be analyzed effectively. That's something that I think parallelism is going to address. ... The only reason not to gather that data is when you run out of affordable processing and storage. Anybody with the budget will have as much data as they can budget for and will try to monetize that. It's going to be pervasive.

The core problem we've solved is the ability for our engine to redistribute the data and the computation on the fly, as these queries and analysis are being performed. ... The combination of the software-switch interconnect, which Greenplum built into the Greenplum product, and the underlying use of commodity parallel computers, is brought together in this database system that makes it possible to do SQL query and languages like MapReduce with automatic parallelism.

Businesses have invested a tremendous amount of their time over the last 15 to 25 years in SQL, and some of the more traditional kinds of business analysis that pay off very well are ensconced in that programming model. So, packaging a system that can do transactional, mixed workloads with large amounts of concurrency, with applications that use the SQL paradigm, is very important.

Packaging this together as software plus hardware, making that available as a reference architecture for customers, has been very important and has been very successful in our accounts at New York Stock Exchange, Fox, MySpace, and many others.

The combination of SQL and MapReduce in a unified way in programming environments ... is a very pragmatic [step] that can help with people's ability to get their hands on data in an organization. ... You want to have the same access to all your data via either an SQL interface or a MapReduce programming interface. ... You ought to be able to access those with whatever language suits you, mix and match.

Some things are easier to do in MapReduce, and some things are easier to do in SQL, even when you know both. Good programmers have a lot of tools in their tool belt. They like to be able to use whatever tool is appropriate for the task. Having both of these things interleaved is really quite helpful.

[The solution] is about users being able to gain access to all that power. What really turned the corner for general data analysis using SQL is the ability for a user to not to have to worry about what kind of table structure they have. They can have lots of small tables joining to lots of big tables, and big tables joining to each other.

What the developer needs is an engine that doesn't care how the data is distributed, per se, just being able to use all of that parallelism on the problems of interest. ... The physical model of how the database is distributed in a shared nothing architecture in a Greenplum system is not visible to the developer.

There are a couple of questions about how an individual organization's data will end up in the cloud. Inevitably it will, but in the short-term, people like to keep their data close, particularly database data that's traditionally been in the warehouses, very carefully managed. ... It's going to be some time until we really see everybody's data warehouses up in the cloud. ... How long will it be until you really get big volumes of data in the cloud[?] The answer is that certainly new applications will be up there. We may start to see old data getting uploaded in the cloud as well.

We'll start to see big data sets up there that don't necessarily belong to anyone, and they are going to be big. In that environment, you can imagine big data analytics will have to run in the cloud, because that's where the data will be. One of the fun things about the cloud that's really exciting is the elasticity of the resources. You don't buy yourself a data center full of machines, but you rent as many machines as you need for a task.

If you have a task that's going to look at a lot of data, you would rent a lot of machines for a few hours, and then you would shrink your pool. What this is going to allow people to do is that even small organizations may, for a short period of time, look at an enormous amount of data, which perhaps doesn't originate in their own data production environment, but is something that they want to utilize for their purposes.

Disk densities show no signs of slowing down. So, data is going to be essentially no cost. The data-gathering infrastructure is also going to be mechanized. We're going through what I call the industrial revolution of data production. We're just going to build machines to generate data, because we think we can get value out of that data, and we can store it essentially for free.

The compute cost of multi-core with parallelism is going to continue Moore's Law. It's just going to continue it in a parallel programming environment. If we can get all those cores looking at all that data, it won't cost much to do that, and the cost of that will continue to shrink by half.

The only real barrier to the process is to make those systems easy to program and manageable. Cloud helps somewhat with manageability, and programming environments like SQL and MapReduce are well-suited to parallelism. We're going to just see an enormous use of data analysis over time. It's just going to grow, because it gets cheaper and cheaper and bigger and bigger.
Read a full transcript of the discussion.

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