An interesting article on Dell’s bespoke server unit which targets the world’s 20 largest hyperscale data center operators. Facebook was an early success story before they founded the Open Compute project and started designing their own servers, like Google before them. But now Dell is looking to commercialize some of the designs they originally developed for this space.

It has been five years since Forrest Norrod and his colleagues at Dell drew up the first custom server design on a napkin at a bar at the Driskill Hotel in Austin, Texas, getting the server maker into the tailoring business. Dell now custom fits servers for very precise workloads and can cater to the tight data center power and cooling requirements found at hyperscale web operators.

The tailoring unit, called Data Center Solutions (DCS), has now grown to over $1bn in sales, says Norrod. Norrod was vice president and general manager of the unit when Dell first started publicly talking about its operations back in October 2008. He is now general manager of server platforms at Dell, and tells El Reg that the DCS business is now a “greater than $1bn a year business,” adding: “We beat that a while ago.”

DCS was founded originally to chase the world’s top 20 hyperscale data center operators, and creates stripped-down, super-dense, and energy-efficient machines that can mean the different between a profit and a loss for those data center operators. These DCS machines were not aimed at general purpose server users, whose workloads generally run on one machine and who need RAID disk controllers, service processors, and other high availability features because all of their eggs are in one basket (if not literally, then there is one server per workload – so it amounts to the same if you are looking at it at the app level). The DCS custom designs were built for companies running parallel workloads that have redundancy, data replication, and failover built into the software stack – so a regular PowerEdge server would not just be overkill, it would be plain stupid.

Facebook was the poster child for the DCS business before the social media giant decided to launch the Open Compute project last April, open-sourcing its own server and data center designs and going straight to original design manufacturers (ODMs) to build its gear.

Dell struck oil with this custom server thing, but Google builds its own gear, and now so does Facebook. Even fellow Texan Rackspace Hosting is using whitebox servers to get more server for the dollar.

But a year before this all happened, Dell saw the writing on the bespoke server wall and did a smart thing: it partially commercialized some of the DCS designs and took them to market as the PowerEdge-C machines. But you can’t just log into the Dell site and buy a PowerEdge-C machine, you have to engage in a formal sales process so Dell can make sure you get the right iron.

“Our aim with the PowerEdge-Cs was for the next 1,000 customers who needed DCS-style machines, and we have blown way beyond that,” brags Steve Cumings, executive director of marketing for the DCS unit. And while Cumings won’t talk specific numbers, he adds “it is not 1,001, either”.

In the Dell lingo, the custom machines are known as the DCS “classic” boxes while the other cloudy boxes are called “PECs” after the abbreviation of their formal name. The PowerEdge-C machines not only traditional multi-node bare-bones servers in 1U and 2U chassis as well as a 3U chassis that can cram up to a dozen single-socket microservers into a 3U chassis. Dell has even built mini-servers based on VIA Technologies’ X86 processors for hosting customers looking for cheap, dedicated nodes. In all cases, these servers have shared power and fans and precious little else but CPUs, memory, and disks.

The DCS unit has another part of the business, which is the modular data centers built from the shipping container or from other modular components. Microsoft is a big Dell customer for modular data centers. Cumings says these containerized data centers are only available to the top-end hyperscale customers because there is so much demand that Dell can’t meet it. Of course, demand is a relative thing. Cumings estimates that worldwide, there are on the order of several hundred containers being used as data centers among the hyperscale crowd. “Our impression is that we are comfortably number one in that market,” says Cumings.

Whether containerized data centers can go quasi-volume, as the PowerEdge-C servers did with several of the DCS custom server designs, remains to be seen. A lot depends on how radically customers are prepared to change their data center operations, how outdated their glass houses are, and how quickly they need to upgrade.

Dell has not broken out shipments and revenues for the DCS unit in the past, but it has hinted that this stealthy part of its business would have been among the top five server shippers in 2008 – and that in some quarters would have ranked as high as number three. This ranking would also depend on the shipments for Dell’s other stealth server business, its OEM Solutions Group, which OEMs Dell’s traditional PowerEdge servers for appliances, kiosks, and other interested parties. Three years ago, the OEM Server Group was twice as large, in terms of revenues, as the DCS unit. The gap has probably closed significantly since then.

Back in October 2008, the DCS unit had 200 employees, mostly engineers and sales people who helped craft machines and the houses they run in for hyperscale customers. Three-and-a-half years later, DCS now has 400 employees. Both then and now, DCS relied on other parts of Dell for back office functions, parts acquisitions, and manufacturing. Norrod says that while DCS uses the same manufacturing facilities as the general-purpose PowerEdge machines, they tend to have dedicated lines because the DCS run is usually 10,000 or more of the same thing, rather than a handful of similar machines built on demand for one set of customer orders that day, followed by a complete new set of machines with different configurations once they are built. You have to tool and run a DCS line differently from a PowerEdge line, he says – and in some ways, it is easier.

If the employee count is any guide, then it looks like the DCS biz has at least doubled in that time and is probably about the same size as the Dell OEM Solutions Group. Dell had about $8.2bn in servers and networking revenues in the trailing four quarters, and it is not unreasonable that these two stealthy server units could account for around a third of Dell’s server revenues.

In the latest server number out of IDC, which covered the fourth quarter of 2011, density-optimized servers like those sold by the DCS unit accounted for 132,876 units (up 51.5 per cent) and generated $458m (up 33.8 per cent). Dell had 39 per cent share of shipments of these boxes (that’s 51,821 machines) and 45.2 per cent share of sales (or $195m). IDC says that this was double the revenue and shipments of the nearest competitor in this category. Clearly, IDC’s definition of a density optimized machine and DCS’ sales SKUs don’t overlap completely for DCS to be pushing more than $1bn in sales per year. But Dell wanted to brag about the IDC numbers just the same.

ARMed … and possibly Tilera’d and FPGA’d

Dell Enterprise Products Group, which makes the PowerEdge line, has been monkeying around with ARM-based servers for years now, and Cumings says that the DCS engineers took another stab at it with the latest rev of ARM chips from unnamed suppliers in the past year – just to keep up to speed on what is possible with these ARM chips in terms of performance and thermals.

“If we see a market develop, we are ready to go,” says Cumings. “We have done a good job in seeing potential markets coming. But we are not shipping a product now and I can’t tell you when we might.”

The DCS engineers have done some research on many-core processors from Tilera, but Cumings concedes that Dell has “spent less time looking at this”.

That said, when it comes to the DCS classic designs, Cumings says that Dell will “look at any technology that will solve a customer’s problem”.

That could mean field programmable gate arrays, GPU coprocessors, and all kinds of weird stuff someday. (That’s El Reg speculating, not Dell DCS talking.)

For now, the DCS unit is focused on five key markets: hyperscale web, big data, cloud, hosting, and high performance computing. The latest projections from IDC show CPU shipments in the cloud segment to be growing at a compound annual growth rate of 15 percent between 2012 and 2015, with HPC growing 7.3 per cent compared to shrinkage of 1.2 per cent in the traditional, general purpose server space. Cumings says that Dell’s DCS business in the five key growth areas is larger than the sub-markets at large, and won’t be specific about how much larger because this is a competitive advantage for Dell.

“The growth is significant enough that DCS was created with its own resources to chase the opportunity,” says Cumings. “Dell believes in this business and it continues to grow.”

A short history of supercomputing and the race to exascale computing.

I recently saw some estimates that show we should hit exascale supercomputer performance by around 2018. That seems a bit ambitious – if not stunningly optimistic – and the search to get some perspective led me on an hours-long meander through supercomputing history, plus what I like to call “Fun With Spreadsheets.”

Right now the fastest super is Fujitsu’s K system, which pegs the Flop-O-Meter at a whopping 10.51 petaflops. Looking at my watch, I notice that we’re barely into 2012; this gives the industry another six years or so to attain 990 more petaflops worth of performance and bring us to the exascale promised land.

This implies an increase in performance of around 115% per year over the next six years. Is this possible? Let’s take a trip in the way-back machine…

Just getting to megaflop performance took from the beginning of recorded history until 1964. If we start the clock with the Xia Dynasty at 2,000 BC, this means it took us 3,964 years to get from nothing to megaflops. This is a pretty meager rate of increase, probably somewhere around 0.17 per cent a year, but you have to factor in that everyone was busy fighting, exploring, coming up with new kinds of hats, and inventing the Morris Dance.

The first megaflop system, the Seymour Cray-designed Control Data CDC 6600, was delivered in 1964. It was a breakthrough in a number of ways: the first system to use newly-invented silicon-based processors, the first RISC-based CPU, and the first to use additional (but simpler) assist processors, called ‘peripheral processors,’ to handle I/O and feed tasks to the CPU. This was game-changing technology.

The transition from megaflop to gigaflop performance took only another 21 years with the introduction of the Cray-2, which hit the market in 1985. Seymour Cray broke away from Control Data in 1972 to start his own shop, Cray Research Inc. The Cray-2 delivered 1.9 gflops peak performance by extensively using integrated circuits (early use of modular building blocks), multiple processors (four units), and innovative full-immersion liquid cooling to handle the massive heat load. In its time, it was also game-changing technology. The Cray-2 was also highly stylish, with a futuristic design complimented by blue, red, or yellow panels. Here’s a PDF of a brochure covering the Cray-2.

Fast-forward another 11 years and we see the first system to sustain teraflop performance, the Intel-based ASCI Red system, which was also a big break from past supercomputer designs. Installed at Sandia National Lab in 1996, it’s an example of what we’ve come to expect from modern supercomputers with 9,298 Intel Pentium processors, a terabyte of RAM, and air cooling.

The compound annual performance growth rate (CAGR) for this move from gflop to tflop (another thousand-fold increase) is roughly 87.5 per cent per year, which won’t get us to exascale until midway through 2019 (just in time for the June Top500 list, I’d expect). Not too far off of the 2018 prediction, however.

Twelve years later, in 2008, the first petaflop (the IBM Roadrunner) system debuted. Achieving another 1000-fold performance increase in 12 years is equivalent to a 78 per cent compound annual growth rate. This is way faster than Moore’s Law, which has an implied CAGR of around 60%, but a little slower than the previous move from giga to teraflops. At this growth rate, we’ll reach exascale in 2020 – probably late in the year, but it might make the November 2020 Top500 list.

A mere three years after that, the K computer hit 10.51 pflops performance. The performance growth rate from Roadrunner to K? 116 per cent CAGR, which is almost exactly the growth rate necessary to deliver exascale by 2020.

Does this mean that we’ll see exascale systems in 2018 or even 2020? No, it doesn’t; it’s merely another data point in handicapping the race. This analysis simply looks at timelines; it ignores the problems inherent in housing, powering, and cooling a system that’s 1,000x faster than the current top performer, which sports more than 80,000 compute nodes, 700,000 processing cores, and uses enough power to run 12,000 households before they all get electric cars.

The technology challenges are mind-boggling, and it’s clear that simply applying ‘smaller but faster’ versions of today’s technology won’t get us over the exascale hump. It’s going to take some technology breakthroughs and new approaches. Even with these hurdles, I’m betting that we’ll see exascale performance before the end of 2020, putting us right in line with previous transitions.

Some predictions on cloud computing - with a shout out to the mainframe.

1. Brands May Come and Go – But No Technology Will Die

Not only are we not living in a ‘post-PC’ world, we are not even living in a ‘post-mainframe’ world! Cloud will not kill data centers, virtual will not kill physical, tablets will not kill PCs, Mac will not kill Windows, Android will not kill iOS, streaming will not kill DVDs. The technology pie is growing, our choices are expanding, and almost every slice is getting bigger. So be prepared to manage an ever-increasing selection of technologies across public and private boundaries.

2. Hybrid IT Will Be ‘The Next Big Thing’

‘Hybrid cloud’ was soooo 2011! In this new world of choices, business will expect hybrid IT: a combination of on-site and off-site; cloud and legacy; private and public; physical and virtual; social and secure; enterprise and consumer; desktop and server; mobile and static. Business will also expect IT to make them work together, whether IT owns the service or not. IT must act as a trusted advisor, as a service broker, and as quality assurance for this brave new world of complex Hybrid IT.

3. Service Quality Will Be IT’s Responsibility Again

As hybrid IT proliferates, business owners will (again) realize they do not want to manage technology; they just want it to work. In 2012, end users will increasingly expect IT to take responsibility for service quality, regardless of who is buying, selling, or delivering that service. IT will need to eliminate the blind spots in hybrid IT, actively support an explosion of devices, deal with complex cross-boundary services, and find a way to deliver a 360-degree service assurance across all facets of end-user experience.

4. Public Cloud Adoption Will Slow

Given the results of this year’s Longhaus research from Australia – an early adopter market and a bellwether for business technology – I suspect the rest of the world is in for a slowdown of public cloud adoption. Issues (perceived or real) with security, compliance, service quality, skills, staffing, complexity, and good old politics will all put the brakes on. Whether ‘cloud stall’ will be as pronounced as ‘virtual stall’ is unsure, but 2012 will see a marked slowdown in public cloud adoption.

5. Public Cloud ‘Gets’ Security

Sad but true – many (most?) enterprise decision-makers still do not trust public cloud. In 2012, IT must do a better job of deploying and explaining cloud security – and I believe we will! In 2012, CIOs will see security as less of a barrier to cloud adoption as organizations adopt more and better cloud-oriented security solutions – including solutions designed for complex hybrid cloud services, as well as solutions that are delivered through the cloud with easily-consumed Security SaaS options.

6. Big Iron is Back – Part I

No, mainframe is still not dead. On the contrary, 2012 will see the rise of the mainframe as a *gasp* cloud platform. Massively scalable, hosting critical (and underutilized) ‘big data’, capable of running complex cloud workloads on a variety of architectures (z/OS, Linux, UNIX, Windows), mainframe is really an obvious cloud platform. It will not replace commodity clouds, but large enterprises and governments especially will leverage their investments and bring big iron into their cloud mix.

7. Cloud Gets Heterogeneous

Not only will mainframe become part of the cloud landscape, but public cloud providers will also start to offer UNIX and maybe even other non-x86 platforms. I have recently seen this in action (CA did it internally years ago), and most large enterprises are heavily dependent on heterogeneous systems for their mission-critical applications. Despite the common myth that cloud == commodity servers, heterogeneous servers will start to become more available for large enterprise deployments.

8. Big Iron is Back – Part II

Big iron concepts of integrated compute, network, and storage are resurgent – but this is not your grandpa’s mainframe. Deployment of integrated fabrics like Cisco UCS and VCE Vblock will accelerate rapidly in 2012 as IT changes the way it thinks about integrated infrastructure for virtualization and cloud – and realizes how amazing these integrated boxes are for diverse, dynamic, high-volume workloads like desktop virtualization, pop-up data centers, and cloudbursting.

 9. ‘Grown-up’ Cloud Service Management Comes To The Forefront

In 2011, the NIST Cloud Reference Architecture devoted a whole section to ‘Cloud Service Management’, and IT started to talk about ‘grown-up’ disciplines – planning, budgeting, performance, asset, inventory, service levels, audit, etc. In 2012, even ‘commodity’ cloud vendors will finally take cloud management seriously, as enterprises and governments demand these disciplines – and smaller providers differentiate on service and security, not just price.

10. Virtualization Management Becomes Irrelevant

In January 2009 I predicted, “in 3-5 years … niche [Virtual System Management] vendors will no longer survive, as virtualization becomes a core part of the enterprise compute fabric.” Three years later this trend has definitely started, and will accelerate in 2012 as IT turns instead to hybrid IT management, recognizing that silos of standalone virtualization management is a costly and inefficient burden. Maybe 2012 is not the end of Virtualization Management, but it is going to be the start of the demise.

IDEAS is always good for a more technical POV on IT industry trends. Here is their list of trends to watch for in 2012:

At the beginning of the year, IDEAS analysts collected their thoughts on the key trends that are poised to play out across the IT industry over the next 12 months. The analysts identified several major topics that are likely to dominate the attention of IT managers throughout 2012, including the continued rise of cloud computing, the diversification of client devices, and the next stage in the evolution of server designs and storage systems. Some developments will be incremental and fairly predictable. Other developments could fundamentally change the course of the industry, as organizations adopt public cloud services for a growing number of workloads, and as users shift from traditional PCs to tablets and other mobile devices for more and more client applications.

In 2012, IDEAS predicts the following:

• Customers will become increasingly concerned with the possibility of lock-in from cloud service providers as those customers grapple with the decision of whether to embrace proprietary cloud solutions that deliver unique benefits, or adopt more open solutions that may have limitations.
• IT workers will start to discover an acute need to reassess their skills in order to reap the benefits of cloud computing; new skill needs may require workers to seek out training in new areas.
• Public cloud consolidation will continue as larger, better-financed companies cherry-pick the best smaller companies and push out the weaker start-ups.
• Vendors that enjoy strong visibility in the client device market (such as smartphones, mobile PCs and tablets) will be able to translate this presence into success in the area of back-end cloud services. This trend will cause traditional server-focused vendors, such as Oracle or IBM, to rethink their client strategies to try to pick up some of this more-consumer-oriented business.
• New x86 processors, coupled with a major push from Microsoft with the release of Windows 8, will help to rejuvenate interest in PCs by underscoring their importance for many critical computing tasks. Intel’s next processor will deliver a quantum leap in performance for the mobile systems that use them, including PC laptops and other devices, and its arrival could impact the mobile market to the same degree that Westmere and Nehalem impacted servers in 2011.
• Solid-state drives (SSDs) will be much more widely deployed, and in a variety of ways: on many clients, directly attached to servers, and within storage arrays. IT architects will start to rethink their data storage and caching approaches as they consider whether SSDs are more effective when deployed close to processors as large caches directly attached to servers, or as added tiers in storage devices. Systems vendors that can offer storage management software that spans both servers and storage arrays, thus addressing both uses cases for SSDs with an integrated solution, will break out from the pack.
• Vendors will step up efforts to simplify and automate storage management as they focus increasingly on emerging world markets (such as BRIC and CIVETS countries). In particular, automated storage tiering solutions, which arbitrate how classes of data are assigned to high-performance SSD pools, will extend into the small and midsize business (SMB) space.
• Unified storage, which manages block and file storage using a single interface, will become a common requirement for entry-level and midrange customers.
• IT infrastructures will continue to become standardized, with increasingly critical workloads deployed on x86 servers and industry-standard operating systems such as Linux and Windows. This standardization means systems vendors will find it harder than ever to establish meaningful differentiation in hardware; as a result, they will go further than ever before to vertically integrate their platforms with unique and proprietary software stacks that are specifically optimized for their server platforms.
• Software-defined networking (SDN) will start to get some attention in 2012, as developers find that they can achieve more deterministic network behavior, and hence higher performance, by integrating network switching directly with applications.

It’s not all about hardwae! Improvements in algorithms also drive computation.

In a review of linear programming solvers from 1987 to 2002, Bob Bixby says that solvers benefited as much from algorithm improvements as from Moore’s law.

Three orders of magnitude in machine speed and three orders of magnitude in algorithmic speed add up to six orders of magnitude in solving power. A model that might have taken a year to solve 10 years ago can now solve in less than 30 seconds.

Source: In Pursuit of the Traveling Salesman

An interesting discussion of computing, energy consumption, and how new architectures will enable exascale computing.

“Power is now the limiter of every computing platform, from cellphones to PCs and even data centres,” said NVIDIA chief executive Jen-Hsun Huang, speaking at the company’s GPU Technology Conference in Beijing last week. There was much talk there about the path to exascale, a form of supercomputing that can execute 10¹⁸ flop/s (Floating Point Operations per Second).

Currently, the world’s fastest supercomputer, Japan’s K computer, achieves 10 petaflops (one petaflop = a thousand trillion floating point operations per second), just 1 per cent of exascale. The K computer consumes 12.66MW (megawatts), and Huang suggests that a realistic limit for a supercomputer is 20MW, which is why achieving exascale is a matter of power efficiency as well as size. At the other end of the scale, power efficiency determines whether your smartphone or tablet will last the day without a recharge, making this a key issue for everyone.

Huang’s thesis is that the CPU, which is optimised for single-threaded execution, will not deliver the required efficiency. “With four cores, in order to execute an operation, a floating point add or a floating point multiply, 50 times more energy is dedicated to the scheduling of that operation than the operation itself,” he says.

“We believe the right approach is to use much more energy-efficient processors. Using much simpler processors and many of them, we can optimise for throughput. The unfortunate part is that this processor would no longer be good for single-threaded applications. By adding the two processors, the sequential code can run on the CPU, the parallel code can run on the GPU, and as a result you can get the benefit of the both. We call it heterogeneous computing.”

He would say that. NVIDIA makes GPUs after all. But the message is being heard in the supercomputing world, where 39 of the top 500 use GPUs, up from 17 a year ago, and including the number 2 supercomputer: Tianhe-1A in China. Thirty-five of those 39 GPUs are from NVIDIA.

At a mere 2.57 petaflops though, Tianhe-1A is well behind the K computer, which does not use GPUs. Does that undermine Huang’s thesis? “If you were to design the K computer with heterogeneous architecture, it would be even more,” he insists. “At the time the K computer was conceived, almost 10 years ago, heterogeneous was not very popular.”

Using GPUs for purposes other than driving a display is only practical because of changes made to the architecture to support general-purpose programming. NVIDIA’s system is called CUDA and is programmed using CUDA C/C++. The latest CUDA compiler is based on LLVM, which makes it easier to add support for other languages. In addition, the company has just announced that it will release the compiler source code to researchers and tool vendors. “It’s open source enough that anybody who would like to develop their target compiler can do it,” says Huang…

The distinction between driving a display and general-purpose programming is blurring. As game visuals become more advanced, more of the code is devoted to simulating real-world physics. “The combination of simulation and visualisation is going to transform how people enjoy games,” Huang says.

In the same way, designers and engineers with workstations can use GPU accelerators to render accurate simulations of their designs. NVIDIA Maximus uses two GPUs, one from its Tesla line for general purpose programming and the other a Quadro for the display. “Now the workstation is completely changed because it can combine the workflow of two parts of the design, the design part, and the simulation part,” claims Huang.

Huang is looking forward to Windows on ARM. He talks about the Asus Transformer tablet and its long battery life, and then says: “Imagine Windows on ARM on that device, and next-generation versions of that device. It’s a foregone conclusion that the PC industry will be revolutionised. I’m anxious to see Windows on ARM come to market and I think Microsoft is going to be very successful with it.”

There are a few clouds on NVIDIA’s horizon. One is that ARM, which dominates the world of mobile CPUs, is now also designing mobile GPUs, under the brand Mali. That could undermine NVIDIA’s Tegra business, a SoC (System on a Chip) which combines an ARM CPU with an NVIDIA GPU. Huang does his best to dismiss Mali as having only “basic capabilities”. He adds, “We have to continue to find our value-add, if we don’t then we don’t have a role in the world.”

Huang will not be drawn on the subject of Kepler, his company’s next generation GPU family, which seems to be delayed though only in a notional sense since no date has been announced.

There is also Intel to think about. Intel’s multi-core evangelist James Reinders says its forthcoming “Knights Corner” MIC (Many Integrated Core) processor will solve the efficiency issues Huang describes. “Knights Corner is superior to any general-purpose GPU type solution for two reasons,” Reinders tells us.

“We don’t have the extra power-sucking silicon wasted on graphics functionality when all we want to do is compute in a power efficient manner, and - second - we can dedicate our design to being highly programmable because we aren’t a GPU - we’re an x86 core, a Pentium-like core for “in order” power efficiency - every algorithm that can run on GPGPUs will certainly be able to run on a MIC co-processor.

“MIC used to be a GPU,” says Huang when asked about Intel’s co-processor. “MIC is Larrabee 3, and Larrabee 1 was a GPU. So there is no difference, except of course that we care very much about GPU computing, and we believe this is going to be the way that high performance computing is performed.”

The Register discusses some of the management issues that stem from virtualization. Good explanation of the types of pain-points that IT managers face everyday.

Virtualising systems often means scaling them up. Sometimes, disparate networks of machines are consolidated together, creating a mega-portfolio of assets. This carries a special set of technical challenges, but let’s not forget the managerial ones.

What happens when you scale a system by virtualising servers and cramming more of them onto more powerful hardware?

Your IT team finds itself having to manage a bigger volume of virtual machines, with all of the associated networking, performance and maintenance issues that it entails.

“If you are scaling and making things that you have to manage bigger, you will be stressing the processes and the individuals that do the work,” says Phil Everson, technology partner at Deloitte.

“It’s not just the case that you will need more analysts to cope. It may be that you can’t manage it in the same way any more.”

Human contact

What the head of storage architecture was able to do in a relatively small-scale system may change when that architecture suddenly covers several networks in different geographies.

If you have 50 people handling storage management across three data centres on two continents, it is not just the technological challenges that become more complex.

“Demand for scaling up from the business requires a much more mature bilateral set of communications,” Everson says.

“People have been trying and getting a bit better at it over the past decade, but it’s still an enormous challenge.”

This focus on solid, clear agreements with the business takes more rigour than you might think.

In the frame

“Does an agreement mean that it has been approved, or accepted? Does that mean there’s budget to do it? That there are people actively working on it and committed delivery dates?” Everson asks.

“Often, it doesn’t, and both sides miscommunicate as a result.”

Simple techniques, such as producing a register of all the projects currently being delivered in IT, can help here. Such a register could include a description of all the milestones planned for the next 100 days.

Management frameworks such as ITIL or Capability Model Maturity Integration can help organisations to manage infrastructures by refining processess. However, they are not a substitute for good IT management.

“They are an approach that drives the right nomenclature to enable good management. They don’t ensure that you have it, but they can help,” Everson says.

The same goes for automation, according to Brian Murray, principal consultant at IT services company 2E2. Manual processes are costly to implement, and as a system scales up staff become overworked and the cost increases.

Automation of manual processes can be a huge help, reducing error and cost. Processes ripe for automation include capacity management and automated storage tiering.

However, automation can backfire if these processes are detrimental to your IT management environment in the first place.

“If you are automating a process that is clunky and slow, then all you end up with is more of these problems, more quickly,” Murray says.

He highlights one process that can often be malformed from the start: patching and provisioning. This can often be done without proper patch testing, or it can be so bogged down in bureaucracy that it takes far too long to complete.

“If you look at how people make change management processes, mistakes are made, things are out of date, and it reduces confidence in that process,” Murray warns.

Getting some of the basic tasks right can give you a solid foundation on which to automate other processes. Configuration management can be mired in bad process, but if you get it right, and automate it effectively, it opens the door to a lot of benefits.

Automatically updating your configuration management database paves the way for more effective infrastructure management.

Blame game

The management problem is likely to become less tractable as we move further towards public cloud-based services, many of which may be mixed and matched from various vendors.

“When people moved to more modular managed services from large outsourcing deals, they had more contracts to manage,” says Everson. “That led to more finger-pointing when things went wrong.”

He believes we will face the same problems when we move to cloud-based services unless we have a solid management framework in place.

Overall, a focus on proper management at the outset as companies scale up their computing infrastructures will save a lot of headaches later.

RedMonk weighs in on IBM’s recent move to open source the MQTT lightweight messaging protocol. As you’d expect, they’re very much in favor and celebrate IBM’s decision.

The last time I wrote about a World Made of Messages was back in 2010, when SpringSource announced it was to acquire RabbitMQ. A lot has happened in the the meantime- but things are really heating up.

Logmein recently acquired Pachube, and then made the end point to end point real time web message broker free to use this week. Making something free is a great way to lower barriers to participation.

“As devices continue to find their way onto the Internet, we want them to be able to take advantage of everything the Web has to offer. We want Pachube users to control their own data, build applications that we would never envision, and share with others as they see fit.”

Another way to lower barriers to participation is to open source code, which is what IBM just announced: it has released Java and c versions of its MQTT clients as an Eclipse proposal under the name Paho.

The MQTT specification for machine to machine, message-based comms was already publicly available, but making the code open source is a whole different ballgame; that’s the big news.

IBM contributes plenty of code to projects like the Apache Web Server and Linux. But in many respects I see this latest drop as IBM’s most significant since it open sourced Eclipse ten years ago. Why? Because the Eclipse Public License is designed to support derivative works and embedding, while the Eclipse Foundation can provide the stewardship of same. One of the main reasons Eclipse has been so successful is that rather than separate software from specification it brings them together – in freely available open source code – while still allowing for proprietary extensions which vendors can sell.

Rather than talking about machine to machine, we could just describe MQTT as a protocol for lightweight messaging. As I described it in the RabbitMQ post mentioned above:

“Web developers tend to scoff at transaction management, but messaging has a really broad applicability – particularly in the cloud world. How are we going to deliver cloud interoperability? If you think the answer is Web Standards think again- remember WS-*? Messaging however offers the opportunity to tie disparate systems together with point to point interactions or indeed other integration patterns.”

At this point I should probably explain that I didn’t think IBM was going to make this move. I have pushed them hard internally to do so, but it still caught me by surprise. As I commented in April 2010:

“definitely need message oriented middleware [MOM] interop, and the simplicity of more RESTful methods. WMQ may have a role to play in the “other spaces” – but IBM isn’t set up to pursue these opportunities. The barriers to entry to WMQ are significant, and IBM’s engagement models isn’t set up to address them. The WebSphere business has little or no interest in pervasive adoption, whatever IBM rhetoric may have been over the years. Rabbit is just filling the vacuum that nature abhors. Same as virtualisation – if IBM won’t address the mass market with its invention, then someone else bloody will. Is Rabbit the VMware of messaging? Could be, right?”

At this point, Andy Piper is surely entitled to say “In Your Face, Governor!”

At this point I should talk to IBM and how it got here, and the huge credit deserved by its Hursley Development Labs team, and the execs that let them pull the trigger. You see – the MQTT open source move is a bottom up phenomenon. Andy Stanford Clark has been on the lightweight messaging tip most of his career but its only in the last couple of years he has really nailed it, co-innovating with local software developers, rather than IBM’s usual multi-billion dollar partnerships. Andy got his house to tweet, building a better mousetrap. He was a huge supporter of HomeCamp, a home automation community event – about ten IBMers came to the first one. Suddenly local UK web hackers like my good friend Chris Dalby were targeting IBM platforms, sending data to an IBM message broker. This was weird and awesome.

Meanwhile IBM was beginning to invest in more local grassroots evangelism – with great people like Zoe Slattery (sadly now retired from IBM), Simon Maple and the aforementioned social bridgebuilder Andy Piper. Hursley managers that allowed all this innovation to flourish under their watch are John McLean and Gerry Reilly.

Given the open sourcing decision happened under the watch of WebSphere/AIM SVP Marie Wieck she deserves credit too. Are we going to see IBM WebSphereMQ open sourced any time soon? Probably not- but there again it just became more likely.

So that’s just a few of the people involved – the key point being that the decision making here seemed as much as Social process as a technical, marketing or sales decision. IBM open sourcing MQTT software is Social Business, with developers being the constituency in question. More messages, and more active endpoints, will also mean a lot more data – Big Data – which IBM can turn into money in plenty of ways.

Internet of Things innovation is not just going to happen at huge traditional IBM customers. Indeed Software is Eating the World.

Talking of eating the world, if you’re wondering if any web companies you’ve ever heard of are interested in lightweight messaging – how about Facebook, which is using the MQTT protocol for Facebook Messenger? As architect Luzy Zhang explains:

“One of the problems we experienced was long latency when sending a message. The method we were using to send was reliable but slow, and there were limitations on how much we could improve it. With just a few weeks until launch, we ended up building a new mechanism that maintains a persistent connection to our servers. To do this without killing battery life, we used a protocol called MQTT that we had experimented with in Beluga. MQTT is specifically designed for applications like sending telemetry data to and from space probes, so it is designed to use bandwidth and batteries sparingly. By maintaining an MQTT connection and routing messages through our chat pipeline, we were able to often achieve phone-to-phone delivery in the hundreds of milliseconds, rather than multiple seconds.”

So IBM is open sourcing technology which directly addresses current megatrends such as Big Data, Internet of Things, and even Social Media. This is a different kind of innovation by IBM, targeting the grassroots developer. Good job. Will MQTT and its Paho implementation prove to be as influential as Eclipse? Perhaps not- but Eclipse commoditised, while in this case IBM’s incentive is to innovate for growth.

Ideas International looks at Oracle’s high-end cloud play and at the overarching market for premium, workload-optimized systems.

When Oracle released Exalogic a few years ago, it was billed as a private cloud-in-a-box. At OpenWorld, Oracle doubled down on the plug-and-play features of Exalogic and Exadata by having these systems serve as the backbone for its new public cloud offering. Recently, IDEAS published a blog post offering a broader definition of mainframes as an integrated solution stack. By this definition, Oracle is eschewing commodity solutions, the architecture on which many existing cloud services are based, in favor of a high-end solution as the basis for its public cloud (see graphic below). Computing as a service has been successfully monetized (recently confirmed), but by taking the high-end approach, Oracle is trading larger potential sales volumes for a niche customer base. The strategy may prove to be as profitable, but it is not without risk.

The above taxonomy defines three types of server platform solutions, but in truth server architecture today is a spectrum along which we highlight three key points. On the one end is the commodity solution. This is the setup that is typical in most data centers for cloud-based workloads. Somewhere in the middle, further integration of hardware and software can be applied. Such converged server platforms, which include HP Matrix and Cisco UCS, have been a hot topic in recent years.

On the opposite end, a solution integrates software and hardware into a single smoothly functioning entity. Such a high-end solution is what Oracle plans to sell as its public cloud, incorporating Oracle’s Exadata and Exalogic platforms with Fusion software and other application layers. At first glance, this may seem unexpected, but as long ago as 1999 commentators were noting that Larry Ellison’s preference for a high-end solution stack was pretty clear:

“Oracle chairman CEO Larry Ellison is taking aim at Microsoft’s core enterprise strategy, mounting an attack on client/server computing, which he describes as an evolutionary dead end, and more specifically taking a pop at Microsoft’s ‘servers everywhere’ distributed computing model. …x86 server isn’t yet sufficiently scalable to rival centralised computing models based on Unix, mid-range and mainframe models… and that’s why Ellison is finally attacking the right target…distributed computing tends towards the chaotic and expensive.”

For Oracle, the verdict on how to best package hardware was in long ago. In the meantime, of course, Microsoft strategy did not fail. But mainframes did not fail either. Cassandra predictions by others about the dim future of the mainframe have been overzealous. IBM still sells plenty of big iron, and the mainframe ecosystem remains a solid, albeit cyclical, business.

A true metric to determine the winner of the commodity vs. high-end solution contest would normalize the amount of compute cycles (or I/O) that are now being delivered by high-end solutions vs. total compute cycles (or I/O). By that measure, high-end solutions have been losing ground quickly over the years to commodity solutions.

High-end systems with tightly integrated software have traditionally been applied for very specific tasks, such as:

• High-performance computing (HPC), which employed specialized hardware designs in the past, but which is now increasingly based on commodity hardware.
• Business-critical systems (in many cases, but not all).
• Code that needs to have an edge to beat competitors. An example of this would be hedge funds that pay big money to experts in multithreaded and parallel programming.

Android apps are an example of a commodity application layer at the low end, in consumer technology. Android applications have to run on many different chips, OS flavors, and hardware. Therefore, investments in optimizing an Android app may be a losing proposition for developers, because that app could be buggy or useless on nonoptimized devices.

On the high end, investment bank hedge funds may hire programmers to make sure that each instruction is matched to a thread of a specific Intel processor (Intel even has a special suite of tools to help enable this degree of optimization). Granted, the hedge fund programmer is working on a x86 platform. However, not everyone can afford a team of high-priced experts. The next best thing may be the high-end solution, in which software has been preintegrated and optimized for a specific hardware platform.

Most public cloud vendors build their services on commodity hardware, because part of their added value is the development of a software stack that provides meaningful differentiation from competitors. This differentiation is essential for avoiding true commoditization, in which the cloud service competes on nothing other than price. There may be a few exceptions where a vendor has some more exotic hardware offerings in the mix, but the majority are built on x86 servers, together with a hodgepodge of applications. This environment lends itself well to being partitioned into compute instances that are sold cheaply.

However, in finally embracing public cloud computing, Oracle faces a dilemma. On the one hand, it cannot afford to cannibalize its own margins on Exadata and Exalogic hardware, Fusion software, its enterprise database, and so forth to customers for on-premise deployment. Therefore, its public cloud offering cannot be too cheap in absolute terms. On the other hand, its cloud offering needs to promise customers genuine benefits in terms of price-performance and other metrics. These two forces are driving much of the risk in Oracle’s business maneuver – will end users choose to deploy Oracle high-end server platform solutions on premises, or purchase access to these systems from Oracle’s public cloud on a utility basis?

Oracle’s databases lead in market share, and Oracle’s middleware sales are second only to IBM’s. Big corporations, especially financial institutions, have traditionally been loyal customers of premium architectures. By accepting that not every startup is going to want to use an Exa-based cloud with an expensive relational database management system, Oracle is forsaking low margins and high volumes in favor of pursuing opportunities with high-end enterprise customers. Oracle’s approach is also a novel way to sell the Exadata and Exalogic to new clients, who run plenty of high-end solutions, but not necessarily on those two systems.

Oracle’s high-end cloud solutions may appeal to fewer customers than other public cloud platforms, and the analyst community may not fully understand the move. However, there is little doubt what the company is trying to achieve: as Ellison seemed to be predicting in 1999, Oracle will try to capture the enterprise with its own centralized, integrated hardware architecture and applications stack – deployed either on premises, or off.