Big data, it’s the buzz word of the year and it’s generating a lot of attention. An incalculable number of articles fervently repeat the words “variety, velocity and volume,” citing click streams, RFID tags, email, surveillance cameras, Twitter® feeds, Facebook® posts, Flickr® images, blog musings, YouTube® videos, cellular texting, healthcare monitoring …. (gasps for air). We have become a society that sweats buckets of data every day (the latest estimates are approximately 34GB per person every 24 hours) and businesses are scrambling to capture all this information to learn more about us.

Save every scrap of data!
“Save all your data” has become the new business mantra, because data – no matter how seemingly meaningless it appears – contains information, and information provides insight, and improved insight makes for better decision-making, and better decision-making leads to a more efficient and profitable business.

Okay, so we get why we save data, but if the electronic bit bucket costs become prohibitive, big data could turn into its own worst enemy, undermining the value of mining data.  While Hadoop® software is an excellent (and cost-free) tool for storing and analyzing data, most organizations use a multitude of applications in conjunction with Hadoop to create a system for data ingest, analytics, data cleansing and record management. Several Hadoop vendors (Cloudera, MapR, Hortonworks, Intel, IBM, Pivotal) offer bundled software packages that ease integration and installation of these applications.

Installing a Hadoop cluster to manage big data can be a chore
With the demand for data scientists growing, the challenge can become finding the right talent to help build and manage a big data infrastructure.  A case in point: Installing a Hadoop cluster involves more than just installing the Hadoop software. Here is the sequence of steps:

  1. Install the hardware, disks, cables.
  2. Install the operating system.
  3. Optimize the file system and operating system (OS) parameters (i.e. open file limits, virtual memory).
  4. Configure and optimize the network and switches.
  5. Plan node management (for Hadoop 1.x this would be Namenode, Secondary Namenode, JobTracker, ZooKeeper, etc.).
  6. Install Hadoop across all the nodes. Configure each node according to its planned role.
  7. Configure high availability (HA) (when required).
  8. Configure security (i.e. Kerberos, Secure Shell [ssh]).
  9. Apply optimizations (I have several years’ experience in Hadoop optimization, so can say with some authority that this is not a job to be taken lightly. The benefits of a well-optimized cluster are incredible, but it can be a challenge to balance the resources correctly without adding undo system pressure elsewhere.)
  10. Install and integrate additional software and connectors (i.e. to connect to data warehousing system, input streams or database management system [DBMS] servers).
  11. Test the system.

Setup, from bare bones to a simple 15-node cluster, can take weeks to months including planning, research, installation and integration. It’s no small job.

Appliances simplify Hadoop cluster deployments
Enter appliances: low-cost, pre-validated, easy-to-deploy “bricks.” According to a Gartner forecast (Forecast: Data Center Hardware Spending to Support Big Data Projects, Worldwide 2013), appliance spending for big data projects will grow from 0.9% of hardware spending in 2012 to 9.3% by 2017. I have found myself inside a swirl of new big data appliance projects all designed to provide highly integrated systems with easy support and fully tested integration. An appliance is a great turnkey solution for companies that can’t (or don’t wish to) employ a hardware and software installation team: Simply pick up the box from the shipping area, unpack it and start analyzing data within minutes. In addition, many companies are just beginning to dabble in Hadoop, and appliances can be an easy, cost-effective way to demonstrate the value of Hadoop before making a larger investment.

While Hadoop is commonplace in the big data infrastructure, the use models can be quite varied. I’ve heard my fair share of highly connected big data engineers attempt to identify core categories for Hadoop deployments, and they generally fall into one of four categories:

  1. Business intelligence, querying, reporting, searching – such as filtering, indexing, trend analysis, search optimization – and good old-fashioned information retrieval.
  2. Higher performance for common data management operations including log storage, data storage and archiving, extraction/transform loading (ETL) processing and data conversions.
  3. Non database applications such as image processing, data sequencing, web crawling and workflow processing.
  4. Data mining and analytical applications including social network/sentiment analysis, profile matching, machine learning, personalization and recommendation analysis, ad optimization and behavioral analysis.

Finding the right appliance for you
While appliances lower the barrier to entry to Hadoop clusters, their designs and costs are as varied as their use cases.  Some appliances build in the flexibility of cloud services, while others focus on integration of applications components and reducing service level agreements (SLAs). Still others focus primarily on low cost storage. And while some appliances are just hardware (although they are validated designs), they still require a separate software agreement and installation via a third-party vendor.

In general, pricing is usually quoted either by capacity ($/TB), or per node or rack depending on the vendor and product. Licensing can significantly increase overall costs, with annual maintenance costs (software subscription and support) and license renewals adding to the cost of doing business. The good news is that, with so many appliances to choose from, any organization can find one that enables it to design a cluster that fits its budget, operating costs and value expectations.

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Emerging and disruptive markets are hard to quantify and forecast: They often apply different marketing labels for the same thing, and have no baseline industry data and no consistent methods of measurement and forecasting.

But this recent Wibikon big data report is head and hands above others. This is the third edition of the report and I wanted to give a shout-out to the authors – Jeff Kelly, David Vellante and David Foyer – on this best-in-class body of work.

Behind the numbers: The way I see it, big data has two different markets with very different technology and investment requirements and pace of adoption:

  • Consumer big data (web-scale Yahoo , Facebook and the like)
  • Enterprise big data for business and industry (banking, healthcare, government, manufacturing, retail)

And now, the color commentary on the Wikibon big data report …

  • $18.8B – 2013 total big data revenue … the 2012 number was $12B. It’s growing fast.
  • $1.4B  IBM is biggest player, and this total includes hardware, software and consulting … no real surprise to me here.
  • $3.8B  Original design manufacturer (ODM) penetration marks a substantial move away from branded servers. In my opinion, this is mostly for big data consumer market.
  • $1.5B  Enterprise hardware Dell, IBM, HP, others … indicates growth for big data in business and industry (in the enterprise segment).
  • $415M, $312M, $305M  Accenture, PWC and Deloitte, respectively … substantial investment serving big data analytics for business and industry.

I’ve discussed the notion of two different markets, consumer big data and enterprise big data, with dozens of my friends, associates and industry co-travelers. And consistently their response is “yes, we see it the same way. There are two very different big data markets:  one for web scale, like Yahoo!, and another for business and industry, like Walmart and GE.” My friends at Rackspace introduced me to the terms consumer big data and enterprise big data … make sense to me.

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It’s the start of the new year, and it’s traditional to make predictions – right? But predicting the future of the datacenter has been hard lately. There have been and continue to be so many changes in flight that possibilities spin off in different directions. Fractured visions through a kaleidoscope. Changes are happening in the businesses behind datacenters, the scale, the tasks and what is possible to accomplish, the value being monetized, and the architectures and technologies to enable all of these.

A few months ago I was asked to describe the datacenter in 2020 for some product planning purposes. Dave Vellante of Wikibon & John Furrier of SiliconANGLE asked me a similar question a few weeks ago. 2020 is out there – almost 7 years. It’s not easy to look into the crystal ball that far and figure out what the world will look like then, especially when we are in the midst of those tremendous changes. For some context I had to think back 7 years – what was the datacenter like then, and how profound have the changes been over the past 7 years?

And 7 years ago, our forefathers…
It was a very different world. Facebook barely existed, and had just barely passed the “university only” membership. Google was using Velcro, Amazon didn’t have its services, cloud was a non-existent term. In fact DAS (direct attach storage) was on the decline because everyone was moving to SAN/NAS. 10GE networking was in the future (1GE was still in growth mode). Linux was not nearly as widely accepted in enterprise – Amazon was in the vanguard of making it usable at scale (with Werner Vogels saying “it’s terrible, but it’s free, as in free beer”). Servers were individual – no “PODs,” and VMware was not standard practice yet. SATA drives were nowhere in datacenters.

An enterprise disk drive topped out at around 200GB in capacity. Nobody used the term petabyte. People, including me, were just starting to think about flash in datacenters, and it was several years later that solutions became available. Big data did not even exist. Not as a term or as a technology, definitely not Hadoop or graph search. In fact, Google’s seminal paper on MapReduce had just been published, and it would become the inspiration for Hadoop – something that would take many years before Yahoo picked it up and helped make it real.

Analytics were statistical and slow, and you had to be very explicitly looking for something. Advertising on the web was a modest business. Cold storage was tape or MAID, not vast pools of cheap disks in the cloud at absurdly low price points. None of the Chinese web-cloud guys existed… In truth, at LSI we had not even started looking at or getting to know the web datacenter guys. We assumed they just bought from OEMs…

No one streamed mainstream media – TV and movies – and there were no tablets to stream them to. YouTube had just been purchased by Google. Blu-ray was just getting started and competing with HD-DVD (which I foolishly bought 7 years ago), and integrated GPS’s in your car were a high-tech growth area. The iPhone or Android had not launched, Danger’s Sidekick was the cool phone, flip phones were mainstream, there was no App store or the billions of sales associated with that, and a mobile web browser was virtually useless.

Dell, IBM, and HP were the only real server companies that mattered, and the whole industry revolved around them, as well as EMC and NetApp for storage. Cisco, Lenovo and Huawei were not server vendors. And Sun was still Sun.

7 years from now
So – 7 years from now? That’s hard to predict, so take this with a grain of salt… There are many ways things could play out, especially when global legal, privacy, energy, hazardous waste recycling, and data retention requirements come into play, not to mention random chaos and invention along the way.

Compute-centric to dataflow-centric
Major applications are changing (have changed) from compute-centric to dataflow architectures. That is big data. The result will probably be a decline in the influence of processor vendors, and the increased focus on storage, network and memory, and optimized rack-level architectures. A handful of hyperscale datacenters are leading the way, and dragging the rest of us along. These types of solutions are already being deployed in big enterprise for specialized use cases, and their adoption will only increase with time. In 7 years, the main deployment model will echo what hyperscale datacenters are doing today: disaggregated racks of compute, memory and storage resources.

The datacenter is now being viewed as a profit growth enabler, rather than a cost center. That implies more compute = more revenue. That changes the investment profile and the expectations for IT. It will not be enough for enterprise IT departments to minimize change and risk because then they would be slowing revenue growth.

Customers and vendors
We are in the early stages of a customer revolt. Whether it’s deserved or not is immaterial, though I believe it’s partially deserved. Large customers have decided (and I’m doing broad brush strokes here) that OEMs are charging them too much and adding “features” that add no value and burn power, that the service contracts are excessively expensive and that there is very poor management interoperability among OEM offerings – on purpose to maintain vendor lockin. The cost structures of public cloud platforms like Amazon are proof there is some merit to the argument. Management tools don’t scale well, and require a lot of admin intervention. ISVs are seen as no better. Sure the platforms and apps are valuable and critical, but they’re really expensive too, and in a few cases, open source solutions actually scale better (though ISVs are catching up quickly).

The result? We’re seeing a push to use whitebox solutions that are interoperable and simple. Open source solutions – both software and hardware – are gaining traction in spite of their problems. Just witness the latest Open Compute Summit and the adoption rate of Hadoop and OpenStack. In fact many large enterprises have a policy that’s pretty much – any new application needs to be written for open source platforms on scale-out infrastructure.

Those 3 OEMs are struggling. Dell, HP and IBM are selling more servers, but at a lower revenue. Or in the case of IBM – selling the business. They are trying to upsell storage systems to offset those lost margins, and they are trying to innovate and vertically integrate to compensate for the changes. In contrast we’re seeing a rapid increase planned from self-built, self-architected hyperscale datacenters, especially in China. To be fair – those pressures on price and supplier revenue are not necessarily good for our industry. As well, there are newer entrants like Huawei and Cisco taking a noticeable chunk of the market, as well as an impending growth of ISV and 3rd party full rack “shrink wrapped” systems. Everybody is joining the party.

Storage, cold storage and storage-class memory
Stepping further out on the limb, I believe (but who really knows) that by 2020 storage as we know is no longer shipping. SMB is hollowed out to the cloud – that is – why would any small business use anything but cloud services? The costs are too compelling. Cloud storage is stratified into 3 levels: storage-class memory, flash/NVM and cool/cold bulk disk storage. Cold storage is going to be a very, very important area. You need to save that data, but spend zero power, and zero $ on storing it. Just look at some of the radical ideas like Facebook’s Blu-ray jukebox to address that, which was masterminded by a guy I really like –  Gio Coglitore – and I am very glad is getting some rightful attention. (http://www.wired.com/wiredenterprise/2014/02/facebook-robots/)

I believe that pooled storage class memory is inevitable and will disrupt high-performance flash storage, probably beginning in 2016. My processor architect friends and I have been daydreaming about this since 2005. That disruption’s OK, because flash use will continue to grow, even as disk use grows. There is just too much data. I’ve seen one massive vendor’s data showing average servers are adding something like 0.2 hard disks per year and 0.1 SSDs per year – and that’s for the average server including diskless nodes that are usually the most common in hyperscale datacenters. So growth in spite of disruption and capacity growth.

Key/value:
Data will be pooled, and connected by fabric as distributed objects or key/value pairs, with erasure coding. In fact, Object store (key/value – whatever) may have “obsoleted” block storage. And the need for these larger objects will probably also obsolete file as we’re used to it. Sure disk drives may still be block based, though key/value gives rise to all sorts of interesting opportunities to support variable size structures, obscure small fault domains, and variable encryption/compression without wasting space on disk platters. I even suspect that disk drives as we know them will be morphing into cold store specialty products that physically look entirely different and are made from different materials – for a lot of reasons. 15K drives will be history, and 10K drives may too. In fact 2” drives may not make sense anymore as the laptop drive and 15K drive disappear and performance and density are satisfied by flash.

Enterprise becomes private cloud that is very similar structurally to hyperscale, but is simply in an internal facility. And SAN/NAS products as we know them will be starting on the long end of the tail as legacy support products. Sure new network based storage models are about to emerge, but they’re different and more aligned to key/value.

SoC servers
Rack-scale architectures will have taken over clustered deployments. That means pooled resources. Processing will be pools of single socket SoC servers enabling massive clusters, rather than lots of 2- socket servers. These SoCs might even be mobile device SoCs at some point or at least derived from that – the economics of scale and fast cadence of consumer SoCs will make that interesting, maybe even inevitable. After all, the current Apple A7 in the iphone 5S is a dual core, 64-bit V8 ARM at 1.4GHz and the whole iPhone costs as much as mainstream server processor chips. In a few years, an 8 or 16 core equivalent at 1.5GHz or 2GHz is not hard to imagine, and the cost structure should be excellent.

Rapidly evolving open source applications will have morphed into eventually consistent dataflow tasks. Or they will be emerging in-memory applications working on vast data structures in the pooled storage class memory at the rack or larger scale, which will add tremendous monetary value to businesses. Whatever the evolutionary paths – the challenge for the next 10 years is optimizing dataflow as the amount used continues to exponentially grow. After all – data has value in aggregate, so why would you throw anything away, even as the amount we generate increases?

Clusters will be autonomous. Really autonomous. As in a new term I love: “emergent.” It’s when you can start using big data analytics to monitor the datacenter, and make workload/management and data placement decisions in real time, automatically, and the datacenter begins to take on un-predicted characteristics. Deployment will be autonomous too. Power on a pod of resources, and it just starts working. Google does that already.

The network
Layer 2 datacenter network switches will either be disappearing or will have migrated to a radically different location in the rack hierarchy. There are many ways this can evolve. I’m not sure which one(s) will dominate, but I know it will look different. And it will have different bandwidth. 100G moving to 400G interconnect fabric over fiber.

So there you have it. Guaranteed correct…

Different applications and dataflow, different architectures, different processors, different storage, different fabrics. Probably even a re-alignment of vendors.

Predicting the future of the datacenter has not been easy. There have been, and are so many changes happening. The businesses behind them. The scale, the tasks and what is possible to accomplish, the value being monetized, and the architectures and technologies to enable all of these. But at least we have some idea what’s ahead. And it’s pretty different, and exciting.

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Last week at LSI’s annual Accelerating Innovation Summit (AIS) the company took the wraps off a vision that should lead its technical direction for the next few years.

The LSI keynote featured a video of three situations as they might evolve in the future:

  • A man falls from a bicycle in a foreign country and needs medical attention
  • A bullet train stops before hitting a tree that fell across its tracks
  • A hacker is prevented from accessing secure information using identity theft

I’ll focus on just one of these to show how LSI expects the future to develop.  In the bicycle accident scenario, a businessman falls to the ground while riding a bicycle in a foreign country.  Security cameras that have been upgraded to understand what they see notify an emergency services agency which sends an ambulance to the scene.  The paramedic performs a retinal scan on the victim, using it to retrieve his medical records, including his DNA sequence, from the web.

The businessman’s wearable body monitoring system also communicates with the paramedic’s instruments to share his vital signs.  All of this information is used by cloud-based computers to determine a course of action which, in the video, requires an injection that has been custom-tuned to the victim’s current situation, his medical history, and his genetic makeup.

That’s a pretty tall order, and it will require several advances in the state of the art, but LSI is using this and other scenarios to work with its clients and translate this vision into the products of the future.

What are the key requirements to make this happen? Talwalkar told the audience that we need to create a society that is supported by preventive, predictive and assisted analytics to move in a direction where the general welfare is assisted by all that the Internet and advanced computing have to offer.  Since data is growing at an exponential rate, he argued that this will require the instant retrieval of interlinked data objects at scale. Everything that is key to solving the task must be immediately available, and must be quickly analyzed to provide a solution to the problem at hand. The key will be the ability to process interlinked pieces of data that have not been previously structured to handle any particular situation.

To achieve this we will need larger-scale computing resources than are currently available, all closely interconnected, that all operate at very high speeds.  LSI hopes to tap into these needs through its strengths in networking and communications chips for the communications, its HDD and server and storage connectivity array chips and boards for large-scale data, and its flash controller memory and PCIe SSD expertise for high performance.

LSI brought to AIS several of the customers and partners it is working with using to develop these technologies. Speakers from Intel, Microsoft, IBM, Toshiba, Ericsson and others showed how they are working with LSI’s various technologies to improve the performance of their own systems.  On the exhibition floor booths from LSI and many of its clients demonstrated new technologies that performed everything from high-speed stock market analysis to fast flash management.

It’s pretty exciting to see a company that has a clear vision of its future and is committed to moving its entire ecosystem ahead to make that happen and help companies manage their business more effectively during what LSI calls the “Datacentric Era.” LSI has certainly put a lot of effort into creating a vision and determining where its talents can be brought to bear to improve our lives in the future.

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Scaling compute power and storage in space-constrained datacenters is one of the top IT challenges of our time. With datacenters worldwide pressed to maximize both within the same floor space, the central challenge is increasing density.

At IBM we continue to design products that help businesses meet their most pressing IT requirements, whether it’s optimizing data analytics, data management, the fastest growing workloads such as social media and cloud delivery or, of course, increasing compute and storage density. Our technology partners are a crucial part of our work, and this week at AIS we are teaming with LSI to showcase our new high-density NeXtScale computing platform and x3650 M4 HD server. Both leverage LSI® SAS RAID controllers for data protection, and the x3650 M4 HD server features an integrated leading-edge LSI 12Gb/s SAS RAID controller.

IBM NeXtScale System

NeXtScale System – ideal for HPC, cloud service providers and Web 2.0
The NeXtScale System®, an economical addition to the IBM System® family, maximizes usable compute density by packing up to 84 x86-based systems and 2,016 processing cores into a standard 19-inch rack to enable seamless integration into existing infrastructures. The family also enables organizations of all sizes and budgets to start small and scale rapidly for growth. The NeXtScale System is an ideal high-density solution for high-performance computing (HPC), cloud service providers and Web 2.0.

IBM System x3650 M4 HD

The System x3650 M4 HD, IBM’s newest high-density storage server, is designed for data-intensive analytics or business-critical workloads. The 2U rack server supports up to 62% more drive bays than the System x3650 M4 platform, providing connections for up to 26 2.5-inch HDDs or SSDs. The server is powered by the Intel Xeon processor E5-2600 family and features up to 6 PCIe 3.0 slots and an onboard LSI 12Gb/s SAS RAID controller. This combination gives a big boost to data applications and cloud deployments by increasing the processing power, performance and data protection that are the lifeblood of these environments.

IBM dense storage solutions to help drive data management, cloud computing and big data strategies
Cloud computing and big data will continue to have a tremendous impact on the IT infrastructure and create data management challenges for businesses. At IBM, we think holistically about the needs of our customers and believe that our new line of dense storage solutions will help them design, develop and execute on their data management, cloud computing and big data strategies.

 

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You may have noticed I’m interested in Open Compute. What you may not know is I’m also really interested in OpenStack. You’re either wondering what the heck I’m talking about or nodding your head. I think these two movements are co-dependent. Sure they can and will exist independently, but I think the success of each is tied to the other.  In other words, I think they are two sides of the same coin.

Why is this on my mind? Well – I’m the lucky guy who gets to moderate a panel at LSI’s AIS conference, with the COO of Open Compute, and the founder of OpenStack. More on that later. First, I guess I should describe my view of the two. The people running these open-source efforts probably have a different view. We’ll find that out during the panel.

I view Open Compute as the very first viable open-source hardware initiative that general business will be able to use. It’s not just about saving money for rack-scale deployments. It’s about having interoperable, multi-source systems that have known, customer-malleable – even completely customized and unique – characteristics including management.  It also promises to reduce OpEx costs.

Ready for Prime Time?
But the truth is Open Compute is not ready for prime time yet. Facebook developed almost all the designs for its own use and gifted them to Open Compute, and they are mostly one or two generations old. And somewhere between 2,000 and 10,000 Open Compute servers have shipped. That’s all. But, it’s a start.

More importantly though, it’s still just hardware. There is still a need to deploy and manage the hardware, as well as distribute tasks, and load balance a cluster of Open Compute infrastructure. That’s a very specialized capability, and there really aren’t that many people who can do that. And the hardware is so bare bones – with specialized enclosures, cooling, etc – that it’s pretty hard to deploy small amounts. You really want to deploy at scale – thousands. If you’re deploying a few servers, Open Compute probably isn’t for you for quite some time.

I view OpenStack in a similar way. It’s also not ready for prime time. OpenStack is an orchestration layer for the datacenter. You hear about the “software defined datacenter.” Well, this is it – at least one version. It pools the resources (compute, object and block storage, network, and memory at some time in the future), presents them, allows them to be managed in a semi-automatic way, and automates deployment of tasks on the scaled infrastructure. Sure there are some large-scale deployments. But it’s still pretty tough to deploy at large scale. That’s because it needs to be tuned and tailored to specific hardware. In fact, the biggest datacenters in the world mostly use their own orchestration layer.  So that means today OpenStack is really better at smaller deployments, like 50, 100 or 200 server nodes.

The synergy – 2 sides of the same coin
You’ll probably start to see the synergy. Open Compute needs management and deployment. OpenStack prefers known homogenous hardware or else it’s not so easy to deploy. So there is a natural synergy between the two. It’s interesting too that some individuals are working on both… Ultimately, the two Open initiatives will meet in the big, but not-too-big (many hundreds to small thousands of servers) deployments in the next few years.

Ecosystem
And then of course there is the complexity of the interaction of for-profit companies and open-source designs and distributions. Companies are trying to add to the open standards. Sometimes to the betterment of standards, but sometimes in irrelevant ways. Several OEMs are jumping in to mature and support OpenStack. And many ODMs are working to make Open Compute more mature. And some companies are trying to accelerate the maturity and adoption of the technologies in pre-configured solutions or appliances. What’s even more interesting are the large customers – guys like Wall Street banks – that are working to make them both useful for deployment at scale. These won’t be the only way scaled systems are deployed, but they’re going to become very common platforms for scale-out or grid infrastructure for utility computing.

Here is how I charted the ecosystem last spring. There’s not a lot of direct interaction between the two, and I know there are a lot of players missing. Frankly, it’s getting crazy complex. There has been an explosion of players, and I’ve run out of space, so I’ve just not gotten around to updating it. (If anyone engaged in these ecosystems wants to update it and send me a copy – I’d be much obliged! Maybe you guys at Nebula ? ;-)).

An AIS keynote panel – What?
Which brings me back to that keynote panel at AIS. Every year LSI has a conference that’s by invitation only (sorry). It’s become a pretty big deal. We have some very high-profile keynotes from industry leaders. There is a fantastic tech showcase of LSI products, partner and ecosystem company’s products, and a good mix of proof of concepts, prototypes and what-if products. And there are a lot of breakout sessions on industry topics, trends and solutions. Last year I personally escorted an IBM fellow, Google VPs, Facebook architects, bank VPs, Amazon execs, flash company execs, several CTOs, some industry analysts, database and transactional company execs…

It’s a great place to meet and interact with peers if you’re involved in the datacenter, network or cellular infrastructure businesses.  One of the keynotes is actually a panel of 2. The COO of Open Compute, Cole Crawford, and the co-founder of OpenStack, Chris Kemp (who is also the founder and CSO of Nebula). Both of them are very smart, experienced and articulate, and deeply involved in these movements. It should be a really engaging, interesting keynote panel, and I’m lucky enough to have a front-row seat. I’ll be the moderator, and I’m already working on questions. If there is something specific you would like asked, let me know, and I’ll try to accommodate you.

You can see more here.

Yea – I’m very interested in Open Compute and OpenStack. I think these two movements are co-dependent. And I think they are already changing our industry – even before they are ready for real large-scale deployment.  Sure they can and will exist independently, but I think the success of each is tied to the other. The people running these open-source efforts might have a different view. Luckily, we’ll get to find out what they think next month… And I’m lucky enough to have a front row seat.

 

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I was lucky enough to get together for dinner and beer with old friends a few weeks ago. Between the 4 of us, we’ve been involved in or responsible for a lot of stuff you use every day, or at least know about.

Supercomputers, minicomputers, PCs, Macs, Newton, smart phones, game consoles, automotive engine controllers and safety systems, secure passport chips, DRAM interfaces, netbooks, and a bunch of processor architectures: Alpha, PowerPC, Sparc, MIPS, StrongARM/XScale, x86 64-bit, and a bunch of other ones you haven’t heard of (um – most of those are mine, like TriCore). Basically if you drive a European car, travel internationally, use the Internet , if you play video games, or use a smart phone, well…  you’re welcome.

Why do I tell you this? Well – first I’m name dropping – I’m always stunned I can call these guys friends and be their peers. But more importantly, we’ve all been in this industry as architects for about 30 years. Of course our talk went to what’s going on today. And we all agree that we’ve never seen more changes – inflexions – than the raft unfolding right now. Maybe its pressure from the recession, or maybe un-naturally pent up need for change in the ecosystem, but change there is.

Changes in who drives innovation, what’s needed, the companies on top and on bottom at every point in the food chain, who competes with whom, how workloads have changed from compute to dataflow, software has moved to opensource, how abstracted code is now from processor architecture, how individual and enterprise customers have been revolting against the “old” ways, old vendors, old business models, and what the architectures look like, how processors communicate, and how systems are purchased, and what fundamental system architectures look like. But not much besides that…

Ok – so if you’re an architect, that’s as exciting as it gets (you hear it in my voice – right ?), and it makes for a lot of opportunities to innovate and create new or changed businesses. Because innovation is so often at the intersection of changing ways of doing things. We’re at a point where the changes are definitely not done yet. We’re just at the start. (OK – now try to imagine a really animated 4-way conversation over beers at the Britannia Arms in Cupertino… Yea – exciting.)

I’m going to focus on just one sliver of the market – but it’s important to me – and that’s enterprise IT.  I think the changes are as much about business models as technology.

Hyperscale datacenters drive innovation
I’ll start in a strange place. Hyperscale datacenters (think social media, search, etc.) and the scale of deployment changes the optimization point. Most of us starting to get comfortable with rack as the new purchase quantum. And some of us are comfortable with the pod or container as the new purchase quantum. But the hyperscale dataenters work more at the datacenter as the quantum. By looking at it that way, they can trade off the cost of power, real estate, bent sheet metal, network bandwidth, disk drives, flash, processor type and quantity, memory amount, where work gets done, and what applications are optimized for. In other words, we shifted from looking at local optima to looking for global optima. I don’t know about you, but when I took operations research in university, I learned there was an unbelievable difference between the two – and global optima was the one you wanted…

Hyperscale datacenters buy enough (top 6 are probably more than 10% of the market today) that 1) they need to determine what they deploy very carefully on their own, and 2) vendors work hard to give them what they need.

That means innovation used to be driven by OEMs, but now it’s driven by hyperscale datacenters and it’s driven hard. That global optimum? It’s work/$ spent. That’s global work, and global spend. It’s OK to spend more, even way more on one thing if over-all you get more done for the $’s you spend.

That’s why the 3 biggest consumers of flash in servers are Facebook, Google, and Apple, with some of the others not far behind. You want stuff, they want to provide it, and flash makes it happen efficiently. So efficiently they can often give that service away for free.

Hyperscale datacenters have started to publish their cost metrics, and open up their architectures (like OpenCompute), and open up their software (like Hadoop and derivatives). More to the point, services like Amazon have put a very clear $ value on services. And it’s shockingly low.

Enterprises are paying attention
Enterprises have looked at those numbers. Hard. That’s catalyzed a customer revolt against the old way of doing things – the old way of buy and billing. OEMs and ISVs are creating lots of value for enterprise, but not that much. They’ve been innovating around “stickiness” and “lock-in” (yea – those really are industry terms) for too long, while hyperscale datacenters have been focused on getting stuff done efficiently. The money they save per unit just means they can deploy more units and provide better services.

That revolt is manifesting itself in 2 ways. The first is seen in the quarterly reports of OEMs and ISVs. Rumors of IBM selling its X-series to Lenovo, Dell going private, Oracle trying to shift business, HP talking of the “new style of IT”… The second is enterprises are looking to emulate hyperscale datacenters as much as possible, and deploy private cloud infrastructure. And often as not, those will be running some of the same open source applications and file systems as the big hyperscale datacenters use.

Where are the hyperscale datacenters leading them? It’s a big list of changes, and they’re all over the place.

  • Simple, “vanity free” servers. Everything you need, nothing you don’t
  • Efficient racks/pods, minimized metal, shipping weight, airflow impediment
  • Simplified management , homogeneous across vendors
  • DAS systems with distributed file systems like HDFS, etc.
  • Flash acceleration for databases sensitive to latency
  • New hardware/software functions like memcached,  key-value stores…
  • Autonomous, self-managed, self-deployed clusters at scale
  • Disagregated servers – also called pooled resources
  • Alternate processor architectures (besides x86)
  • The promise of “far” main memory in massive chucks of next generation non-volatile memory like PCM, STT, ReRam, and possibly flash

But they’re also looking at a few different things. For example, global name space NAS file systems. Personally? I think this one’s a mistake. I like the idea of file systems/object stores, but the network interconnect seems like a bottleneck. Storage traffic is shared with network traffic, creates some network spine bottlenecks, creates consistency performance bottlenecks between the NAS heads, and – let’s face it – people usually skimp on the number of 10GE ports on the server and in the top of rack switch. A typical SAS storage card now has 8 x 12G ports – that’s 96G of bandwidth. Will servers have 10 x 10G ports? Yea. I didn’t think so either.

Anyway – all this is not academic. One Wall Street bank shared with me that – hold your breath – it could save 70% of its spend going this route. It was shocked. I wasn’t shocked, because at first blush this seems absurd – not possible. That’s how I reacted. I laughed. But… The systems are simpler and less costly to make. There is simply less there to make or ship than OEMs force into the machines for uniqueness and “value.” They are purchased from much lower margin manufacturers. They have massively reduced maintenance costs (there’s less to service, and, well, no OEM service contracts). And also important – some of the incredibly expensive software licenses are flipped to open source equivalents. Net savings of 70%. Easy. Stop laughing.

Disaggregation: Or in other words, Pooled Resources
But probably the most important trend from all of this is what server manufacturers are calling “disaggregation” (hey – you’re ripping apart my server!) but architects are more descriptively calling pooled resources.

First – the intent of disaggregation is not to rip the parts of a server to pieces to get lowest pricing on the components. No. If you’re buying by the rack anyway – why not package so you can put like with like. Each part has its own life cycle after all. CPUs are 18 months. DRAM is several years. Flash might be 3 years. Disks can be 5 to 7 years. Networks are 5 to 10 years. Power supplies are… forever? Why not replace each on its own natural failure/upgrade cycle? Why not make enclosures appropriate to the technology they hold? Disk drives need solid vibration-free mechanical enclosures of heavy metal. Processors need strong cooling. Flash wants to run hot. DRAM cool.

Second – pooling allows really efficient use of resources. Systems need slush resources. What happens to a systems that uses 100% of physical memory? It slows down a lot. If a database runs out of storage? It blue screens. If you don’t have enough network bandwidth? The result is, every server is over provisioned for its task. Extra DRAM, extra network bandwidth, extra flash, extra disk drive spindles.. If you have 1,000 nodes you can easily strand TBytes of DRAM, TBytes of flash, a TByte/s of network bandwidth of wasted capacity, and all that always burning power. Worse, if you plan wrong and deploy servers with too little disk or flash or DRAM, there’s not much you can do about it. Now think 10,000 or 100,000 nodes… Ouch.

If you pool those things across 30 to 100 servers, you can allocate as needed to individual servers. Just as importantly, you can configure systems logically, not physically. That means you don’t have to be perfect in planning ahead what configurations and how many of each you’ll need. You have sub-assemblies you slap into a rack, and hook up by configuration scripts, and get efficient resource allocation that can change over time. You need a lot of storage? A little? Higher performance flash? Extra network bandwidth? Just configure them.

That’s a big deal.

And of course, this sets the stage for immense pooled main memory – once the next generation non-volatile memories are ready – probably starting around 2015.

You can’t underestimate the operational problems associated with different platforms at scale. Many hyperscale datacenters today have around 6 platforms. If you think they are rolling out new versions of those before old ones are retired they often have 3 generations of each. That’s 18 distinct platforms, with multiple software revisions of each. That starts to get crazy when you may have 200,000 to 400,000 servers to manage and maintain in a lights out environment. Pooling resources and allocating them in the field goes a huge way to simplifying operations.

Alternate Processor Architecture
It didn’t always used to be Intel x86. There was a time when Intel was an upstart in the server business. It was Power, MIPs, Alpha, SPARC… (and before that IBM mainframes and minis, etc). Each of the changes was brought on by changing the cost structure. Mainframes got displaced by multi-processor RISC, which gave way to x86.

Today, we have Oracle saying they’re getting out of x86 commodity servers and doubling down on SPARC. IBM is selling off its x86 business and doubling down on Power (hey – don’t confuse that with PowerPC – which started as an architectural cut-down of Power – I was there…). And of course there is a rash of 64-bit ARM server SOCs coming – with HP and Dell already dabbling in it. What’s important to realize is that all of these offerings are focusing on the platform architecture, and how applications really perform in total, not just the processor.

Another view
Let me warp up with an email thread cut/paste from a smart friend – Wayne Nation. I think he summed up some of what’s going on well, in a sobering way most people don’t even consider.

“Does this remind you of a time, long ago, when the market was exploding with companies that started to make servers out of those cheap little desktop x86 CPUs? What is different this time? Cost reduction and disaggregation? No, cost and disagg are important still, but not new.

  • So what IS new?  I think it’s the massive hyperscale datacenter purchasing and intelligence of the customers at the hyperscale datacenter.
  • Before, it was the intelligence of a few server suppliers that drove innovation. Now, it is the intelligence and purchasing of a few massive hyperscale datacenter *buyers* that drive innovation.

A new CPU architecture? No, x86 was “new” before. ARM promises to reduce cost, as did Intel.

  • So what IS new? The promise of competitive multi-sourcing of the silicon – What’s so great about that?
  • ARM and licensees need to be as consistent and regular as Intel was in promising and delivering *silicon*.
  • When some of the half-dozen ARM SOC vendors fail to deliver, the others get stronger. Isn’t that how we arrived with Intel?
  • The ISA does not matter as much, and Intel (if smart) can still use that as a strength as much as ARM can do it.
  • This is Intel’s silicon to lose (and they may still lose). But Intel will need to take some pain, just like AS/400, S/360, DEC went through.

Disaggregation enables hyperscale datacenters to leverage vanity-free, but consistent delivery will determine the winning supplier. There is the potential for another Intel to rise from these other companies. “

Wow.

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