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:
And now, the color commentary on the Wikibon big data report …
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.
I was asked some interesting questions recently by CEO & CIO, a Chinese business magazine. The questions ranged from how Chinese Internet giants like Alibaba, Baidu and Tencent differ from other customers and what leading technologies big Internet companies have created to questions about emerging technologies such as software-defined storage (SDS) and software-defined datacenters (SDDC) and changes in the ecosystem of datacenter hardware, software and service providers. These were great questions. Sometimes you need the press or someone outside the industry to ask a question that makes you step back and think about what’s going on.
I thought you might interested, so this blog, the first of a 3-part series covering the interview, shares details of the first two questions.
CEO & CIO: In recent years, Internet companies have built ultra large-scale datacenters. Compared with traditional enterprises, they also take the lead in developing datacenter technology. From an industry perspective, what are the three leading technologies of ultra large-scale Internet data centers in your opinion? Please describe them.
There are so many innovations and important contributions to the industry from these hyperscale datacenters in hardware, software and mechanical engineering. To choose three is difficult. While I would prefer to choose hardware innovations as their big ones, I would suggest the following as they have changed our world and our industry and are changing our hardware and businesses:
Autonomous behavior and orchestration
An architect at Microsoft once told me, “If we had to hire admins for our datacenter in a normal enterprise way, we would hire all the IT admins in the world, and still not have enough.” There are now around 1 million servers in Microsoft datacenters. Hyperscale datacenters have had to develop autonomous, self-managing, sometimes self-deploying datacenter infrastructure simply to expand. They are pioneering datacenter technology for scale – innovating, learning by trial and error, and evolving their practices to drive more work/$. Their practices are specialized but beginning to be emulated by the broader IT industry. OpenStack is the best example of how that specialized knowledge and capability is being packaged and deployed broadly in the industry. At LSI, we’re working with both hyperscale and orchestration solutions to make better autonomous infrastructure.
High availability at datacenter level vs. machine level
As systems get bigger they have more components, more modes of failure and they get more complex and expensive to maintain reliability. As storage is used more, and more aggressively, drives tend to fail. They are simply being used more. And yet there is continued pressure to reduce costs and complexity. By the time hyperscale datacenters had evolved to massive scale – 100’s of thousands of servers in multiple datacenters – they had created solutions for absolute reliability, even as individual systems got less expensive, less complex and much less reliable. This is what has enabled the very low cost structures of the cloud, and made it a reliable resource.
These solutions are well timed too, as more enterprise organizations need to maintain on-premises data across multiple datacenters with absolute reliability. The traditional view that a single server requires 99.999% reliability is giving way to a more pragmatic view of maintaining high reliability at the macro level – across the entire datacenter. This approach accepts the failure of individual systems and components even as it maintains data center level reliability. Of course – there are currently operational issues with this approach. LSI has been working with hyperscale datacenters and OEMs to engineer improved operational efficiency and resilience, and minimized impact of individual component failure, while still relying on the datacenter high-availability (HA) layer for reliability.
It’s such an overused term. It’s difficult to believe the term barely existed a few years ago. The gift of Hadoop® to the industry – an open source attempt to copy Google® MapReduce and Google File System – has truly changed our world unbelievably quickly. Today, Hadoop and the other big data applications enable search, analytics, advertising, peta-scale reliable file systems, genomics research and more – even services like Apple® Siri run on Hadoop. Big data has changed the concept of analytics from statistical sampling to analysis of all data. And it has already enabled breakthroughs and changes in research, where relationships and patterns are looked for empirically, rather than based on theories.
Overall, I think big data has been one of the most transformational technologies this century. Big data has changed the focus from compute to storage as the primary enabler in the datacenter. Our embedded hard disk controllers, SAS (Serial Attached SCSI) host bus adaptors and RAID controllers have been at the heart of this evolution. The next evolutionary step in big data is the broad adoption of graph analysis, which integrates the relationship of data, not just the data itself.
CEO & CIO: Due to cloud computing, mobile connectivity and big data, the traditional IT ecosystem or industrial chain is changing. What are the three most important changes in LSI’s current cooperation with the ecosystem chain? How does LSI see the changes in the various links of the traditional ecosystem chain? What new links are worth attention? Please give some examples.
Cloud computing and the explosion of data driven by mobile devices and media has and continues to change our industry and ecosystem contributors dramatically. It’s true the enterprise market (customers, OEMs, technology, applications and use cases) has been pretty stable for 10-20 years, but as cloud computing has become a significant portion of the server market, it has increasingly affected ecosystem suppliers like LSI.
Timing: It’s no longer enough to follow Intel’s ticktock product roadmap. Development cycles for datacenter solutions used to be 3 to 5 years. But these cycles are becoming shorter. Now, demand for solutions is closer to 6 months – forcing hardware vendors to plan and execute to far tighter development cycles. Hyperscale datacenters also need to be able to expand resources very quickly, as customer demand dictates. As a result they incorporate new architectures, solutions and specifications out of cycle with the traditional Intel roadmap changes. This has also disrupted the ecosystem.
End customers: Hyperscale datacenters now have purchasing power in the ecosystem, with single purchase orders sometimes amounting to 5% of the server market. While OEMs still are incredibly important, they are not driving large-scale deployments or innovating and evolving nearly as fast. The result is more hyperscale design-win opportunities for component or sub-system vendors if they offer something unique or a real solution to an important problem. This also may shift profit pools away from OEMs to strong, nimble technology solution innovators. It also has the potential to reduce overall profit pools for the whole ecosystem, which is a potential threat to innovation speed and re-investment.
New players: Traditionally, a few OEMs and ISVs globally have owned most of the datacenter market. However, the supply chain of the hyperscale cloud companies has changed that. Leading datacenters have architected, specified or even built (in Google’s case) their own infrastructure, though many large cloud datacenters have been equipped with hyperscale-specific systems from Dell and HP. But more and more systems built exactly to datacenter specifications are coming from suppliers like Quanta. Newer network suppliers like Arista have increased market share. Some new hyperscale solution vendors have emerged, like Nebula. And software has shifted to open source, sometimes supported for-pay by companies copying the Redhat® Linux model – companies like Cloudera, Mirantis or United Stack. Personally, I am still waiting for the first 3rd-party hardware service emulating a Linux support and service company to appear.
Open initiatives: Yes, we’ve seen Hadoop and its derivatives deployed everywhere now – even in traditional industries like oil and gas, pharmacology, genomics, etc. And we’ve seen the emergence of open-source alternatives to traditional databases being deployed, like Casandra. But now we’re seeing new initiatives like Open Compute and OpenStack. Sure these are helpful to hyperscale datacenters, but they are also enabling smaller companies and universities to deploy hyperscale-like infrastructure and get the same kind of automated control, efficiency and cost structures that hyperscale datacenters enjoy. (Of course they don’t get fully there on any front, but it’s a lot closer). This trend has the potential to hurt OEM and ISV business models and markets and establish new entrants – even as we see Quanta, TYAN, Foxconn, Wistron and others tentatively entering the broader market through these open initiatives.
New architectures and new algorithms: There is a clear movement toward pooled resources (or rack scale architecture, or disaggregated servers). Developing pooled resource solutions has become a partnership between core IP providers like Intel and LSI with the largest hyperscale datacenter architects. Traditionally new architectures were driven by OEMs, but that is not so true anymore. We are seeing new technologies emerge to enable these rack-scale architectures (RSA) – technologies like silicon photonics, pooled storage, software-defined networks (SDN), and we will soon see pooled main memory and new nonvolatile main memories in the rack.
We are also seeing the first tries at new processor architectures about to enter the datacenter: ARM 64 for cool/cold storage and web tier and OpenPower P8 for high power processing – multithreaded, multi-issue, pooled memory processing monsters. This is exciting to watch. There is also an emerging interest in application acceleration: general-purposing computing on graphics processing units (GPGPUs), regular expression processors (regex) live stream analytics, etc. We are also seeing the first generation of graph analysis deployed at massive scale in real time.
Innovation: The pace of innovation appears to be accelerating, although maybe I’m just getting older. But the easy gains are done. On one hand, datacenters need exponentially more compute and storage, and they need to operate 10x to 1000x more quickly. On the other, memory, processor cores, disks and flash technologies are getting no faster. The only way to fill that gap is through innovation. So it’s no surprise there are lots of interesting things happening at OEMs and ISVs, chip and solution companies, as well as open source community and startups. This is what makes it such an interesting time and industry.
Consumption shifts: We are seeing a decline in laptop and personal computer shipments, a drop that naturally is reducing storage demand in those markets. Laptops are also seeing a shift to SSD from HDD. This has been good for LSI, as our footprint in laptop HDDs had been small, but our presence in laptop SSDs is very strong. Smart phones and tablets are driving more cloud content, traffic and reliance on cloud storage. We have seen a dramatic increase in large HDDs for cloud storage, a trend that seems to be picking up speed, and we believe the cloud HDD market will be very healthy and will see the emergence of new, cloud-specific HDDs that are radically different and specifically designed for cool and cold storage.
There is also an explosion of SSD and PCIe flash cards in cloud computing for databases, caches, low-latency access and virtual machine (VM) enablement. Many applications that we take for granted would not be possible without these extreme low-latency, high-capacity flash products. But very few companies can make a viable storage system from flash at an acceptable cost, opening up an opportunity for many startups to experiment with different solutions.
Summary: So I believe the biggest hyperscale innovations are autonomous behavior and orchestration, HA at the datacenter level vs. machine level, and big data. These are radically changing the whole industry. And what are those changes for our industry and ecosystem? You name it: timing, end customers, new players, open initiatives, new architectures and algorithms, innovation, and consumption patterns. All that’s staying the same are legacy products and solutions.
These were great questions. Sometimes you need the press or someone outside the industry to ask a question that makes you step back and think about what’s going on. Great questions.
Tags: Alibaba, Apple Siri, Arista, ARM 64, Baidu, big data, Casandra, CEO & CIO Magazine, China, cloud storage, Cloudera, cold storage, cool storage, datacenter, datacenter ecosystem, Dell, flash, Foxconn, Google File System, Google MapReduce, Hadoop, hard disk drive, HDD, high availability, HP, hyperscale datacenter, Intel, Internet, latency, Microsoft, Mirantis, Nebula, OEM, Open Compute, OpenPower P8, OpenStack, original equipment manufacturer, Quanta, rack scale, RAID, Redhat Linux, SAS, SDDC, SDN, SDS, Serial Attached SCSI, software-defined datacenter, software-defined networks, software-defined storage, solid state drive, SSD, Tencent, TYAN, United Stack, virtual machine, VM, Wistron
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.
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.
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.
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.
Tags: 10 gigabit ethernet, 2020, Amazon, Apple, China, Cisco, cloud storage, cold storage, datacenter, Dell, EMC, Facebook, flash, Google, Hadoop, HP, Huawei, hyperscale datacenter, IBM, iPhone, kaleidoscope, Lenovo, NAS, NetApp, non-volatile memory, NVM, Open Compute, OpenStack, rack scale architecture, SAN, SoC, Sun, VMware, YouTube
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.
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.
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.
A new CPU architecture? No, x86 was “new” before. ARM promises to reduce cost, as did Intel.
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. “