I started working years ago to engage large datacenters, learn what their problems are and try to craft solutions for their problems. It’s taken years, but we engaged them, learned, changed how we thought about storage and began creating solutions that are being deployed at scale.

We’ve started to do the same with the Chinese Internet giants. They’re growing at an incredible rate.  They have similar problems, but it’s surprising how different their solution approaches are. Each one is unique. And we’re constantly learning from these guys.

So to wrap up the blog series on my interview with CIO & CEO magazine, here are the last two questions to explain a bit more.

CEO & CIO: Please use examples to tell the stories about the forward-looking technologies and architectures that LSI has jointly developed with Internet giants.

While our host bus adapters (HBAs) and MegaRAID® solutions have been part of the hyperscale Internet companies’ infrastructure since the beginning, we have only recently worked very closely with them to drive joint innovation. In 2009 I led the first LSI engagement with what we then called “mega datacenters.” It took a while to understand what they were doing and why. By 2010 we realized there were specialized needs, and began to imagine new hardware products that worked with these datacenters. Out of this work came the realization that flash was important for efficiency and capability, and the “invention” of LSI® Nytro™ product portfolio. (More are in the pipeline). We have worked closely with hyperscale datacenters to evolve and tune these solutions, to where Nytro products have become the backbone of their main revenue platforms. Facebook has been a vitally important partner in evolving our Nytro platform – teaching us what was truly needed, and now much of their infrastructure runs on LSI products. These same products are a good fit for other hyperscale customers, and we are slowly winning many of the large ones.

Looking forward, we are partnered with several Internet giants in the U.S. and China to work on cold storage solutions, and more importantly shared DAS (Distributed DAS: D-DAS) solutions. We have been demonstrating prototypes. These solutions enable pooled architectures and rack scale architecture, and can be made to work tightly with software-defined datacenters (SDDCs). They simplify management and resource allocation – making task deployment more efficient and easier. Shared DAS solutions increase infrastructure efficiency and improves lifecycle management of components. And they have the potential to radically improve application performance and infrastructure costs.

Looking further into the future, we see even more radical changes in silicon supporting transport protocols and storage models, and in rack scale architectures supporting storage and pooled memory. And cold storage is a huge though, some would say, boring problem that we are also focused on – storing lots of data for free and using no power to do it… but I really can’t talk about any of that.

CEO & CIO: LSI maintains good contact with big Internet companies in China. What are the biggest differences between dealing with these Internet enterprises and dealing with traditional partners?

Yes, we have a very good relationship with large Chinese Internet companies. In fact, I will be visiting Tencent, Alibaba and Baidu in a few weeks. One of the CTOs I would like to say is a friend. That is, we have fun talking together about the future.

These meetings have evolved. The first meetings LSI had about two years ago were sales calls, or support for OEM storage solutions. These accomplished very little. Once we began visiting as architects speaking to architects, real dialogs began. Our CEO has been spending time in China meeting with these Internet companies both to learn, and to make it clear that they are important to us, and we want a chance to solve their problems. But the most interesting conversations have been the architectural ones. There have been very clear changes in the two years I have traveled within China – from standard enterprise to hyperscale architectures.

We’ve received fascinating feedback on architecture, use, application profiles, platforms, problems and goals. We have strong engagement with the U.S. Internet giants. At the highest level, the Chinese Internet companies have similar problems and goals. But the details quickly diverge because of revenue per user, resources, power availability, datacenter ownership and Internet company age. The use of flash is very different.

The Chinese Internet giants are at an amazing change point. Most are ready for explosive growth of infrastructure and deployment of cloud services. Most are changing from standard OEM systems and architectures to self-designed hyperscale systems after experimenting with Scorpio and microserver deployments. Several, like JD.com (an Amazon-like company) are moving from hosted to self-built infrastructure. And there seems to be a general realization that the datacenter has changed from a compute-centric model to a dataflow model, where storage and network dictate how much work gets done more than the CPU does. These giants are leveraging their experience and capability to move very quickly, and in a few cases are working to create true pooled rack level architectures much like Facebook and Google have started in the U.S. In fact, Baidu is similar to Facebook in this approach, but is different in its longer term goals for the architecture.

The Chinese companies are amazingly diverse, even within one datacenter, and arguments on architectural direction are raging within these Internet giants – it’s healthy and exciting. However, the innovations that are coming are similar to those developed by large U.S. Internet companies. Personally I have found these Internet companies much more exciting and satisfying to work with than traditional OEMs. The speed and cadence of advancement, the recognition of problems and their importance, the focus on efficiency and optimization have been much more exciting. And the youthful mentality and view to problems, without being burdened by “the way we’ve always done this” has been wonderful.

Also see these blogs of mine over the past year, where you can read more about some of these changes:

Postcard from Shenzhen: China’s hyperscale datacenter growth, mixed with a more traditional approach
China in the clouds, again
China: A lot of talk about resource pooling, a better name for disaggregation

Or see them (and others) all here.

Summary: So it’s taken years, but we engaged U.S. Internet giants, learned about their problems, changed how we thought about storage and began creating solutions that are now being deployed at scale. And we’re constantly learning from these guys. Constantly, because their problems are constantly changing.

We’ve now started to do the same with the Chinese Internet giants. They have similar problems, and will need similar solutions, but they are not the same. And just like the U.S. Internet giants, each one is unique.

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Software-defined datacenters (SDDC) and software-defined storage (SDS) are big movements in the industry right now. Just read the trade press or attend any conference and you’ll see that – it’s a big deal. We’re seeing for-pay vendors providing solutions, as well as strong ecosystems evolving around open source solutions. It’s not surprising why – there is a need for enterprises to deploy large scale compute clusters, and that takes either deep expertise that’s very rare, or orchestration tools that have not existed in the past. It’s the “necessity being the mother of invention” thing…

So datacenters are being forced to deploy large-scale clusters to handle the scale of compute needed, and the amount of data that is being captured, analyzed and stored. As an industry then, we’re being forced to simplify applications as well as the management and deployment of these large scale clusters. That’s great for datacenters. It’s even better that we’re figuring out how to provide those expanded resources and manage them for less money, and with fewer people to manage them. (well, it’s probably good for everyone but the sys admins…)

These new technologies are the key enabler. This blog, the second in my three-part series (based on interesting questions I was asked by CEO & CIO, a Chinese business magazine) examines how SDDC and SDS are helping enterprises get more out of their datacenter gear. You can read part 1 here.

CEO & CIO: What are your views on software-defined storage? What’s the development roadmap of LSI in achieving software-defined storage?

We see SDS as one of a number of vital changes underway in the datacenter. SDS promises to span some or all of file, object, key-value and block in order to pool resources and to simplify the infrastructure required in a datacenter, as well as to smooth the migration to object or key-value storage over time. Great examples of these SDS solutions are: Ceph, Swift, Cinder, Gluster, VSAN / VVOLs …. The model brings great benefits in datacenter management, resource pooling and allocation and usability. The main problem is performance – and by that I do not mean extreme performance. I mean poor performance that damages TCO, reduces efficiency of infrastructure and increases costs. Much worse than you would get otherwise. These solutions work, but compromise resource efficiency. Many require flash integrated in the system to simply maintain existing performance. However, this is a permanent change in how storage is used and deployed, and it’s a good change.

While block is what underlies most storage and will continue to for some time, the system and application level view is changing. We view SDS as having great synergy with LSI’s architectural direction – shared DAS infrastructure and ability to add “above the block” capability like quality of service (QoS), direct key/value hardware, etc, and bring improved performance and resource efficiency. Together, SDS + LSI innovation = resource pooling and allocation, including flash and cool/cold storage, management and virtual machine (VM) agility, performance and resource efficiency.

As a result, there has been tremendous interest from SDS vendors to work with us, to demonstrate prototype systems, and to make solutions better. We are working with many SDS partners to provide complete solutions. This is not a one-size-fits-all world, so there will be several solutions. Those solutions are not ready yet, but they’re coming, and will probably displace the older file and block storage systems we know and love.

CEO & CIO: Industry giants such as Intel have outlined their visions for software-defined datacenters. Chinese Internet giants have also put forward similar plans. What views does LSI have on software-defined datacenter?

If you view the AIS keynote, you’ll see we believe this is a critical part of the future datacenter. But just one critical part. Interestingly, we had Intel present as well during AIS.

SDDC creates a critical control plane for the datacenter. It is the software abstraction model that enables resource pooling. Resource pooling of compute, storage and network, with memory in the near future. It enables the automation and allocation of tasks and resources in the datacenter. The leading models are VMware® SDDC and OpenStack® software, but there are others that are important too. They’re just a little less public right now. Anyway – it’s way too early to predict which will be dominant. Just like SDS, SDDC exchanges simplified control and abstraction for performance and efficiency. As a result, it’s not a very useful concept, at least not at hyperscale levels, without hardware that really, truly supports and enables it. As the datacenter has changed from a compute-centric model to a dataflow model, the storage and network and, soon, memory become very important. They dictate the useful work that can be gotten from the datacenter.

I believe we are, as an industry, at the start of the hardware transition to support these. We are building hardware solutions for storage and network that are being designed into products today. We are working very closely with three of the largest datacenters in the US, and two in China to build not just the SDDC, but the pooled hardware infrastructure that is needed to make it work.

It’s critical to understand that SDDC solutions really work, but often the performance and efficiency is – well – terrible. That’s been the evolution in computer science and computer architecture since the beginning. You raise the abstraction level, which simplifies development and support, but either causes poor performance or requires more hardware capability that is architected to support those abstractions.

As a result, it’s really difficult to talk about SDDCs without a rack-scale architecture to support them. So we are working closely with the key SDDC software solutions/vendors, even the ones I didn’t list, to integrate and optimize the solutions to make the SDDC actually work. We have been working very closely with VMware and the OpenStack community, and we are changing the way the software plane interacts with the pooled resources. Again, there has been so much interest in our shared DAS, incorporating flash in the same architecture and management, and our Axxia® SDN control plane processor for networks.

I talk about rack-scale architectures to support SDS in the second half of this keynote and in my blog “China: A lot of talk about resource pooling, a better name for disaggregation.”

Summary: So I believe SDS is a big movement, it’s a good thing, and it’s here to stay. But… the performance is poor today. Very poor. That’s where we come in, with hardware that enables SDS and not only makes performance acceptable, but helps make it excellent, and improves efficiency and cost too. And SDDC is also a massive movement that will define the future datacenter. But it is intertwined with the rack-level concepts of pooling or disaggregation to make it really compelling. Again – that’s where we come in.

These were good questions that were interesting to answer. I hope it’s interesting to you too. I’ll post some more soon about how the Chinese Internet giants differ from other customers, and about forward-looking technologies.

Restructuring the datacenter ecosystem (Part 1)

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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.

Big data
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.

Restructuring the datacenter ecosystem (Part 2)

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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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Deploying a mix of datacenter resources in a preconfigured server – compute, storage, network, and memory – in a way that they are fixed, can’t be tuned to a use case, and must be replaced entirely for an upgrade is how the IT industry has been working for years. Each server is an island.

This is an inefficient path when you deploy more than a few servers. That’s why there is an architectural movement in hyperscale datacenters (and it’s sure to be emulated by enterprise in a few years) to “disaggregate” – or, the term I prefer, “pool” – these resources. That allows deployments to be “configured” in the field, creating tailored platforms depending on needs. And it enables more efficient life-cycle management of subsystems. Ultimately this enables more work/$, and that’s almost everyone’s goal. One hyperscale CTO I know told me over dinner he views this pooling and allocating as “hardware-based virtualization,” which is sort of true.

In this AIS interview I talk about the concept, the rational, and show how costly forklift upgrades will be behind us once this small movement becomes common practice.

Keeping up with the flood of global data with network acceleration
It’s hardly a secret that the growth of PC, mobile, and intelligent media devices and their related applications worldwide is exploding. It also no secret that they are driving an equivalent increase in global network traffic. The mystery for many datacenter network managers is how to keep up, as the flood of data traffic saturates networks, drags down network performance, and frustrates users with waiting.

Here, LSI’s Troy Bailey discusses how networks will become smarter to deliver the data you need, in the priority you need, when you need it.

 

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Open Compute and OpenStack are changing the datacenter world that we know and love. I thought they were having impact. Changing our OEMs and ODM products, changing what we expect from our vendors, changing the interoperability of managing infrastructure from different vendors. Changing our ability to deploy and manage grid and scale-out infrastructure. And changing how quickly and at what high level we can be innovating. I was wrong. It’s happening much more quickly than I thought.

On November 20-21 we hosted LSI AIS 2013. As I mentioned in a previous post, I was lucky enough to moderate a panel about Open Compute and OpenStack – “the perfect storm.” Truthfully? It felt more like sitting with two friends talking about our industry over beer. I hope to pick up that conversation again someday.

The panelists were awesome: Cole Crawford of Open Compute and Chris Kemp of OpenStack. These guys are not only influential. They have been involved from the very start of these two initiatives, and are in many ways key drivers of both movements. These are impressive, passionate guys who really are changing the world. There aren’t too many of us who can claim that. It was an engaging hour that I learned quite a bit from, and I think the audience did too. I wanted to share from my notes what I took away from that panel. I think you’ll be interested.

 

 Goals and Vision: two open source initiatives
There were a few motivations behind Open Compute, and the goal was to improve these things.

  • There have been no standards or formats for interchange in hardware design.
  • IT infrastructure has roots going back to railway switching standards (19” rack).
  • IT infrastructure has consisted of very closed systems with limited interoperability.
  • Datacenters have been wasting tremendous amounts of energy and resources on cooling and power distribution.

The goal then, for the first time, is to work backwards from workload and create open source hardware and infrastructure that is openly available and designed from the start for large scale-out deployments. The idea is to drive high efficiency in cost, materials use and energy consumption. More work/$.

One surprising thing that came up – LSI is in every current contribution in Open Compute.

OpenStack layers services that describe abstractions of computer networking and storage. LSI products tend to sit at that lowest level of abstraction, where there is now a wave of innovation. OpenStack had similar fragmentation issues to deal with and its goals are something like:

  • Bring software resource components together for pooled compute, storage and network resources.
  • Present them as resources for application deployment.
  • Create a virtual reference implementation, where the details can vary.
  • Allow integrating new infrastructure under that abstraction.
  • Simplify deployment of clusters at scale.
  • Almost like a kernel for the scale-out cluster

There is a certain amount of compatibility with Amazon’s cloud services. Chris’s point was that Amazon is incredibly innovative and a lot of enterprises should use it, but OpenStack enables both service providers and private clouds to compete with Amazon, and it allows unique innovation to evolve on top of it.

OpenStack and Open Compute are not products. They are “standards” or platform architectures, with companies using those standards to innovate on top of them. The idea is for one company to innovate on another’s improvements – everybody building on each other’s work. A huge brain trust. The goal is to create a competitive ecosystem and enable a rapid pace of innovation, and enable large-scale, inexpensive infrastructure that can be managed by a small team of people, and can be managed like a single server to solve massive scale problems.

Here’s their thought. Hardware is a supply chain management game + services.  Open Compute is an opportunity for anyone to supply that infrastructure. And today, OEMs are killer at that. But maybe ODMs can be too. Open Compute allows innovation on top of the basic interoperable platforms. OpenStack enables a framework for innovation on top as well: security, reliability, storage, network, performance. It becomes the enabler for innovation, and it provides an “easy” way for startups to plug into a large, vibrant ecosystem. And for customers – someone said its “exa data without exadollar”…

As a result, the argument is this should be good for OEMs and ISVs, and help create a more innovative ecosystem and should also enable more infrastructure capacity to create new and better services. I’m not convinced that will happen yet, but it’s a laudable goal, and frankly that promise is part of what is appealing to LSI.

Open Compute and OpenStack are peanut butter and jelly
Ok – if you’re outside of the US, that may not mean much to you. But if you’ve lived in the US, you know that means they fit perfectly, and make something much greater together than their humble selves.

Graham Weston, Chairman of the Rackspace Board, was the one who called these two “peanut butter and jelly.”

Cole and Chris both felt the initiatives are co-enabling, and probably co-travelers too. Sure they can and will deploy independently, but OpenStack enables the management of large scale clusters, which really is not easy. Open Compute enables lower cost large-scale manageable clusters to be deployed. Together? Large-scale clusters that can be installed and deployed more affordably, and easily without hiring a cadre of rare experts.

Personally? I still think they are both a bit short of being ready for “prime time” – or broad deployment, but Cole and Chris gave me really valid arguments to show me I’m wrong. I guess we’ll see.

US or global vision?
I asked if these are US-centric or global visions. There were no qualms – these are global visions. This is just the 3rd anniversary of OpenStack, but even so, there are OpenStack organizations in more than 100 countries, 750 active contributors, and large-scale deployments in datacenters that you probably use every day – especially in China and the US. Companies like PayPal and Yahoo, Rackspace, Baidu, Sina Weibo, Alibaba, JD, and government agencies and HPC clusters like CERN, NASA, and China Defense.

Open Compute is even younger – about 2 years old. (I remember – I was invited to the launch). Even so, most of Facebook’s infrastructure runs on Open Compute. Two Wall Street banks have deployed large clusters, with more coming, and Riot Games, which uses Open Compute infrastructure, drives 3% of the global network traffic with League of Legends. (A complete aside – one of my favorite bands to workout with did a lot of that game’s music, and the live music at the League of Legends competition a few months ago: http://www.youtube.com/watch?v=mWU4QvC09uM – not for everyone, but I like it.)

Both Cole and Chris emailed me more data after the fact on who is using these initiatives. I have to say – they are right. It really has taken off globally, especially OpenStack in the fast-paced Chinese market this year.

Book: 4th Paradigm – A tribute to computer science researcher Jim Grey
Cole and Chris mentioned a book during the panel discussion. A book I had frankly never heard of. It’s called the 4th Paradigm. It was a series of papers dedicated to researcher Jim Grey, who was a quiet but towering figure that I believe I met once at Microsoft Research. The book was put together by Gordon Bell, someone who I have met, and have profound respect for. And there are mentions of people, places, and things that have been woven through my (long) career. I think I would sum up its thesis in a quote from Jim Grey near the start of the book:

“We have to do better producing tools to support the whole research cycle – from data capture and data curation to data analysis and data visualization.”

This is stunningly similar to the very useful big data framework we have been using recently at LSI: ”capture, hold, analyze”… I guess we should have added visualize, but that doesn’t have too much to do with LSI’s business.

As an aside, I would recommend this book for the background and inspiration in why we as an industry are trying to solve many of these computer science problems, and how transformational the impact might be. I mean really transformational in the world around us, what we know, what we can do, and how quickly we can do it – which is tightly related to our CEO’s keynote and the vision video at AIS.

Demos at AIS: peanut butter and jelly - and bread?
Ok – I’m struggling for analogy. We had an awesome demo at AIS that Chris and Cole pointed out during the panel. It was originally built using Nebula’s TOR appliance, Open Compute hardware, and LSI’s storage magic to make it complete. The three pieces coming together. Tasty. The Open Compute hardware was swapped out last minute (for safety, those boxes were meant for the datacenter – not the showcase in a hotel with tipsy techies) and were  generously supplied by Supermicro.

I don’t think the proto was close to any one of our visions, but even as it stood, it inspired a lot of people, and would make a great product. A short rack of servers, with pooled storage in the rack, OpenStack orchestrating the point and click spawning and tear down of dynamically sized LUNs of different characteristics under the Cinder presentation layer, and deployment of tasks or VMs on them.

We’re working on completing our joint vision. I think the industry will be very impressed when they see it. Chris thinks people will be stunned, and the industry will be changed.

Catalyzing the market The future may be closer than we think
Ultimately, this is all about economics. We’re in the middle of an unprecedented bifurcation in IT use. On one hand we’re running existing apps on new, dense enterprise hardware using VMs to layer many applications on few servers. On the other, we’re investing in applications to run at scale across inexpensive clusters of commodity hardware. This has spawned a split in IT vendor business units, product lines and offerings, and sometimes even IT infrastructure management in the datacenter.

New applications and services are needing more infrastructure, and are getting more expensive to power, cool, purchase, run. And there is pressure to transform the datacenter from a cost center into a profit center. As these innovations start, more companies will need scale infrastructure, arguably Open Compute, and then will need an Openstack framework to deploy it quickly.

Whats this mean? With a combination of big data and mobile device services driving economic value, we may be at the point where these clusters start to become mainstream. As an industry we’re already seeing a slight decline in traditional IT equipment sales and a rapid growth in scale-out infrastructure sales. If that continues, then OpenStack and Open Compute are a natural fit. The deployment rate uptick in life sciences, oil and gas, financials this year – really anywhere there is large-scale Hadoop, big data or analytics – may be the start of that growth curve. But both Chris and Cole felt it would probably take 5 years to truly take off.

Time to Wrap Up
I asked Chris and Cole for audience takeaways. Theirs were pretty simple, though possibly controversial in an industry like ours.

Hardware vendors should think about products and how they interface and what abstractions they present and how they fit into the ecosystem. These new ecosystems should allow them to easily plug in. For example, storage under Cinder can be quickly and easily morphed – that’s what we did with our demo.

We should be designing new software to run on distributed scale-out systems in clouds. Chris went on to say their code name was “Maestro” because it orchestrates like in a symphony, bringing things together in a beautiful way. He said “make instruments for the artists out there.” The brain trust. Look for their brushstrokes.

Innovate in the open, and leverage the open initiatives that are available to accelerate innovation and efficiency.

On your next IT purchase, try an RFP with an Open Compute vendor. Cole said you might be surprised. Worst case, you may get a better deal from your existing vendor.

So, Open Compute and Openstack are changing the datacenter world that we know and love. I thought these were having a quick impact, changing our OEMs and ODM products, changing what we expect from our vendors, changing the interoperability of managing infrastructure from different vendors, changing our ability to deploy and manage grid and scale-out infrastructure, and changing how quickly and at what high level we can be innovating. I was wrong. It’s happening much more quickly than even I thought.

 

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Back in the 1990s, a new paradigm was forced into space exploration. NASA faced big cost cuts. But grand ambitions for missions to Mars were still on its mind. The problem was it couldn’t dream and spend big. So the NASA mantra became “faster, better, cheaper.” The idea was that the agency could slash costs while still carrying out a wide variety of programs and space missions. This led to some radical rethinks, and some fantastically successful programs that had very outside-the-box solutions. (Bouncing Mars landers anyone?)

That probably sounds familiar to any IT admin. And that spirit is alive at LSI’s AIS – The Accelerating Innovation Summit, which is our annual congress of customers and industry pros, coming up Nov. 20-21 in San Jose. Like the people at Mission Control, they all want to make big things happen… without spending too much.

Take technology and line of business professionals. They need to speed up critical business applications. A lot. Or IT staff for enterprise and mobile networks, who must deliver more work to support the ever-growing number of users, devices and virtualized machines that depend on them. Or consider mega datacenter and cloud service providers, whose customers demand the highest levels of service, yet get that service for free. Or datacenter architects and managers, who need servers, storage and networks to run at ever-greater efficiency even as they grow capability exponentially.

(LSI has been working on many solutions to these problems, some of which I spoke about in this blog.)

It’s all about moving data faster, better, and cheaper. If NASA could do it, we can too. In that vein, here’s a look at some of the topics you can expect AIS to address around doing more work for fewer dollars:

  • Emerging solid state technologies – Flash is dramatically enhancing datacenter efficiency and enabling new use cases. Could emerging solid state technologies such as Phase Change Memory (PCM) and Spin-Torque Transfer (STT) RAM radically change the way we use storage and memory?
  • Hyperscale deployments – Traditional SAN and NAS lack the scalability and economics needed for today’s hyperscale deployments. As businesses begin to emulate hyperscale deployments, they need to scale and manage datacenter infrastructure more effectively. Will software increasingly be used to both manage storage and provide storage services on commodity hardware?
  • Sub-20nm flash – The emergence of sub-20nm flash promises new cost savings for the storage industry. But with reduced data reliability, slower overall access times and much lower intrinsic endurance, is it ready for the datacenter?
  • Triple-Level Cell flash – The move to Multi-Level Cell (MLC) flash helped double the capacity per square millimeter of silicon, and Triple-Level Cell (TLC) promises even higher storage density. But TCL comes at a cost: its working life is much shorter than MLC. So what, if any role will TLC play in the datacenter? Remember – it wasn’t long ago no one believed MLC could be used in enterprise.
  • Flash for virtual desktop – Virtual desktop technology has seen significant growth in today’s datacenters. However, storage demands on highly utilized VDI servers can cause unacceptable response times. Can flash help virtual desktop environments achieve the best overall performance to improve end-user productivity while lowering total solution cost?
  • Flash caching – Oracle and storage vendors have started enhancing their products to take advantage of flash caching. How can database administrators implement caching technology running on Oracle® Linux with Oracle Unbreakable Enterprise Kernel, utilizing Oracle Database Smart Flash Cache?
  • Software Defined Networks (SDN) – SDNs promise to make networks more flexible, easier to manage, and programmable. How and why are businesses using SDNs today?  
  • Big data analytics – Gathering, interpreting and correlating multiple data streams as they are created can enhance real-time decision making for industries like financial trading, national security, consumer marketing, and network security. How can specialized silicon greatly reduce the compute power necessary, and make the “real-time” part of real-time analytics possible?
  • Sharable DAS – Datacenters of all sizes are struggling to provide high performance and 24/7 uptime, while reducing TCO. How can DAS-based storage sharing and scaling help meet the growing need for reduced cost and greater ease of use, performance, agility and uptime?
  • 12Gb/s SAS – Applications such as Web 2.0/cloud infrastructure, transaction processing and business intelligence are driving the need for higher-performance storage. How can 12Gb/s SAS meet today’s high-performance challenges for IOPS and bandwidth while providing enterprise-class features, technology maturity and investment protection, even with existing storage devices?

And, I think you’ll find some astounding products, demos, proof of concepts and future solutions in the showcase too – not just from LSI but from partners and fellow travelers in this industry. Hey – that’s my favorite part. I can’t wait to see people’s reactions.

Since they rethought how to do business in 2002, NASA has embarked on nearly 60 Mars missions. Faster, better, cheaper. It can work here in IT too.

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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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Optimizing the work per dollar spent is a high priority in datacenters around the world. But there aren’t many ways to accomplish that. I’d argue that integrating flash into the storage system drives the best – sometimes most profound – improvement in the cost of getting work done.

Yea, I know work/$ is a US-centric metric, but replace the $ with your favorite currency. The principle remains the same.

I had the chance to talk with one of the execs who’s responsible for Google’s infrastructure last week. He talked about how his fundamental job was improving performance/$. I asked about that, and he explained “performance” as how much work an application could get done. I asked if work/$ at the application was the same, and he agreed – yes – pretty much.

You remember as a kid that you brought along a big brother as authoritative backup? OK – so my big brother Google and I agree – you should be trying to optimize your work/$. Why? Well – it could be to spend less, or to do more with the same spend, or do things you could never do before, or simply to cope with the non-linear expansion in IT demands even as budgets are shrinking. Hey – that’s the definition of improving work/$… (And as a bonus, if you do it right, you’ll have a positive green impact that is bound to be worth brownie points.)

Here’s the point. Processors are no longer scaling the same – sure, there are more threads, but not all applications can use all those threads. Systems are becoming harder to balance for efficiency. And often storage is the bottleneck. Especially for any application built on a database. So sure – you can get 5% or 10% gain, or even in the extreme 100% gain in application work done by a server if you’re willing to pay enough and upgrade all aspects of the server: processors, memory, network… But it’s almost impossible to increase the work of a server or application by 200%, 300% or 400% – for any money.

I’m going to explain how and why you can do that, and what you get back in work/$. So much back that you’ll probably be spending less and getting more done. And I’m going to explain how even for the risk-averse, you can avoid risk and get the improvements.

More work/$ from general-purpose DAS servers and large databases
Let me start with a customer. It’s a bank, and it likes databases. A lot. And it likes large databases even more. So much so that it needs disks to hold the entire database. Using an early version of an LSI Nytro™ MegaRAID® card, it got 6x the work from the same individual node and database license. You can read that as 600% if you want. It’s big. To be fair – that early version had much more flash than our current products, and was much more expensive. Our current products give much closer to 3x-4x improvement. Again, you can think of that as 300%-400%. Again, slap a Nytro MegaRAID into your server and it’s going to do the work of 3 to 4 servers. I just did a web search and, depending on configuration, Nytro MegaRAIDs are $1,800 to $2,800 online. I don’t know about you, but I would have a hard time buying 2 to 3 configured servers + software licenses for that little, but that’s the net effect of this solution. It’s not about faster (although you get that). It’s about getting more work/$.

But you also want to feel safe – that you’re absolutely minimizing risk. OK. Nytro MegaRAID is a MegaRAID card. That’s overwhelmingly the most common RAID controller in the world, and it’s used by 9 of the top 10 OEMs, and protects 10’s to 100‘s of millions of disks every day. The Nytro version adds private flash caching in the card and stores hot reads and writes there. Writes to the cache use a RAID 1 pair. So if a flash module dies, you’re protected. If the flash blocks or chip die wear out, the bad blocks are removed from the cache pool, and the cache shrinks by that much, but everything keeps operating – it’s not like a normal LUN that can’t change size. What’s more, flash blocks usually finally wear out during the erase cycle – so no data is lost.  And as a bonus, you can eliminate the traditional battery most RAID cards use – the embedded flash covers that – so no more annual battery service needed. This is a solution that will continue to improve work/$ for years and years, all the while getting 3x-4x the work from that server.

More work/$ from SAN-attached servers (without actually touching the SAN)
That example was great – but you don’t use DAS systems. Instead, you use a big iron SAN. (OK, not all SANs are big iron, but I like the sound of that expression.) There are a few ways to improve the work from servers attached to SANs. The easiest of course is to upgrade the SAN head, usually with a flash-based cache in the SAN controller. This works, and sometimes is “good enough” to cover needs for a year or two. However, the server still needs to reach across the SAN to access data, and it’s still forced to interact with other servers’ IO streams in deeper queues. That puts a hard limit on the possible gains. 

Nytro XD caches hot data in the server. It works with virtual machines. It intercepts storage traffic at the block layer – the same place LSI’s drivers have always been. If the data isn’t hot, and isn’t cached, it simply passes the traffic through to the SAN. I say this so you understand – it doesn’t actually touch the SAN. No risk there. More importantly, the hot storage traffic never has to be squeezed through the SAN fabric, and it doesn’t get queued in the SAN head. In other words, it makes the storage really, really fast.

We’ve typically found work from a server can increase 5x to 10x, and that’s been verified by independent reviewers. What’s more, the Nytro XD solution only costs around 4x the price of a high-end SAN NIC. It’s not cheap, but it’s way cheaper than upgrading your SAN arrays, it’s way cheaper than buying more servers, and it’s proven to enable you to get far more work from your existing infrastructure. When you need to get more work – way more work – from your SAN, this is a really cost-effective approach. Seriously – how else would you get 5x-10x more work from your existing servers and software licenses?

More work/$ from databases
A lot of hyperscale datacenters are built around databases of a finite size. That may be 1, 2 or even 4 TBytes. If you use Apple’s online services for iTunes or iCloud, or if you use Facebook, you’re using this kind of infrastructure.

If your datacenter has a database that can fit within a few TBytes (or less), you can use the same approach. Move the entire LUN into a Nytro WarpDrive® card, and you will get 10x the work from your server and database software. It makes such a difference that some architects argue Facebook and Apple cloud services would never have been possible without this type of solution. I don’t know, but they’re probably right. You can buy a Nytro WarpDrive for as little as a low-end server. I mean low end. But it will give you the work of 10. If you have a fixed-size database, you owe it to yourself to look into this one.

More work/$ from virtualized and VDI (Virtual Desktop) systems
Virtual machines are installed on a lot of servers, for very good reason. They help improve the work/$ in the datacenter by reducing the number of servers needed and thereby reducing management, maintenance and power costs. But what if they could be made even more efficient?

Wall Street banks have benchmarked virtual desktops. They found that Nytro products drive these results: support of 2x the virtual desktops, 33% improvement in boot time during boot storms, and 33% lower cost per virtual desktop. In a more general application mix, Nytro increases work per server 2x-4x.  And it also gives 2x performance for virtual storage appliances.

While that’s not as great as 10x the work, it’s still a real work/$ value that’s hard to ignore. And it’s the same reliable MegaRAID infrastructure that’s the backbone of enterprise DAS storage.

A real example from our own datacenter
Finally – a great example of getting far more work/$ was an experiment our CIO Bruce Decock did. We use a lot of servers to fuel our chip-design business. We tape out a lot of very big leading-edge process chips every year. Hundreds.  And that takes an unbelievable amount of processing to get what we call “design closure” – that is, a workable chip that will meet performance requirements and yield. We use a tool called PrimeTime that figures out timing for every signal on the chip across different silicon process points and operating conditions. There are 10’s to 100’s of millions of signals. And we run every active design – 10’s to 100’s of chips – each night so we can see how close we’re getting, and we make multiple runs per chip. That’s a lot of computation… The thing is, electronic CAD has been designed to try not to use storage or it will never finish – just /tmp space, but CAD does use huge amounts of memory for the data structures, and that means swap space on the order of TBytes. These CAD tools usually don’t need to run faster. They run overnight and results are ready when the engineers come in the next day. These are impressive machines: 384G or 768G of DRAM and 32 threads.  How do you improve work/$ in that situation? What did Bruce do?

He put LSI Nytro WarpDrives in the servers and pointed /tmp at the WarpDrives. Yep. Pretty complex. I don’t think he even had to install new drivers. The drivers are already in the latest OS distributions. Anyway – like I said – complex.

The result? WarpDrive allowed the machines to fully use the CPU and memory with no I/O contention. With WarpDrive, the PrimeTime jobs for static timing closure of a typical design could be done on 15 vs. 40 machines. That’s each Nytro node doing 260% of the work vs. a normal node and license. Remember – those are expensive machines (have you priced 768G of DRAM and do you know how much specialized electronic design CAD licenses are?) So the point wasn’t to execute faster. That’s not necessary. The point is to use fewer servers to do the work. In this case we could do 11 runs per server per night instead of just 4. A single chip design needs more than 150 runs in one night.

To be clear, the Nytro WarpDrives are a lot less expensive than the servers they displace. And the savings go beyond that – less power and cooling. Lower maintenance. Less admin time and overhead. Fewer Licenses.  That’s definitely improved work/$ for years to come. Those Nytro cards are part of our standard flow, and they should probably be part of every chip company’s design flow.

So you can improve work/$ no matter the application, no matter your storage model, and no matter how risk-averse you are.

Optimizing the work per dollar spent is a high – maybe the highest – priority in datacenters around the world. And just to be clear – Google agrees with me. There aren’t many ways to accomplish that improvement, and almost no ways to dramatically improve it. I’d argue that integrating flash into the storage system is the best – sometimes most profound – improvement in the cost of getting work done. Not so much the performance, but the actual work done for the money spent. And it ripples through the datacenter, from original CapEx, to licenses, maintenance, admin overhead, power and cooling, and floor space for years. That’s a pretty good deal. You should look into it.

For those of you who are interested, I already wrote about flash in these posts:
What are the driving forces behind going diskless?
LSI is green – no foolin’

 

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I am sitting in the terminal waiting for my flight home from – yes, you guessed it – China. I am definitely racking up frequent flier miles this year.

This trip ended up centering on resource pooling in the datacenter. Sure, you might hear a lot about disaggregation, but the consensus seems to be: that’s the wrong name (unless you happen to make standalone servers). For anyone else, it’s about a much more flexible infrastructure, simplified platforms, better lifecycle management, and higher efficiency. I call it “resource pooling,” which is descriptive, but others simply call it rack scale architecture.

It’s been a long week, but very interesting. I was asked to keynote at the SACC conference (Systems Architect Conference China) in Beijing. It was also a great chance to meet 1-on-1 with the CTOs and chief architects from the big datacenters, and visit for a few hours with other acquaintances. I even had the chance to have dinner with the CEO /CIO China Magazine editor in chief, and CIO’s from around Beijing. As always in life, if you’re willing to listen, you can learn a lot. And I did.
Thinking on disaggregation aligns
With CTOs, there was a lot of discussion about disaggregation in the datacenter. There is a lot of aligned thinking on the topic, and it’s one of those occasions where you had to laugh because I think anyone of the CTOs keynoting could have given anyone else’s presentation. So what’s the big deal? Resource pooling and rack scale architecture.

I’ll use this trip as an excuse to dig a little deeper into my view on what this means.

First – you need to understand where these large datacenters are in their evolution. They usually have 4 to 6 platforms and2 or 3 generations of each in the datacenter. That can be 18 different platforms to manage, maintain, and tune. Worse – they have to plan 6 to 9 months in advance to deploy equipment. If you guess wrong, you’ve got a bunch of useless equipment, and you spent a bunch of money – the size of mistake that will get you fired… And even if you get it right, you’re left with the problem – Do I upgrade servers when the CPU is new? Or at, say, 18 months? Or do I wait until the biggest cost item – the drives – need to be replaced in 4 or 5 years? That’s difficult math. So resource pooling is about lifecycle management of different types of components and sub-systems. You can optimally replace each resource on its own schedule.

Increasing resource utilization and efficiency
But it’s also about resource utilization and efficiency. Datacenters have multiple platforms because each platform needs a different configuration of resources. I use the term configuration on purpose. If you have storage in your server, it’s in some standard configuration – say, 6 3 TByte drives or 18 raw TBytes. Do you use all that capacity? Or do you leave some space so databases can grow? Of course you leave empty space. You might not even have any use for that much storage in that particular server – maybe you just use half the capacity. After all, it’s a standard configuration. What about disk bandwidth? Can your Hadoop node saturate 6 drives? Probably. It could probably use 12 or maybe even 24. But sorry – it’s a standard configuration. What about latency-sensitive databases? Sure, I can plug a PCIe card in, but I only have 1.6 TByte PCIe cards as my standard configuration. My database is 1.8 TBytes and growing. Sorry – you have to refactor and put on 2 servers. Or my database is only 1 TByte. I’m wasting 600 GBytes of really expensive resource.

Meeting with Shiding Lin, Chief Architect at Baidu.

For network resources – the standard configuration gets maybe exactly 1 10GE port. You need more? Can’t have it. You don’t need that much? Sorry – wasted bandwidth capacity. What about standard memory? You either waste DRAM you don’t use, or you starve for more DRAM you can’t get.

But if I have pools of rack scale resources that I can allocate to a standard compute platform – well – that’s a different story. I can configure exactly the amount of network bandwidth, memory, flash high- performance storage, and disk bulk storage. I can even add more configured storage if a database grows, instead of being forced to refactor a database into shards across multiple standard configurations.

Pooling resources = simplified operations
So the desire to pool resources is really as much about simplified operations as anything else. I can have standardized modules that are all “the same” to manage, but can be resource configured into a well-tailored platform that can even change over time.

But pooling is also about accommodating how the application architectures have changed, and how much more important dataflow is than compute for so much of the datacenter. As a result there is a lot of uncertainty about how parts of these rack scale architectures and interconnect will evolve, even as there is a lot of certainty that they will evolve, and they will include pooled resource “modules.” Whatever the overall case, we’re pretty sure we understand how the storage will evolve. And at a high level, that’s what I presented in my keynote. (Hey – I’m not going to publicly share all our magic!)

One storage architecture of pooled resources at the rack scale level. One storage architecture that combines boot management, flash storage for performance, and disk storage for efficient bandwidth and capacity. And those resources can be allocated however and whenever the datacenter manager needs them. And the existing software model doesn’t need to change. Existing apps, OS’s, file systems, and drivers are all supported, meaning a change to pooled resource rack scale deployments is de-risked dramatically. Overall, this one architecture simplifies the number of platforms, simplifies the management of platforms, utilizes the resources very efficiently, and simplifies image and boot management.  I’m pretty sure it even reduces datacenter-level CapEx. I know it dramatically reduces OpEx.

Keynote slide: Single Pooled Storage Architecture Summary. Capture, Hold, Analyze refer to a framework for the 3 stages of Big Data usage as a theme throughout the presentation.

Yea – I know what you’re thinking – it’s awesome ! (That’s what you thought – right?)

Oh – what about those CIO meetings? Well, there is tremendous pressure to not buy American IT equipment in China because of all the news from the Snowden NSA leaks. As most of the CIO’s pointed out, though, in today’s global sourcing market, it’s pretty hard to not buy US IT equipment. So they’re feeling a bit trapped. In a no-risk profession, I suspect that means they just won’t buy anything for a year or so and hope it blows over.

But in general, yep, I think this trip was centered on resource pooling in the datacenter. Sure, you might hear about disaggregation, but there’s a lot of agreement that’s the wrong name. It’s much more about resource pooling for flexible infrastructure, simplified platforms, better lifecycle management, and higher efficiency. And we aim to be right in the middle. Literally.

 

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