Ever since SandForce introduced data reduction technology with the DuraWrite™ feature in 2009, some users have been confused about how it works and questioned whether it delivers the benefits we claim. Some even believe there are downsides to using DuraWrite with an SSD. In this blog, I will dispel those misconceptions.
Data reduction technology refresher
Four of my previous blogs cover the many advantages of using data reduction technology like DuraWrite:
In a nutshell, data reduction technology reduces the size of data written to the flash memory, but returns 100% of the original data when reading it back from the flash. This reduction in the required storage space helps accelerate reads and writes, extend the life of the flash and increase the dynamic over provisioning (OP).
What is incompressible data?
Data is incompressible when data reduction technology is unable to reduce the size of a dataset – in which case the technology offers no benefit for the user. File types that are altogether or mostly incompressible include MPEG, JPEG, ZIP and encrypted files. However, data reduction technology is applied to an entire SSD, so the free space resulting from the smaller, compressed files increases OP for all file types, even incompressible files.
The images below help illustrate this process. The image on the left represents a standard SSD 256GB SSD filled to about 80% capacity with a typical operating system, applications and user data. The remaining 20% of free space is automatically used by the SSD as dynamic OP. The image on the right shows how the same data stored on a data reduction-capable SSD can nearly double the available OP for the SSD because the operating system, applications and half of the user data can be reduced in this example.
Why is dynamic OP so important?
OP is the lifeblood of a flash memory-based SSD (nearly all of them available today). Without OP the SSD could not operate. Allocating more space for OP increases an SSD’s performance and endurance, as well as reduces it power consumption. In the illustrations above, both SSDs are storing about 30% of user data as incompressible files like MPEG movies and JPG images. As I mentioned, data reduction technology can’t compress those files, but the rest of the data can be reduced. The result is the SSD with data reduction delivers higher overall performance than the standard SSD even with incompressible data.
Misconception 1: Data reduction technology is a trick
There’s no trickery with data reduction technology. The process is simple: It reduces the size of data differently depending on the content, increasing SSD speed and endurance.
Misconception 2: Users with movie, picture, and audio files will not benefit from data reduction
As illustrated above, as long as an operating system and other applications are stored on the SSD, there will be at least some increase in dynamic OP and performance despite the incompressible files.
Misconception 3: Testing with all incompressible data delivers worst-case performance
Given that a typical SSD stores an operating system, programs and other data files, an SSD test that writes only incompressible data to the device would underestimate the performance of the SSD in user deployments.
Data reduction technology delivers
Data reduction technology, like LSI® SandForce® DuraWrite, is often misunderstood to the point that users believe they would be better off without it. The truth is, with data reduction technology, nearly every user will see performance and endurance gains with their SSD regardless of how much incompressible data is stored.
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.
Tags: Alibaba, Amazon, Baidu, CEO & CIO Magazine, China, cloud services, cold storage, D-DAS, DAS, datacenter, datacenter ecosystem, direct attached storage, distributed DAS, Facebook, flash, flash storage, Google, HBA, host bus adapter, hyperscale datacenter, Internet, JD.com, MegaRAID, OEM, original equipment manufacturer, Scorpio, Tencent
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
Last week at LSI’s annual Accelerating Innovation Summit (AIS) the company took the wraps off a vision that should lead its technical direction for the next few years.
The LSI keynote featured a video of three situations as they might evolve in the future:
I’ll focus on just one of these to show how LSI expects the future to develop. In the bicycle accident scenario, a businessman falls to the ground while riding a bicycle in a foreign country. Security cameras that have been upgraded to understand what they see notify an emergency services agency which sends an ambulance to the scene. The paramedic performs a retinal scan on the victim, using it to retrieve his medical records, including his DNA sequence, from the web.
The businessman’s wearable body monitoring system also communicates with the paramedic’s instruments to share his vital signs. All of this information is used by cloud-based computers to determine a course of action which, in the video, requires an injection that has been custom-tuned to the victim’s current situation, his medical history, and his genetic makeup.
That’s a pretty tall order, and it will require several advances in the state of the art, but LSI is using this and other scenarios to work with its clients and translate this vision into the products of the future.
What are the key requirements to make this happen? Talwalkar told the audience that we need to create a society that is supported by preventive, predictive and assisted analytics to move in a direction where the general welfare is assisted by all that the Internet and advanced computing have to offer. Since data is growing at an exponential rate, he argued that this will require the instant retrieval of interlinked data objects at scale. Everything that is key to solving the task must be immediately available, and must be quickly analyzed to provide a solution to the problem at hand. The key will be the ability to process interlinked pieces of data that have not been previously structured to handle any particular situation.
To achieve this we will need larger-scale computing resources than are currently available, all closely interconnected, that all operate at very high speeds. LSI hopes to tap into these needs through its strengths in networking and communications chips for the communications, its HDD and server and storage connectivity array chips and boards for large-scale data, and its flash controller memory and PCIe SSD expertise for high performance.
LSI brought to AIS several of the customers and partners it is working with using to develop these technologies. Speakers from Intel, Microsoft, IBM, Toshiba, Ericsson and others showed how they are working with LSI’s various technologies to improve the performance of their own systems. On the exhibition floor booths from LSI and many of its clients demonstrated new technologies that performed everything from high-speed stock market analysis to fast flash management.
It’s pretty exciting to see a company that has a clear vision of its future and is committed to moving its entire ecosystem ahead to make that happen and help companies manage their business more effectively during what LSI calls the “Datacentric Era.” LSI has certainly put a lot of effort into creating a vision and determining where its talents can be brought to bear to improve our lives in the future.
Tags: AIS, chips, communications, connectivity, data, Datacentric Era, Ericsson, flash, flash memory, hard disk drive, HDD, IBM, Intel, large-scale data, Microsoft, Networking, server, Storage, Toshiba
The lifeblood of any online retailer is the speed of its IT infrastructure. Shoppers aren’t infinitely patient. Sluggish infrastructure performance can make shoppers wait precious seconds longer than they can stand, sending them fleeing to other sites for a faster purchase. Our federal government’s halting rollout of the Health Insurance Marketplace website is a glaring example of what can happen when IT infrastructure isn’t solid. A few bad user experiences that go viral can be damaging enough. Tens of thousands can be crippling.
In hyperscale datacenters, any number of problems including network issues, insufficient scaling and inconsistent management can undermine end users’ experience. But one that hits home for me is the impact of slow storage on the performance of databases, where the data sits. With the database at the heart of all those online transactions, retailers can ill afford to have their tier of database servers operating at anything less than peak performance.
Slow storage undermines database performance
Typically, Web 2.0 and e-commerce companies run relational databases (RDBs) on these massive server-centric infrastructures. (Take a look at my blog last week to get a feel for the size of these hyperscale datacenter infrastructures). If you are running that many servers to support millions of users, you are likely using some kind of open-sourced RDB such as MySQL or other variations. Keep in mind that Oracle 11gR2 likely retails around $30K per core but MSQL is free. But the performance of both, and most other relational databases, suffer immensely when transactions are retrieving data from storage (or disk). You can only throw so much RAM and CPU power at the performance problem … sooner rather than later you have to deal with slow storage.
Almost everyone in industry – Web 2.0, cloud, hyperscale and other providers of massive database infrastructures – is lining up to solve this problem the best way they can. How? By deploying flash as the sole storage for database servers and applications. But is low-latency flash enough? For sheer performance it beats rotational disk hands down. But … even flash storage has its limitations, most notably when you are trying to drive ultra-low latencies for write IOs. Most IO accesses by RDBs, which do the transactional processing, are a mix or read/writes to the storage. Specifically, the mix is 70%/30% reads/writes. These are also typically low q-depth accesses (less than 4). It is those writes that can really slow things down.
PCIe flash reduces write latencies
The good news is that the right PCIe flash technology in the mix can solve the slowdowns. Some interesting PCIe flash technologies designed to tackle this latency problem are on display at AIS this week. DRAM and in particular NVDRAM are being deployed as a tier in front of flash to really tackle those nasty write latencies.
Among other demos, we’re showing how a Nytro™ 6000 series PCIe flash card helps solve the MySQL database performance issues. The typical response time for a small data read (this is what the database will see for a Database IO) from an HDD is 5ms. Flash-based devices such as the Nytro WarpDrive® card can complete the same read in less than 50μs on average during testing, an improvement of several orders-of-magnitude in response time. This response time translates to getting much higher transactions out of the same infrastructure – but with less space (flash is denser) and a lot less power (flash consumes a lot lower power than HDDs).
We’re also showing the Nytro 7000 series PCIe flash cards. They reach even lower write latencies than the 6000 series and very low q-depths. The 7000 series cards also provide DRAM buffering while maintaining data-integrity even in the event of a power loss.
For online retailers and other businesses, higher database speeds mean more than just faster transactions. They can help keep those cash registers ringing.
Tags: AIS, database, DRAM, e-commerce, flash, flash memory, hard disk drive, HDD, hyperscale datacenter, latency, MySQL, NVDRAM, Nytro 6000, Nytro 7000, Nytro WarpDrive, Oracle, PCIe flash, relational database, storage latency, web 2.0, write latency
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:
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.
Tags: 12Gb/s SAS, AIS, big data analytics, cloud infrastructure, cloud services, datacenter, flash, flash memory, hyperscale datacenters, NAS, NASA, SAN, SDN, shareable DAS, software-defined networks, sub-20nm flash, triple-level cell flash, VDI, web 2.0
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’
Tags: Bruce Decock, DAS, datacenter, direct attached storage, enterprise IT, flash, Google, hyperscale datacenter, Nytro MegaRAID, Nytro WarpDrive, Nytro XD, PrimeTime, RAID, SAN, server storage, storage area network, VDI, virtual desktop infrastructure, work per dollar
I want to warn you, there is some thick background information here first. But don’t worry. I’ll get to the meat of the topic and that’s this: Ultimately, I think that PCIe® cards will evolve to more external, rack-level, pooled flash solutions, without sacrificing all their great attributes today. This is just my opinion, but other leaders in flash are going down this path too…
I’ve been working on enterprise flash storage since 2007 – mulling over how to make it work. Endurance, capacity, cost, performance have all been concerns that have been grappled with. Of course the flash is changing too as the nodes change: 60nm, 50nm, 35nm, 24nm, 20nm… and single level cell (SLC) to multi level cell (MLC) to triple level cell (TLC) and all the variants of these “trimmed” for specific use cases. The spec “endurance” has gone from 1 million program/erase cycles (PE) to 3,000, and in some cases 500.
It’s worth pointing out that almost all the “magic” that has been developed around flash was already scoped out in 2007. It just takes a while for a whole new industry to mature. Individual die capacity increased, meaning fewer die are needed for a solution – and that means less parallel bandwidth for data transfer… And the “requirement” for state-of-the-art single operation write latency has fallen well below the write latency of the flash itself. (What the ?? Yea – talk about that later in some other blog. But flash is ~1500uS write latency, where state of the art flash cards are ~50uS.) When I describe the state of technology it sounds pretty pessimistic. I’m not. We’ve overcome a lot.
We built our first PCIe card solution at LSI in 2009. It wasn’t perfect, but it was better than anything else out there in many ways. We’ve learned a lot in the years since – both from making them, and from dealing with customer and users – about our own solutions and our competitors. We’re lucky to be an important player in storage, so in general the big OEMs, large enterprises and the hyperscale datacenters all want to talk with us – not just about what we have or can sell, but what we could have and what we could do. They’re generous enough to share what works and what doesn’t. What the values of solutions are and what the pitfalls are too. Honestly? It’s the hyperscale datacenters in the lead both practically and in vision.
If you haven’t nodded off to sleep yet, that’s a long-winded way of saying – things have changed fast, and, boy, we’ve learned a lot in just a few years.
Most important thing we’ve learned…
Most importantly, we’ve learned it’s latency that matters. No one is pushing the IOPs limits of flash, and no one is pushing the bandwidth limits of flash. But they sure are pushing the latency limits.
PCIe cards are great, but…
We’ve gotten lots of feedback, and one of the biggest things we’ve learned is – PCIe flash cards are awesome. They radically change performance profiles of most applications, especially databases allowing servers to run efficiently and actual work done by that server to multiply 4x to 10x (and in a few extreme cases 100x). So the feedback we get from large users is “PCIe cards are fantastic. We’re so thankful they came along. But…” There’s always a “but,” right??
It tends to be a pretty long list of frustrations, and they differ depending on the type of datacenter using them. We’re not the only ones hearing it. To be clear, none of these are stopping people from deploying PCIe flash… the attraction is just too compelling. But the problems are real, and they have real implications, and the market is asking for real solutions.
Of course, everyone wants these fixed without affecting single operation latency, or increasing cost, etc. That’s what we’re here for though – right? Solve the impossible?
A quick summary is in order. It’s not looking good. For a given solution, flash is getting less reliable, there is less bandwidth available at capacity because there are fewer die, we’re driving latency way below the actual write latency of flash, and we’re not satisfied with the best solutions we have for all the reasons above.
If you think these through enough, you start to consider one basic path. It also turns out we’re not the only ones realizing this. Where will PCIe flash solutions evolve over the next 2, 3, 4 years? The basic goals are:
One easy answer would be – that’s a flash SAN or NAS. But that’s not the answer. Not many customers want a flash SAN or NAS – not for their new infrastructure, but more importantly, all the data is at the wrong end of the straw. The poor server is left sucking hard. Remember – this is flash, and people use flash for latency. Today these SAN type of flash devices have 4x-10x worse latency than PCIe cards. Ouch. You have to suck the data through a relatively low bandwidth interconnect, after passing through both the storage and network stacks. And there is interaction between the I/O threads of various servers and applications – you have to wait in line for that resource. It’s true there is a lot of startup energy in this space. It seems to make sense if you’re a startup, because SAN/NAS is what people use today, and there’s lots of money spent in that market today. However, it’s not what the market is asking for.
Another easy answer is NVMe SSDs. Right? Everyone wants them – right? Well, OEMs at least. Front bay PCIe SSDs (HDD form factor or NVMe – lots of names) that crowd out your disk drive bays. But they don’t fix the problems. The extra mechanicals and form factor are more expensive, and just make replacing the cards every 5 years a few minutes faster. Wow. With NVME SSDs, you can fit fewer HDDs – not good. They also provide uniformly bad cooling, and hard limit power to 9W or 25W per device. But to protect the storage in these devices, you need to have enough of them that you can RAID or otherwise protect. Once you have enough of those for protection, they give you awesome capacity, IOPs and bandwidth, too much in fact, but that’s not what applications need – they need low latency for the working set of data.
What do I think the PCIe replacement solutions in the near future will look like? You need to pool the flash across servers (to optimize bandwidth and resource usage, and allocate appropriate capacity). You need to protect against failures/errors and limit the span of failure, commit writes at very low latency (lower than native flash) and maintain low latency, bottleneck-free physical links to each server… To me that implies:
That means the performance looks exactly as if each server had multiple PCIe cards. But the capacity and bandwidth resources are shared, and systems can remain resilient. So ultimately, I think that PCIe cards will evolve to more external, rack level, pooled flash solutions, without sacrificing all their great attributes today. This is just my opinion, but as I say – other leaders in flash are going down this path too…
What’s your opinion?
Tags: DAS, datacenter, direct attached storage, enterprise IT, flash, hard disk drive, HDD, hyperscale, latency, NAS, network attached storage, NVMe, PCIe, SAN, solid state drive, SSD, storage area network
Big data and Hadoop are all about exploiting new value and opportunities with data. In financial trading, business and some areas of science, it’s all about being fastest or first to take advantage of the data. The bigger the data sets, the smarter the analytics. The next competitive edge with big data comes when you layer in flash acceleration. The challenge is scaling performance in Hadoop clusters.
The most cost-effective option emerging for breaking through disk-to-I/O bottlenecks to scale performance is to use high-performance read/write flash cache acceleration cards for caching. This is essentially a way to get more work for less cost, by bringing data closer to the processing. The LSI® Nytro™ product has been shown during testing to improve the time it takes to complete Hadoop software framework jobs up to a 33%.
Flash cache cards increase Hadoop application performance
Combining flash cache acceleration cards with Hadoop software is a big opportunity for end users and suppliers. LSI estimates that less than 10% of Hadoop software installations today incorporate flash acceleration1. This will grow rapidly as companies see the increased productivity and ROI of flash to accelerate their systems. And Hadoop software adoption is also growing fast. IDC predicts a CAGR of as much as 60% by 20162. Drivers include IT security, e-commerce, fraud detection and mobile data user management. Gartner predicts that Hadoop software will be in two-thirds of advanced analytics products by 20153. Many thousands of Hadoop software clusters are already deployed.
Where flash makes the most immediate sense is with those who have smaller clusters doing lots of in-place batch processing. Hadoop is purpose-built for analyzing a variety of data, whether structured, semi-structured or unstructured, without the need to define a schema or otherwise anticipate results in advance. Hadoop enables scaling that allows an unprecedented volume of data to be analyzed quickly and cost-effectively on clusters of commodity servers. Speed gains are about data proximity. This is why flash cache acceleration typically delivers the highest performance gains when the card is placed directly in the server on the PCI Express® (PCIe) bus.
Combining the best of flash and HDDs to drive higher performance and storage capacity
PCIe flash cache cards are now available with multiple terabytes of NAND flash storage, which substantially increases the hit rate. We offer a solution with both onboard flash modules and Serial-Attached SCSI (SAS) interfaces to enable high-performance direct-attached storage (DAS) configurations consisting of solid state and hard disk drive storage. This couples the low-latency performance benefits of flash with the capacity and cost-per-gigabyte advantages of HDDs.
To keep the processor close to the data, Hadoop uses servers with DAS. And to get the data even closer to the processor, the servers are usually equipped with significant amounts of random access memory (RAM). An additional benefit: Smart implementation of Hadoop and flash components can reduce the overall server footprint and simplify scaling, with some solutions enabling up to 128 devices to share a very high bandwidth interface. Most commodity servers provide 8 or less SATA ports for disks, reducing expandability.
Hadoop is great, but flash-accelerated Hadoop is best. It’s an effective way, as you work to extract full value from big data, to secure a competitive edge.