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

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

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

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

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

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

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

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

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

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

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

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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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