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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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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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High Availability (HA) systems traditionally have been confined to large datacenters because of their high cost and the difficulty of scaling down clustered servers and shared storage arrays to support smaller environments such as Small Office Home Office (SOHO) and Remote Business Office (ROBO).

Microsoft and LSI are changing that.

As part of Windows Server® 2012, Microsoft and LSI collaborated on the development of the innovation called Cluster in a Box (CiB). With CiB, HA systems are now available for SOHO and ROBO applications. At AIS, we’re demonstrating our Syncro® CS High Availability controller in a clustered server system.

Our demo shows how Syncro CS, with its easy-to-deploy yet powerful automatic failover, helps protect and provide cost-effective continuous access and availability to your valuable data. The solution now supports both Linux® and Windows® OS environments.

Last June, we launched Syncro CS solutions with a demo at the Microsoft® Tech Ed Conference.  The demo featured a Syncro CS discrete server cluster using two servers, two Syncro CS controllers and a JBOD.  Each server was loaded with Windows Server 2012 in a cluster.  Syncro CS controllers enabled the shared storage.  The entire system was interconnected with a “backbone communications” system provided through a SAS interface.  Each server was running Microsoft Server 2012 Hyper-V with a virtual machine (VM) housing Counterstrike 1.6 server.  Basically, the Counterstrike server was built into a Syncro CS High Availability server cluster.  Clients accessed the game with Microsoft Surface Tablets.

When one of the servers was turned off, the automatic failover engaged and the tablet users never were aware of the “failure.”

Our AIS demo uses RHEL 6.4 Linux as the native OS running the KVM hypervisor with a Windows Server 2012 VM.  Microsoft Surface Tablets access the Counterstrike server housed in the Windows VM.

The demonstration highlights the option for administrators to use Linux as the native operating system for each server while running Windows applications in HA architectures.

At AIS, we’re also excited about our panel discussion “Delivering a Paradigm of High Availability” featuring several industry experts and thought leaders.  On the panel are Michael Steineke, VP Information Technology, Edgenet; Trenton Baker, VP Business Development, DataOn; Gene Lee, CEO, EchoStreams; John Loveall, Principal Program Manager, Microsoft Windows Server, Microsoft Corporation; Greg Kleiman, Director Strategy, Storage Business Unit, Red Hat; and Rick Reisner, Product Line Director, Datacenter Solutions Group, LSI.

The panel will discuss market needs for HA storage, offer their perspectives on product deployment, and discuss potential future HA use cases and product developments.

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In preparation for the development of Windows Server® 2012, Microsoft polled customers and found that features that make high availability easier to configure and more affordable are critical. Little wonder. The features are pennies from heaven to the vast universe of smaller IT shops that often have found traditional high-availability solutions too expensive and difficult to install and maintain.

In a recent video, John Loveall, principal program manager for the Windows Server Division of Microsoft, discusses how Microsoft® Windows Server 2012 and the LSI® Syncro™ CS solution can make it easier for organizations of all sizes to deploy high availability.

While large organizations remain a vital proving ground for new breeds of computer gear, Loveall sees small businesses, branch offices and private cloud environments using high-availability systems as a window into the future of server technology.

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When I am out on the road in Europe, visiting customers and partners, one common theme that comes up on a daily basis is that high-availability systems are essential to nearly all businesses regardless of size or industry. Sadly, all too often we see what can happen when systems running business-critical applications such as transaction processing, Web servers or electronic commerce are not accessible – potentially lost revenue and lost productivity, leading to dramatically downward-spiralling customer satisfaction.

To reduce this risk, the industry focus has been on achieving the best level of high availability, and for the enterprise market segment this has often meant installing and running storage area network (SAN) solutions. SANs can offer users a complete package – scalability, performance, centralised management and the all-important uptime or high availability.

Drawbacks of SAN
But for all its positives, the SAN also has its downsides. To ensure continuous application availability, server clustering and shared-node connections that build redundancy into a cluster and eliminate single points of failure are crucial. The solution is not only extremely complex, it can have a hefty price tag, amounting to tens of thousands of dollars, and can be hard for many smaller to medium-sized businesses to afford.

When considering budgets and  storage needs, many businesses have shied away from investing in a SAN and opted for a far simpler direct attached storage (DAS) solution – mainly because it can be  far easier to implement and considerably cheaper. Historically, however, the biggest problem with this was that DAS could not offer high availability, and recovery from a server or storage failure could take several hours or even days.

Combining the simplicity of DAS with the high availability of SAN storage
As businesses work to reduce storage costs, simplify deployment, and increase agility and uptime in the face of massive data growth, storage architects are often looking for a way to combine the best of both worlds: the simplicity of DAS storage and the high availability of SAN storage. The goal for many is to create a system that is not only cheaper than a regular SAN but also offers full redundancy, less management complexity and guarantees uptime for the business in case a server goes down.

LSI has pioneered an HA-DAS solution, Syncro™ CS, that costs approximately 30% less than traditional HA entry-level SAN solutions, depending on the solution/configuration. It reduces complexity by providing fully redundant, shared-node storage and application failover, without requiring storage networking hardware. Syncro CS solutions are also designed to reduce latency compared to SAN-based solutions, helping to accelerate storage I/O performance and speed applications.

The good news for businesses that rely on DAS is that they have an option, Syncro CS, to now more easily upgrade their DAS infrastructure to help achieve high availability, with easier management and lower cost. The result is a much simpler failover solution that  provides more affordable business continuity and reduces downtime.

 

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