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GNAX.jpgGlobal Net Access (GNAX) provides a range of colocation and infrastructure-as-a-service (IaaS) solutions with a special focus on healthcare. With demand for services growing, the GNAX team decided to build a multi-tenant cloud environment that could provide the flexibility and security that healthcare customers require while delivering the density to support efficient growth. GNAX chose Intel® Xeon® processor 5600 series as the platform for the new environment. Built-in security capabilities help GNAX provide a multilayer approach to security without sacrificing performance. At the same time, the processors help GNAX maximize server density to support fast-growing environments while controlling power, cooling, and real estate costs.

 

“The fact that Intel and VMware have such a close relationship made it easy to select Intel Xeon processors,” explained Matt Mong, vice president of marketing for GNAX. “We knew we could go to market faster with our secure cloud offering, with fewer integration issues, by selecting the Intel platform.”

 

For all the details, read our new GNAX business success story. As always, you can find this one, and many others, on the Intel.com Business Success Stories for IT Managers page.

Whether it’s a small business or enterprise level, mission critical software and big data are both very important factors in any organization. This year, Intel had some interesting discussions surrounding business continuity, mission critical and big data. In case you missed it, here is some of the best content on those topics from 2011.

 

We Read:

 

Breakthrough Server Innovations and Economics on New Xeon E7 series Servers by Pauline Nist

 

Benchmarks vs. the Real World: Reality Check by Pauline Nist

 

Some Big Data Needs Big Memory (and Big Processing) by Mitchell Shults

 

Mission Critical Solutions at IDF by Pauline Nist

 

We Watched

 

Intel’s Mission Critical General Manager Pauline Nist and Jim Totton, VP Platform Business Unit of Red Hat, talked about the collaboration and value Intel and Red Hat bring to enterprise mission critical computing.

 

Industry expert analysts and executives from IBM, Microsoft, Oracle, SAP and Intel commented on new data security challenges in Mission Critical Software Ecosystem Innovation.

 

Patrick Buddenbaum of Intel’s Mission Critical team and Richard Jackson, Colfax International Technical Director, explained the benefits of transitioning to Intel Xeon processors for mission critical business needs.

 

Intel’s Pauline Nist and Juan Loaiza, senior vice president of systems technology at Oracle, discussed the key features of the Xeon processor families that help make Oracle Exadata a world class mission-critical database machine. 

 

We Listened

 

Big Data expert Mitch Shults talked with Allyson Klein about the history and future of mission critical computing.

 

Allyson Klein and Pauline Nist chatted about mission critical technology and Itanium, recorded live from IDF 2011.

 

 

It’s been a big year for mission critical software and big data. What do you think the next year will bring? Let us know what you think, and comment below!

 

 

Remember to follow @IntelITS for all the latest on Itanium, business inteligence, enterprise solutions, and all things mission critical from Intel.

When you think of 2011 in the tech world, there tends to be a related supercomputing event attached. ISC 2011, Supercomputing 2011, IDF 2011…2011 was all about events, events, events!  This year was also full of big HPC events at Intel. We delivered the initial development units of MIC, and took part in the announcement of the 10-petaflop system in Texas. As 2012 approaches, let’s reminisce on some of the HPC & Supercomputing highlights of 2011:

 

We read:


Intel MIC Scores 1st Home Run with 10 Petaflop “Stampede” Supercomputer by Joe Curley

 

Supercomputing 2011 Day 2: Knights Corner shown at 1TF Per Socket by John Hengeveld

 

Supercomputing 2011: Monday - Intel and the Top500 by John Hengeveld

 

Accelerating Open Science Research: A Collaboration with TACC by John Hengeveld

 

High Performance Computing Around the World – The Wandering Geek by John Hengeveld

 

Linking Efficiency and Performance: Another Look at the Latest Top500 and Green500 by Winston Saunders

 

We Watched:


At the High Performance Computing mega-briefing at IDF 2011, we talked about Exascale High Performance Computing.

 

Intel’s Director of High Performance Computing, John Hengeveld discussed Intel’s innovations in High Performance Computing and Intel Many Integrated Core Architecture.

 

We Listened to:


Just in time for Supercomputing 2011, John Hengeveld and Joe Curley talked about how HPC affects our world and New High Performance Computing Capabilities on Intel Chip Chat.

 

At IDF2011, James Reinders of Intel’s software group talked about Parallel Programming for HPC and Linux.

 

 

As we close the chapter on one year of high performance computing, we look forward to what the future brings. What are your predictions for the next year? How will High Performance Computing evolve? Let us know what you think and comment!

 

Remember to follow @IntelITS for the latest on all things supercomputing & HPC from Intel!

At the base of cloud infrastructure is virtualization..

 

With a physical approach, most of the challenges you may face include under-utilization of server resources, difficulty in protecting server availability, and dealing with disaster recovery. All of these problems are made easier with virtualization. However, due to the complexities associated with hypervisor management resources and the shared storage model, the largest challenge comes from storage management.

 

In a cloud environment, usually there are two approaches to design the storage solution: scale-up and scale-out, and how the adoption of each strategy will affect the overall cost, performance, availability, and scalability of the entire cloud solution.

 

Beside the fact that topology decision is a combination of functionalities, price, TCO and skill, the biggest differences between scale-out and scale–up topology is shown below:

 

Scale-out (SAN/NAS)

Scale-up (DAS/SAN/NAS)

Hardware scaling

Add commodity devices

Add faster, larger   devices

Hardware limits

Scale beyond device limits

Scale up to device limit

Availability, resiliency

Usually more

Usually less

Storage management complexity

More resources do manage, software   required

Less resources do manage

Span multiple geographic locations

Yes

No

 

Usually, scaling up an existing system often results in simpler storage management than with the scale-out approach, as the complexity of the underlying environment is reduced, or at least known. However, as you scale up a system, the performance may suffer due to increasing density of shared resources in this topology. With scale-out topology, the performance may increase due to the increased number of nodes where more CPU, memory, spindle and network interfaces are added with each node.

 

Storage is a key component in cloud computing. Now, there are a number of options based on the workload, as shown in the graphic below:

 

StorageWorkload.png

 

There isn’t a “one solution fits all” in a cloud environment. The architecture should be built to allow a separation of virtual machines from the physical layer, and a virtual storage topology that allows any virtual machine to connect to any storage in the network. These are both required for a strong cloud infrastructure.

 

Based on a typical private cloud environment, there are systems that require this solution due to high-speed transfers of small pages, such as databases with 8kb pages like those on OLTP business databases. The solution is also best in cases of large sequential access, such as backup and archive systems or systems with large sums of application data, like VM files and web content.

 

The virtual storage architecture should be connected to each node, and share the same connectivity method (e.g. TCP/IP) to support the cloud infrastructure. An example of this is shown in the image below.

 

StorageArchitecture.png

 

There are several ways to provide this connectivity, for example, iSCSI, NFS or even FCoE. These allow connectivity with legacy SAN based storage. In this environment, you can use tiers of storage, such as a tier of high-performance disks such as SSDs for faster IOPS, or disks with better GB/$ capacity, such as 10k rpm. While you are able to define a “balanced” tier, it is important that the scale-out storage management software allocates the data on most appropriated tier, as needed.

 

A common question about cloud computing infrastructure is should I use DAS? Booting from storage can be an option. For a virtualized environment where the hypervisor image is stored in the network and streamed to server during the boot process, it can narrow down the server costs and MTBF associated with regular local hard disks. However, in a big environment, the server that hosts the hypervisor image should be designed to support a boot storm from several machines to thousands of machines at the same time.

 

In a scale-out storage architecture where booting from the network is a requirement, a set of special NICs that support the iSCSI boot should be considered. Usually, it is 25% more expensive than a regular 10GbE NIC. However, if you would like to mitigate risks with a possible boot storm and at the same time improve reliability of server platform, adoption of local SSD for hypervisor can provide a higher MTBF and improve the MTTR.

 

Best Regards!

 

-Bruno Domingues

It’s time for the final migration.  You’ve completed a Proof of Concept and a rehearsal for the process but now you have to complete the process and move the actual production application while it remains in production. Regardless of the migration methodology you’ve chosen, if you don’t get a lump in your stomach at the start, you’ve got ice water running in your veins. I’ve heard this process compared to changing all the tires on an 18-wheeler while it is still running down the road.

 

All of the previous steps taken lead up to this event.  The initial analysis defined the scope of the migration.  The proof of concept determined what the scope really is, and defined all of the components involved in the migration.  Also, the proof of concept likely ported and proofed the components that could be rewritten or modified without impact on the production system.  The rehearsal provided a timeline for the migration, along with an estimate of the production outage that may need to occur.

 

With all this data, the application owners and those charged with the migration need to sit down and plan this final step.  Cold feet can’t stop this intrepid team. Resumes prepared and recruiters notified, the team pushes on.

 

The final step plan starts with targeting the possible outage at the most reasonable time. This is often a weekend or a late evening, depending upon how long the outage would last.  Often, holiday weekends are chosen  (I once did a migration over the fourth of July holiday).  Include in your plan for best case and worst case scenarios for the outage duration.

 

Start the migration by cleaning up the target server.  Set the BIOS to the optimal settings you determined during the POC.  Load the operating system and set the parameters. If you’re installing Linux, add any required packages and, of course, add sysstat.  Install any application systems that are required by the application you run. Tune these for the application and platform.

 

Begin the migration by setting up the application system.  If you are moving a database, you set up the database.  If you are hosting a custom application, install the source code and compile the executable.  Set up the network links for the final conversion.

 

As soon as all the preliminary tasks are done, begin the migration.  For a migration of a custom coded application, this could as simple as pointing the other applications dependent on it to the new host.  If a binary data store like a database is involved, then the migration can be a lot more complex.  But you know what to do -- you’ve found out the best method in the POC and practiced it in the migration rehearsal.

 

Once the data is moved over and the application is ready to run, if possible run the test harness against the new system.  Audit the database to be sure all the tables, indexes, procedures, views, referential integrity constraints, database links, synonyms, and other database objects are all there. Finally, the last step is opening it back up to production. Then, go get some sleep!

 

After one migration I went back to the hotel and slept hard. I’d had maybe 4 hours rest in 72 hours, and I was pretty bleary.  When I went in Monday morning, there were cars in the parking lot.

 

"Hurrah! It worked! I thought. The company wasn’t going to let any workers into the plant if the migration had failed, but here they were working.   Subsequent testing by the user team assembled by the company to proof the process showed a few glitches on Monday (and for a few more days) but those were quickly tackled by our QA team. The lesson here is that once you’re finished with the steps, you aren’t completely done. You still have to monitor the situation to ensure all is well.

 

However, the migration is now finished.  You’ve got an all new platform for the application.  The old one can be put out to pasture (but it’s probably not a good idea to use it to make a fishing reef, given all the chemicals in a computer system!)

*Please not a version of this blog first apeared as an Intel industry perspective on Data Center Knowledge as Tips for Simplifying Your Cloud Network.

 

 

"Ethernet is the backbone of the Cloud."

 

 

Bold statement? Not at all. Any data center, cloud or otherwise, depends on its Ethernet network to allow servers, storage systems, and other devices to talk to each other. No network means no data center. Today, as IT departments prepare to deploy internal cloud environments, it’s important for them to consider how network infrastructure choices will impact their cloud’s ability to meet its service level agreements (SLAs). Terms commonly used to describe cloud computing capabilities, such as agility, flexibility, and scalability, should absolutely apply to the underlying network as well.

 

With that in mind, I’d like to take a look at some recommendations for simplifying a private cloud network. You can consider this post a sort of CliffsNotes* version of a white paper we completed recently; you’ll get a basic idea of what’s going on, but you’ll need to read the full piece to get all the details. It’s a great paper, and I recommend reading it.

 

Consolidate Ports and Cables


Most cloud environments are heavily virtualized, and virtualization has been a big driver of increasing server bandwidth needs. Today it’s common to see virtualized servers sporting eight or more Gigabit Ethernet (GbE) ports. That, of course, means a lot of cabling, network adapters, and switch ports. Consolidating the traffic of those GbE connections onto just a couple of 10 Gigabit Ethernet (10GbE) connections simplifies network connectivity while lowering equipment costs, reducing the number of possible failure points, and increasing the total amount of bandwidth available to the server.

 

 

GbE Configuration.jpg

With 10 Gigabit Ethernet, you can consolidate this . . .

 

10GbE Configuration.jpg

. . . down to this.

 

 

Converge Data and Storage onto Ethernet Fabrics


10GbE’s support for storage technologies, such as iSCSI and Fibre Channel over Ethernet (FCoE), takes network consolidation a step further by converging storage traffic onto Ethernet. Doing so eliminates the need for storage-specific server adapters and infrastructure equipment. IT organizations can combine LAN and SAN traffic onto a single network or maintain a separate Ethernet-based storage network. Either way, they’ve made it easier and more cost-effective to connect servers to network storage systems, reduced equipment costs, and increased network simplicity.

 

Maximize I/O Virtualization Performance and Flexibility


Once you have a 10GbE unified network connecting your cloud resources, you need to make sure you’re using those big pipes effectively. Physical servers can host many virtual machines (VMs), and it’s important to make sure bandwidth is allocated and balanced properly between those VMs. There are different methods for dividing a 10GbE port into smaller, virtual pipes, but they’re not all created equal. Some methods allow these virtual functions to scale and use the available bandwidth of the 10GbE connection as needed, while others assign static bandwidth amounts per virtual function, limiting elasticity and leaving unused capacity in critical situations.

 

Enable a Solution That Works with Multiple Hypervisors


It’s likely that most cloud deployments will consist of hardware and software, including hypervisors, from multiple vendors. Different hypervisors take different approaches to I/O virtualization—and it’s important that network solutions optimize I/O performance for those various software platforms; inconsistent throughput in a heterogeneous environment could result in bottlenecks that impact the delivery of services. Intel Virtualization Technology for Connectivity, included in Intel Ethernet server adapters and controllers, includes Virtual Machine Device Queues (VMDq) and support for Single Root I/O Virtualization (SR-IOV), to improve network performance across all major hypervisors.

 

Utilize Quality of Service for Multi-Tenant Networking


Like a public cloud, a private cloud provides services to many different clients, ranging from internal business units or departments within the company to customers, and they all have performance expectations of the cloud.  Quality of Service (QoS) helps ensure that clients’ requirements are met.

 

Technologies are available that provide QoS on the network and within a physical server. QoS between devices on the network is delivered by Data Center Bridging (DCB), a set of standards that defines how bandwidth is allocated to specific traffic classes and how those policies are enforced. For traffic between virtual machines in a server, QoS can be controlled in either hardware or software, depending on the hypervisor. When choosing a network adapter for your server, support for these types of QoS should be taken into consideration.

 

Again, keep in mind that these are high-level looks at the recommendations. The white paper I’m summarizing goes into much greater detail on the hows and whys behind each recommendation. If you’re thinking about deploying a cloud data center, it’s highly recommended reading.

 

 

Follow @IntelEthernet for the latest updates on Intel Ethernet technologies.

hotelplan.jpgDownload Now


The Hotelplan Group owns a range of travel and tourism companies that specialize in vacation packages—including adventure holidays, skiing, and luxury breaks—across Europe. Over the last decade, the Group has implemented a number of innovative applications to handle its customer interactions, all hosted at its data center in Switzerland. This increasingly complex suite of SAP*-based applications requires an ever-growing amount of processor power.


“The Intel® Itanium® processor 9000 series works with our virtualization layer to allow us to use our server farm as a private cloud,” explained Heini Kalt, director ICT infrastructure for Hotelplan Group. “This gives us the flexibility to allocate processor power to the changing demands of our applications on-demand and ensures we are always using our resources most efficiently.”


For all the details, download our new Hotelplan Group business success story.  As always, you can also find this one, and many others, on the Intel.com Business Success Stories for IT Managers page.

 

 

*Other names and brands may be claimed as the property of others.

INFN.jpgDownload Now

 

The Italian National Institute for Nuclear Physics (INFN) operates an organization in Bologna known as CNAF–the National Center for Research and Development in Information and Data-Transmission Technologies. CNAF is responsible for managing and developing the most important information and data transmission services to support INFN’s high-energy physics research at a national level. Its research activities are divided into five scientific categories: accelerator physics, astroparticle physics, nuclear physics, theoretical physics, and technological development.

 

INFN CNAF has implemented a new on-demand grid/cloud framework for scientific computing based on open, standard technologies and powered by the Intel® Xeon® processor 5600 series. This is one of the world’s first proven, OS-based implementations to achieve excellent scalability and flexibility in providing shared access to resources and integration between grids and clouds–without the need to partition resource pools.

 

“Our data centers support thousands of users and tens of diverse communities,” explained Davide Salomoni, computing research director for INFN CNAF. “Through efficient use of virtualization technologies, we have been able to expand our offerings and to integrate grid and cloud services.”

 

For details, download our new INFN CNAF business success story. As always, you can find this one, and many others, on the Intel.com Business Success Stories for IT Managers page.

In the Spring of 1911, Teddy Roosevelt was asked about a reciprocity agreement with Canada. He said, “Economic considerations mattered less in foreign negotiations than those of national pride.” What was true then still holds today.

 

What does something Teddy Roosevelt said in the early 20th century have to do with the cloud? I address these questions, and other related topics, in Part two of my latest Data Center Knowledge (DCK) Industry Perspective.

 

In the first part on cloud strategy & policy, I identified six top-tier policy and standards considerations, based on Keio University’s Asia Cloud Manifesto, that you need to consider in your journey to the cloud—and your cloud service provider needs to understand intimately.

 

As we discussed in the earlier column, a robust cloud framework— whether private or public— must be community-oriented if it is expected to provide on-demand services, rapid elasticity and resource pooling. In practice though, it is this community-oriented element of the ecosystem that poses the greatest threat to broad adoption. In a response posted to the earlier column, Intel IT senior data engineer Brad Ellison says, “One of the things often lost in the industry’s discussion about the cloud is that there is a physicality underlying the capability.”

 

This simple, yet brilliant statement summarizes the cloud’s greatest challenges and, happily (at least in my view) substantiates content I included in earlier posts about cloud computing. Thanks again, Brad. I wish I had come up with those words!

 

I think you’ll find Part two interesting. As always, I welcome your feedback, so please join the discussion. For more on this topic or answers to your questions, feel free to contact me on LinkedIn.

Re-introducing myself, in case you didn’t feel like clicking on me to see what I look like, I am an ETS(Enterprise Technical Specialist) for Intel.  What the rest of the world would call a Sales Engineer.  I cover the fortune 2000, local and state education and government and healthcare in NW North America.  I have a pretty solid technical background but IT is a big place.  I manage to hold my own on topics Intel, but I also find that with every customer meeting I learn something, sometimes I learn a lot.

 

Since I started posting articles, I have been positioning Xeon as the logicalsuccessor to Risc ( IBM Power/AIX & Oracle Sparc/Solaris) based systems.  This seems like a good thing for Intel, and it is, but it is also a good thing for the customer.  I have posted several entries on Risc Migration where I have tried to address challenges customers might consider.

Recently I find my role has flipped.  I am no longer proselytizing to customers on the benefits of Unix migration, but instead I am being asked for any information on how to get there faster.  Blame it on the economy, the collective IT zeitgeist, or credit my persuasion – whatever the cause, my customers seem to have internalized the message.  I am working with one of my last Power/AIX purchasing holdouts to choreograph their journey to Xeon.

 

I often get the question “what size server for my XYZ application”. This can be tough to answer for a couple of reasons, and I hate responding with “it depends”.  Benchmarks are ok, but at best they give you a rough relative comparison of a specific use of an application or code.  Virtually every published server benchmark has current Xeon results, but for many benchmarks the Risc vendors just don’t publish.  I guess if you can’t say anything nice…

 

For SAP, a common app, there are generally benchmarks available. I usually recommend the SAP SD 2 tier scores for comparison.  As of this writing, the best published four socket Power 7 score is about 25% higher than the best published four socket Xeon E7 processor based system.  The big difference is in the system cost and support cost.  Intel based systems can cost as little as 1/5thof a comparable Power 7 platforms.  I think every company has developed expertise in operating Xeon environments and the operations, support, and licensing costs are well understood.

 

Frequently the target “XYZ” applications are databases.  It would be great if everybody chose to publish TPC-C, TPC-E, TPC-H, but many times the benchmarks just are not available, or if they are they cover different database products.  Customers ask me to clarify how we stack up as a database platform, but without published results on Power 7 there is little I can say.  My strong preference and recommendation to any migration evaluation is to “Run your own benchmarks”.  I have built test harnesses and benchmark tests, and I know it is hard.  To really understand how your application will perform on yournetwork configuration, with your storage architecture, running yourdata – there is no substitute.  Remaining questions on performance, migration/porting, and architecture can be answered, or at least accurately projected.  I have yet to have a customer run their own benchmarks then choose a Risc/Unix platform.  The potential ROI makes Xeon the logical, and most defendable, choice.

One of my more popular blogs earlier this year was about “The Elephant in your Data Center" -- inefficient servers. As I explained, older, inefficient, under-performing servers rob energy and contribute very little to the “information-work” done by a data center.

 

Almost everyone already knows that, of course. The contribution of the blog was to take a potentially complex idea (relative server performance) and build a simple way to access it.

 

The blog proposes a metric called SUE (Server Utilization Effectiveness). We build the idea based on practical experience with lots of input from our Intel IT and DCSG experts. The notion was very similar to Emerson’s CUPS metric with the added twist to normalize so that SUE = 1.0 was ideal and larger numbers were worse (consistent with the way PUE is defined, for better or worse!). Mike Patterson and I discussed some of the benefits of the SUE  approach in a recent Chip Chat podcast on data center and server efficiency with Allyson Klein.

 

The overarching message is that SUE complements PUE in the sense that PUE looks at the building infrastructure efficiency, and SUE looks at the IT equipment efficiency in the data center.

 

The proposal for SUE was primarily oriented around usability. We wanted a way to go into a data center an make an assessment quickly and for a low cost. So, we focused on a simple age-based metric for relative performance. The simplification got a lot of comments, and one was, “what if I want more precision?” The good news is there are answers out there for you. I summarized the results of the discussion below:

 

 

SUE Maturity Model.jpg

Please click on the image for an enlarged view


 

I chatted with Jon Haas here at Intel about this problem. Jon leads the Green Grid’s Technical Committee where he and industry partners are collaborating to run experiments on more accurate Productivity Proxies for server work output. Of course, running a proxy on your server configuration is something that might take longer than a few days, and would occupy some precious engineering resources. But given the high operating and capital costs, the accuracy benefit in many cases will make solid business sense.

 

There are other ways to measure server and data center performance. A common way to estimate server performance and efficiency is to look up published benchmark scores. Depending on the server model, configuration, and workload type of interest, these table look-ups can be accurate without consuming a lot of time and resources.

 

And finally, many advanced internet companies instrument their applications directly to monitor performance. This represents the highest investment level, but produces the highest accuracy.

 

In all cases, the normalization of the actual server performance to the performance of state-of-the-art servers will produce numbers that can be correlated to SUE in the manner discussed in my previous blog and podcast.

 

The good news is that you can find out more about progress on the proxy front, and more at the upcoming Green Grid Forum in San Jose this coming March.

 

As always, I welcome your comments. The idea, as originally proposed, was closer to conceptual than realizable. Yet, taking into account a maturity model, I think it starts to have legs as something which can be standardized. What do you think?

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ntt.jpgA new technology called complex event processing (CEP) processes data flows directly before storage in a database. CEP is currently being adopted in fields like marketing, social infrastructure, and particularly financial markets. In the future, it’s likely to be used in applications such as smart grids that use IT to manage electric energy efficiently.


Japan’s NTT Data is the country’s leader in applying large-scale, real-time data analysis based on CEP. After an evaluation, it chose the Intel® Xeon® processor E7 family, which has approximately 1.5 times the performance of the Intel Xeon processor 7500 series NTT was using and can support more than 10,000 sensors. The evaluation also showed the difference from enabling or disabling Intel® Hyper-Threading Technology (for multi-threaded processing) and Intel® Turbo Boost Technology (for automatically boosting the core clock frequency).


“The improvement in performance means we can analyze a wider range of bridge information with high precision. Similarly, greater processing capacity per unit means more bridges monitored by one server, for better cost/performance,” explained Yoko Inaba, assistant manager of the Information Technology Deployment Center  at NTT’s Research and Development Headquarters.


To learn more, download our new NTT business success story. As always, you can find this one, and many others, on the Intel.com Business Success Stories for IT Managers page (www.intel.com/itcasestudies).

In my last post, I talked about Oracle’s Big Data appliance, and the broad database landscape. As promised, this week I want to discuss data management. Moving on to in-memory...

 

Since its introduction in 2009, many wrote extensively on the subject of Oracle's Exadata appliance. I won't cover that ground here. But what makes Exadata interesting in the context of this post is the way Oracle positioned it at the center of Oracle's data management universe.

 

If you've got Big Data problems, then the starting point for that universe is the previously mentioned Oracle appliance, which can feed the needles from the Hadoop haystack directly into Exadata at full 40Gbit InfiniBand-driven line speed.

 

Once those needles are in Exadata, you can perform data warehousing queries against that data to your heart's content. Thanks to Oracle's use of E5-series Xeon processor-based servers throughout the parallel cluster of storage cells in Exadata, those queries are likely to run pretty quickly.

 

But what if even Exadata can't run the queries quickly enough?  What if you need near-instantaneous response time against large datasets with complex queries and heaps of simultaneous users?

 

That's where the other major new product announcement from OpenWorld comes in: Exalytics.  This appliance combines a 4-socket E7-series Xeon processor-based server, configured with a full Terabyte of DRAM, with new Oracle-authored software for in-memory analytics.  That software derives from the integration of the classic Times Ten in-memory database engine, which until now was focused primarily on OLTP, with the Oracle BI engine (formerly known as ESSBase), along with a sprinkling of columnar-orientation and associated columnar deduplication.

 

If you watched the development of SAP's HANA in-memory database appliance over the last year, then you know what that all means.  What it competitively entails is that HANA is no longer the only enterprise-grade in-memory analytics alternative out there.

 

Competition is a good thing, especially when the competitors run on a common underlying platform, as these two (HANA and Exalytics) do.

 

I found one aspect of Exalytics very intriguing.  Larry Ellison called the feature 'Adaptive Heuristic In-Memory Analytics'.   Thanks to its intimate linkage with Exadata, an Exalytics appliance can successfully deal with working sets that exceed the physical memory capacity of an Exalytics box.  Queries won't run as fast when the working set exceeds physical memory capacity, since physical I/O to the Exadata machine will be needed to fetch the excess. However, that fetching happens at a very high data rate, thanks to the high-speed linkages between Exalytics and Exadata and the parallel nature of Exadata's I/O subsystem.

 

If Oracle delivers on these OpenWorld promises, then their customers will have the option of using a one-stop-shopping approach to acquiring a highly integrated set of tools for Big Data analysis, high-speed extract and load into the Exadata engine for massively parallel data warehousing (and OLTP), and near-real-time in-memory analytics using Exalytics.

 

If it all works as described, I think it will be very impressive. However, Oracle isn't the only game in town. Their competitors aren't standing still. Next time, I'll continue the discussion with my take on the recent announcements of IBM and Microsoft.

 

What do you think of Oracle's announcements?  Do you see an application for them in your shop?  Are you concerned at all about the appliance-only nature of the delivery mechanism for these technologies?  Respond to this post and let's get the conversation started!

 

P.S. - You may have read some of the coverage about Oracle's announcement of the new SPARC T4 processor and the SPARC SuperCluster T4-4 platform that's based on it.  To me, the most interesting thing about the SuperCluster product is the way that it takes advantage of Xeon processor-based subsystem elements.

 

The use of T4 processors on the database machine nodes is secondary. The important thing is that both the ZFS server and the storage cell machines in the SuperCluster are based on E5-series Xeon processors.  The performance gains that Oracle describes for the SuperCluster are due almost entirely to the system architecture improvements that the Exadata approach pioneered, and to the processing acceleration delivered by the Xeon processor-based subsystems -- not the T4 processor itself.

 

I hope Oracle publishes a head-to-head comparison of the performance and price performance of an Exadata system vs. a SuperCluster system.  I'm confident that the all-Xeon-based Exadata system will win on every conceivable measure. I don't expect such a result to be published, do you?

With thousands of cloud services proliferating the Internet, managing identity becomes a real challenge for most of us. Each provider has its own user and password policy. For a human being, it’s almost impossible to safely record dozens of login credentials. Most people try to keep a maximum of three or four passwords. The consequence of this approach is obvious; if someone stole your information for one service, they would probably compromise your identity for several others.


Solutions to solve this problem are not new. Microsoft tried to implement a web Single Sign-on with Passport (now called Windows Live ID). To compete with Microsoft, Sun Microsystems launched the Liberty Alliance with the goal of creating a de facto standard for Internet web applications. Unfortunately, both initiatives had limited adoption and now both applications are almost dead.


A few years later at the RSA conference in 2006,Bill Gates gave a keynote on the end of passwords for the Internet by using CardSpace (i.e. InfoCards), which was introduced with Microsoft Windows Vista. However, five years later, just a few services on the Internet have adopted the “standard.”  In fact, it is really hard to change user behavior. Nowadays, users access their services from several devices such as PC at the office and home, smartphones, tablets, TVs, etc. That contributes to a low-rate of adoption for CardSpace.

 

Passport/Liberty Alliance and CardSpace were designed for user convenience, but in reality didn’t increase the security level. There are valid concerns from service providers, which can lead to low adoption of these technologies. This is the reason why most Internet Banking systems around the globe never adopted it. Instead, banking systems added mechanisms to confirm user identity, while at the same time providing ways for users to utilize web-based services.

 

User_and_provide_problem_statement.png

 

Usually, a user has a login and password as authentication, but it’s not enough to guarantee the user’s identity since his or her credentials could be stolen. Some efforts have been made to protect users against this kind of attack. For example, today many financial institutions use virtual keyboards that change the position of the numbers and letters with each new access.

 

 

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However, attackers can potentially circumvent this process by adding the capability to take screen snapshots at every mouse click. An improvement from this basic approach would be to put together two characters in a single button, as shown:

 

 

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It increases the security, but not for a long time. The more you use this interface and the character clusters change; the attacker can gather more data and more clues about your password.

 

Therefore, adding a second factor for authentication (i.e. two-factor authentication) can improve security and mitigate attacks of a stolen login and password. To make it work, the system should be beyond what the user knows (login & password) and incorporate into the system what the user has (e.g. One-Time Password token – aka OTP).

 

However, giving something to a user is not an inexpensive approach. There are many logistics to deploy and maintain a solution like this. There are many technologies out there that companies can use. One of the cheapest methods available is token table. Token table is a rudimentary OTP challenge/response solution where the service not only provides a login and password, but also a request for the user to insert, for example, the code 10 of his token table.

 

 

Santander_OTP_table.png

 

I can’t say that this method is ineffective, but of course it has its limitations due to the nature of limited number of codes, easy to scan, etc.


Some Internet Banking services useOTP tokens. OTP tokens are six-digit codes that are time-based. You press the button, and the token that generates is valid for a period (i.e. usually 1 minute). As you can imagine, it’s not a cheap solution, and from a user’s perspective, it doesn’t scale. Take my own example: I have an account in two different banks. Each bank offered me these tokens. Can you imagine one for each bank, one for Facebook, one for Twitter, one for Amazon, etc.? In the end, l would carry dozens of these tokens…this is not an effective approach, and it is not convenient for users.


There are a variety of solutions out there. Facebook and Google adopted an approach that uses mobile phones to retrieve a password or unlock an account. Some banks even use a similar approach to authorize a transaction. This approach relies on a third party device to attest user identity but at the same time it does not use a reliable media — SMS is not very reliable (at least not worldwide).


In order to unify and simplify this process this year Intel launched an initiative called Identity Protection Technology (IPT) which is an umbrella for a number of building block components such as OTP authentication embedded into the chipset. By developing this imitative and centralizing the technology in a single device the user uses to access their services we will be able to decrease the concern around men-in-the-middle or men-in-the-browser style attacks.

 

There are many solutions to the identity issue.  From a service provider standpoint, the most pragmatic approach may be to adopt many technologies to support authentication to provide the least path of resistance and hassle for the user.  For example, I hate the idea of carrying an OTP token.

 

 

Best Regards!

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celeris.jpgSpanish financial services provider Celeris needed to reduce server sprawl and energy consumption while offering customers faster, more reliable access to services like online banking. The firm carried out a remote virtualization readiness assessment (RVRA) with Dell and then created a more agile and cost-effective virtual environment with Dell PowerEdge* servers based on the Intel® Xeon® processor 5600 series.

 

“Through the remote virtualization readiness assessment, Dell forecast that we would cut energy consumption by around 50 percent,” explained David García, IT and communications manager for Celeris. “This includes a reduction in air conditioning costs of around 20 percent.”


For all the details, read our new Celeris business success story. As always, you can find this one, and many others, on the Intel.com Business Success Stories for IT Managers page (www.intel.com/itcasestudies).

 

 

*Other names and brands may be claimed as the property of others.

Perhaps you've heard the ancient Chinese curse: "May you live in interesting times".

 

After personally attending (and presenting) at both Oracle's OpenWorld and IBM's Information on Demand conferences in the past two months, and closely following Microsoft's announcements at their recent PASS Summit conference, I feel that the future of database technology is extremely interesting.

 

The Chinese intended their saying as a curse.  However, in the database setting, it's more of a blessing since it means that as a practitioner, you’re presented with a far richer set of capabilities than you've ever had available in the past.  But that plethora of choices can also become overwhelming.

I can't begin to cover everything from these conferences and announcements in a single post.  What I hope to convey here and in the coming weeks is my take on where the broad database landscape stands today vs. even just one year ago, and where I think it's headed in the future.

 

OpenWorld was the first event in the sequence, so I'll start there (and stop there for this post). A year ago, OpenWorld was all about Exadata and Exalogic.  Meanwhile, Oracle's many aspiring competitors promoted their ability to use NoSQL and in-memory techniques to do things that Big Red couldn't do.

 

Apparently Big Red took notice, because this year's event saw the announcement of two appliance offerings that are clearly a response to the NoSQL and in-memory opportunities.  Consider Oracle's approach to Big Data and NoSQL.  Perhaps unsurprisingly, it's a bit different than most.

 

The conventional approach to Big Data is to use Hadoop. The standard approach for implementing Hadoop seems to mean taking a big pile of the cheapest servers you can find, loading them up with big, cheap disks, interconnecting them with cheap but slow gigabit Ethernet, installing the Apache Hadoop stack on each one of these servers, and then throwing reams of mostly useless data at the resulting Hadoop cluster in an attempt to find the useful needle in the data exhaust haystack.

 

The question of what to actually DO with those useful needles once you've managed to find them using Hadoop (using algorithms that you've hand-coded yourself to run in the Hadoop run-time environment), is generally left as an exercise for the reader.

 

Oracle takes a different approach. Instead of old, underpowered, slow servers, they use modern dual-socket E5-series Intel Xeon processor-based servers. As an alternative to slow gigabit Ethernet, they use 40Gbit Infiniband Architecture-based interconnects, which also connect to Exadata.

Instead of merely integrating the standard open-source Hadoop stack on their Big Data appliance, Oracle augments that stack with two key Oracle-proprietary software elements that might just prove useful to Big Data practitioners: 1) the Oracle Database Loader for Hadoop and 2) a variant of the open-source 'R' statistical analysis package that they adapted to work in the Big Data appliance (there are other elements as well, but these two are what I view as the two biggies).

 

If you've ever loaded bulk data into an Oracle database (or any other database, for that matter), you know that it can take a while.  Much of the reason for that is that the database engine has to extensively massage the incoming data stream in order to prepare it for proper storage in the real database. But what if you knew how the data needed to be formatted in the real database, so you could do that processing 'out of band', in a massively parallel manner, and present only pre-processed data to the database for final storage?

 

Sounds more efficient, doesn't it? That's what the Oracle Database Loader for Hadoop does.

 

Of course, Oracle's stuff isn't free.  Neither are the similar offerings from EMC/Greenplum (who kind of started this Big Data frenzy), or IBM, or Microsoft.  However, you get something for your money in all cases.  Implementing a working Hadoop cluster using software downloaded from the Apache project is a time-consuming exercise, to say the least.  If you've got the time to fiddle with that stuff, by all means go for it.  But if your mission is to start making productive use of your company's big data as quickly as possible, then one of the commercial options is probably a better choice.

In that case, pay the vendors to do the intricate technical work for you, and be aware that all of them share a common underlying platform: the Intel Xeon(r) processor.

 

Oracle's Big Data Appliance won't be shipping until next year, so it's appropriate to take a wait-and-see approach, but the specifications look interesting. In my next post, I’ll talk about in-memory. For now, I welcome comments or questions regarding your thoughts on the future of Big Data. The future of Big Data seems fascinating, and I’d love to hear about your thoughts on the topic.

Stateless, stateless, and stateless! That is the order for developers who want to create a cloud-aware application. Cloud computing, which allows users to access their applications hosted on servers over the Internet, brings both new benefits and responsibilities for developers.

 

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For developers who host their code in a public PaaS, some providers expose APIs that allow the application to request more resources than the application needs. These can automatically scale when necessary, and that is a great asset for capacity planning and quality of services. Now, more than ever, developers need to take care of redundancy -- not only because cloud providers use commodity machines but because dynamically scaling-out requires that application and user sessions be treated in the application code. And that means stateless.

 

Even a simple application needs to store some kind of state. This is one of the reasons why we still need databases or objects to store some user’s information state. It’s the reason why most web applications do not store any information on front end servers – it’s the part that usually needs scale.

Dealing with databases is not the same either. The concept of relational database is almost dead in a cloud environment. Actually, most PaaS bring the notion of “storage engine”, where the denormalization model is encouraged. Since tables are split across multiple machines for scalability, many “old school” concepts are not valuable in the cloud (such as Store Procedures, use of JOINS in the query, etc.). On the other hand, performance can increase as a result of the capability to retrieve database information on multiples servers at same time.

 

For developers, this is new in the computing world. Some of these ideas can be scary, but it is just a matter of understanding the new rules, and applying them to your application. A new development concept came from this model, which is called “eventual consistency”. Eventual consistency is when a change in the database/application may not be registered for a few milliseconds. As developer, it is very important that you are aware of this.

 

Developing an application hosted in the cloud differs from day one of an on-premise deployment. You can abstract many layers and consume different resources available on the Internet. For example, you can host your presentation layer on Facebook, store data on Amazon’s S3, deliver static content using Akamai’s CDN and utilize application logic that runs in an entirely separate place. The biggest enemy in this environment is the latency due to the nature of the Internet. Not only are there issues with interconnecting application components but also delivering the service to the end-user.

 

Lower latency is not something that you can’t obtain. You can buy bandwidth but it will not solve latency issues of requests crossing dispersed geographic resources. Adopting a strategy to use geo-located servers can minimize this effect. For example, Microsoft offers Microsoft Azure. However, from an architecture standpoint, the service reliability works best on stateless application designs.

 

     Aside from the nature of a cloud-aware application, there are several devices ranging from smartphones, tablets, notebooks, desktop computers, car entertainment systems, home automation, etc. that move towards specialization. The capability available in these devices varies as well, for example, the size of the screen, audio, camera, battery, GPS, Internet connectivity bandwidth, keyboard, etc.  Finally, so many possible combinations, such as different operating systems or browsers, can make this process miserable. In order to abstract these differences, many applications adopt the principle to run entirely on cloud, delivering the service via browser. However, for user experience reasons, some applications need to execute locally. Therefore, the creation of a scalable, stateless, web service interface in the cloud and use of the best framework available for device specific applications both become very important.

 

We can’t deny the increase in web developers that use rich media content and graphic intensive applications in order to attract users and present the best possible user experience. Yet, the less capable device limits service offering. Using Web APIs to access a user’s device can help developers deliver the best user experience based on capability available, as presented in the following web portal:

 

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In this scenario, the cloud application can decide to stream HD video quality instead of low-quality video due to connection type. There is no concern over whether the battery will be enough to ensure the user sees the whole video because it is connected to an external source.

 

In order to explore these capabilities, I suggest that you read the Cloud Builder Reference Architecture from gproxy.com and NetSuite.

 

Best Regards!

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