Tony Pearson is a Master Inventor and Senior IT Architect for the IBM Storage product line at the
IBM Systems Client Experience Center in Tucson Arizona, and featured contributor
to IBM's developerWorks. In 2016, Tony celebrates his 30th year anniversary with IBM Storage. He is
author of the Inside System Storage series of books. This blog is for the open exchange of ideas relating to storage and storage networking hardware, software and services.
(Short URL for this blog: ibm.co/Pearson )
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For the longest time, people thought that humans could not run a mile in less than four minutes. Then, in 1954, [Sir Roger Bannister] beat that perception, and shortly thereafter, once he showed it was possible, many other runners were able to achieve this also. The same is being said now about the IBM Watson computer which appeared this week against two human contestants on Jeopardy!
(2014 Update: A lot has happened since I originally wrote this blog post! I intended this as a fun project for college students to work on during their summer break. However, IBM is concerned that some businesses might be led to believe they could simply stand up their own systems based entirely on open source and internally developed code for business use. IBM recommends instead the [IBM InfoSphere BigInsights] which packages much of the software described below. IBM has also launched a new "Watson Group" that has [Watson-as-a-Service] capabilities in the Cloud. To raise awareness to these developments, IBM has asked me to rename this post from IBM Watson - How to build your own "Watson Jr." in your basement to the new title IBM Watson -- How to replicate Watson hardware and systems design for your own use in your basement. I also took this opportunity to improve the formatting layout.)
Often, when a company demonstrates new techology, these are prototypes not yet ready for commercial deployment until several years later. IBM Watson, however, was made mostly from commercially available hardware, software and information resources. As several have noted, the 1TB of data used to search for answers could fit on a single USB drive that you buy at your local computer store.
Take a look at the [IBM Research Team] to determine how the project was organized. Let's decide what we need, and what we don't in our version for personal use:
Do we need it for personal use?
Yes, That's you. Assuming this is a one-person project, you will act as Team Lead.
Yes, I hope you know computer programming!
No, since this version for personal use won't be appearing on Jeopardy, we won't need strategy on wager amounts for the Daily Double, or what clues to pick next. Let's focus merely on a computer that can accept a question in text, and provide an answer back, in text.
Yes, this team focused on how to wire all the hardware together. We need to do that, although this version for personal use will have fewer components.
Optional. For now, let's have this version for personal use just return its answer in plain text. Consider this Extra Credit after you get the rest of the system working. Consider using [eSpeak], [FreeTTS], or the Modular Architecture for Research on speech sYnthesis [MARY] Text-to-Speech synthesizers.
Yes, I will explain what this is, and why you need it.
Yes, we will need to get information for personal use to process
Yes, this team developed a system for parsing the question being asked, and to attach meaning to the different words involved.
No, this team focused on making IBM Watson optimized to answer in 3 seconds or less. We can accept a slower response, so we can skip this.
(Disclaimer: As with any Do-It-Yourself (DIY) project, I am not responsible if you are not happy with your version for personal use I am basing the approach on what I read from publicly available sources, and my work in Linux, supercomputers, XIV, and SONAS. For our purposes, this version for personal use is based entirely on commodity hardware, open source software, and publicly available sources of information. Your implementation will certainly not be as fast or as clever as the IBM Watson you saw on television.)
Step 1: Buy the Hardware
Supercomputers are built as a cluster of identical compute servers lashed together by a network. You will be installing Linux on them, so if you can avoid paying extra for Microsoft Windows, that would save you some money. Here is your shopping list:
Three x86 hosts, with the following:
64-bit quad-core processor, either Intel-VT or AMD-V capable,
8GB of DRAM, or larger
300GB of hard disk, or larger
CD or DVD Read/Write drive
Computer Monitor, mouse and keyboard
Ethernet 1GbE 4-port hub, and appropriate RJ45 cables
Surge protector and Power strip
Local Console Monitor (LCM) 4-port switch (formerly known as a KVM switch) and appropriate cables. This is optional, but will make it easier during the development. Once your implementation is operational, you will only need the monitor and keyboard attached to one machine. The other two machines can remain "headless" servers.
Step 2: Establish Networking
IBM Watson used Juniper switches running at 10Gbps Ethernet (10GbE) speeds, but was not connected to the Internet while playing Jeopardy! Instead, these Ethernet links were for the POWER7 servers to talk to each other, and to access files over the Network File System (NFS) protocol to the internal customized SONAS storage I/O nodes.
The implementation will be able to run "disconnected from the Internet" as well. However, you will need Internet access to download the code and information sources. For our purposes, 1GbE should be sufficient. Connect your Ethernet hub to your DSL or Cable modem. Connect all three hosts to the Ethernet switch. Connect your keyboard, video monitor and mouse to the LCM, and connect the LCM to the three hosts.
Step 3: Install Linux and Middleware
To say I use Linux on a daily basis is an understatement. Linux runs on my Android-based cell phone, my laptop at work, my personal computers at home, most of our IBM storage devices from SAN Volume Controller to XIV to SONAS, and even on my Tivo at home which recorded my televised episodes of Jeopardy!
For this project, you can use any modern Linux distribution that supports KVM. IBM Watson used Novel SUSE Linux Enterprise Server [SLES 11]. Alternatively, I can also recommend either Red Hat Enterprise Linux [RHEL 6] or Canonical [Ubuntu v10]. Each distribution of Linux comes in different orientations. Download the the 64-bit "ISO" files for each version, and burn them to CDs.
Graphical User Interface (GUI) oriented, often referred to as "Desktop" or "HPC-Head"
Command Line Interface (CLI) oriented, often referred to as "Server" or "HPC-Compute"
Guest OS oriented, to run in a Hypervisor such as KVM, Xen, or VMware. Novell calls theirs "Just Enough Operating System" [JeOS].
For this version for personal use, I have chosen a [multitier architecture], sometimes referred to as an "n-tier" or "client/server" architecture.
Host 1 - Presentation Server
For the Human-Computer Interface [HCI], the IBM Watson received categories and clues as text files via TCP/IP, had a [beautiful avatar] representing a planet with 42 circles streaking across in orbit, and text-to-speech synthesizer to respond in a computerized voice. Your implementation will not be this sophisticated. Instead, we will have a simple text-based Query Panel web interface accessible from a browser like Mozilla Firefox.
Host 1 will be your Presentation Server, the connection to your keyboard, video monitor and mouse. Install the "Desktop" or "HPC Head Node" version of Linux. Install [Apache Web Server and Tomcat] to run the Query Panel. Host 1 will also be your "programming" host. Install the [Java SDK] and the [Eclipse IDE for Java Developers]. If you always wanted to learn Java, now is your chance. There are plenty of books on Java if that is not the language you normally write code.
While three little systems doesn't constitute an "Extreme Cloud" environment, you might like to try out the "Extreme Cloud Administration Tool", called [xCat], which was used to manage the many servers in IBM Watson.
Host 2 - Business Logic Server
Host 2 will be driving most of the "thinking". Install the "Server" or "HPC Compute Node" version of Linux. This will be running a server virtualization Hypervisor. I recommend KVM, but you can probably run Xen or VMware instead if you like.
Host 3 - File and Database Server
Host 3 will hold your information sources, indices, and databases. Install the "Server" or "HPC Compute Node" version of Linux. This will be your NFS server, which might come up as a question during the installation process.
Technically, you could run different Linux distributions on different machines. For example, you could run "Ubuntu Desktop" for host 1, "RHEL 6 Server" for host 2, and "SLES 11" for host 3. In general, Red Hat tries to be the best "Server" platform, and Novell tries to make SLES be the best "Guest OS".
My advice is to pick a single distribution and use it for everything, Desktop, Server, and Guest OS. If you are new to Linux, choose Ubuntu. There are plenty of books on Linux in general, and Ubuntu in particular, and Ubuntu has a helpful community of volunteers to answer your questions.
Step 4: Download Information Sources
You will need some documents for your implementation to process.
IBM Watson used a modified SONAS to provide a highly-available clustered NFS server. For this version, we won't need that level of sophistication. Configure Host 3 as the NFS server, and Hosts 1 and 2 as NFS clients. See the [Linux-NFS-HOWTO] for details. To optimize performance, host 3 will be the "official master copy", but we will use a Linux utility called rsync to copy the information sources over to the hosts 1 and 2. This allows the task engines on those hosts to access local disk resources during question-answer processing.
We will also need a relational database. You won't need a high-powered IBM DB2. Your implementation can do fine with something like [Apache Derby] which is the open source version of IBM CloudScape from its Informix acquisition. Set up Host 3 as the Derby Network Server, and Hosts 1 and 2 as Derby Network Clients. For more about structured content in relational databases, see my post [IBM Watson - Business Intelligence, Data Retrieval and Text Mining].
Linux includes a utility called wget which allows you to download content from the Internet to your system. What documents you decide to download is up to you, based on what types of questions you want answered. For example, if you like Literature, check out the vast resources at [FullBooks.com]. You can automate the download by writing a shell script or program to invoke wget to all the places you want to fetch data from. Rename the downloaded files to something unique, as often they are just "index.html". For more on wget utility, see [IBM Developerworks].
Step 5: The Query Panel - Parsing the Question
Next, we need to parse the question and have some sense of what is being asked for. For this we will use [OpenNLP] for Natural Language Processing, and [OpenCyc] for the conceptual logic reasoning. See Doug Lenat presenting this 75-minute video [Computers versus Common Sense]. To learn more, see the [CYC 101 Tutorial].
Unlike Jeopardy! where Alex Trebek provides the answer and contestants must respond with the correct question, we will do normal Question-and-Answer processing. To keep things simple, we will limit questions to the following formats:
Who is ...?
Where is ...?
When did ... happen?
What is ...?
Host 1 will have a simple Query Panel web interface. At the top, a place to enter your question, and a "submit" button, and a place at the bottom for the answer to be shown. When "submit" is pressed, this will pass the question to "main.jsp", the Java servlet program that will start the Question-answering analysis. Limiting the types of questions that can be posed will simplify hypothesis generation, reduce the candidate set and evidence evaluation, allowing the analytics processing to continue in reasonable time.
Step 6: Unstructured Information Management Architecture
The "heart and soul" of IBM Watson is Unstructured Information Management Architecture [UIMA]. IBM developed this, then made it available to the world as open source. It is maintained by the [Apache Software Foundation], and overseen by the Organization for the Advancement of Structured Information Standards [OASIS].
Basically, UIMA lets you scan unstructured documents, gleam the important points, and put that into a database for later retrieval. In the graph above, DBs means 'databases' and KBs means 'knowledge bases'. See the 4-minute YouTube video of [IBM Content Analytics], the commercial version of UIMA.
Starting from the left, the Collection Reader selects each document to process, and creates an empty Common Analysis Structure (CAS) which serves as a standardized container for information. This CAS is passed to Analysis Engines , composed of one or more Annotators which analyze the text and fill the CAS with the information found. The CAS are passed to CAS Consumers which do something with the information found, such as enter an entry into a database, update an index, or update a vote count.
(Note: This point requires, what we in the industry call a small matter of programming, or [SMOP]. If you've always wanted to learn Java programming, XML, and JDBC, you will get to do plenty here. )
If you are not familiar with UIMA, consider this [UIMA Tutorial].
Step 7: Parallel Processing
People have asked me why IBM Watson is so big. Did we really need 2,880 cores of processing power? As a supercomputer, the 80 TeraFLOPs of IBM Watson would place it only in 94th place on the [Top 500 Supercomputers]. While IBM Watson may be the [Smartest Machine on Earth], the most powerful supercomputer at this time is the Tianhe-1A with more than 186,000 cores, capable of 2,566 TeraFLOPs.
To determine how big IBM Watson needed to be, the IBM Research team ran the DeepQA algorithm on a single core. It took 2 hours to answer a single Jeopardy question! Let's look at the performance data:
Number of cores
Time to answer one Jeopardy question
Single IBM Power750 server
< 4 minutes
Single rack (10 servers)
< 30 seconds
IBM Watson (90 servers)
< 3 seconds
The old adage applies, [many hands make for light work]. The idea is to divide-and-conquer. For example, if you wanted to find a particular street address in the Manhattan phone book, you could dispatch fifty pages to each friend and they could all scan those pages at the same time. This is known as "Parallel Processing" and is how supercomputers are able to work so well. However, not all algorithms lend well to parallel processing, and the phrase [nine women can't have a baby in one month] is often used to remind us of this.
Fortuantely, UIMA is designed for parallel processing. You need to install UIMA-AS for Asynchronous Scale-out processing, an add-on to the base UIMA Java framework, supporting a very flexible scale-out capability based on JMS (Java Messaging Services) and ActiveMQ. We will also need Apache Hadoop, an open source implementation used by Yahoo Search engine. Hadoop has a "MapReduce" engine that allows you to divide the work, dispatch pieces to different "task engines", and the combine the results afterwards.
Host 2 will run Hadoop and drive the MapReduce process. Plan to have three KVM guests on Host 1, four on Host 2, and three on Host 3. That means you have 10 task engines to work with. These task engines can be deployed for Content Readers, Analysis Engines, and CAS Consumers. When all processing is done, the resulting votes will be tabulated and the top answer displayed on the Query Panel on Host 1.
Step 8: Testing
To simplify testing, use a batch processing approach. Rather than entering questions by hand in the Query Panel, generate a long list of questions in a file, and submit for processing. This will allow you to fine-tune the environment, optimize for performance, and validate the answers returned.
There you have it. By the time you get your implementation fully operational, you will have learned a lot of useful skills, including Linux administration, Ethernet networking, NFS file system configuration, Java programming, UIMA text mining analysis, and MapReduce parallel processing. Hopefully, you will also gain an appreciation for how difficult it was for the IBM Research team to accomplish what they had for the Grand Challenge on Jeopardy! Not surprisingly, IBM Watson is making IBM [as sexy to work for as Apple, Google or Facebook], all of which started their business in a garage or a basement with a system as small as this version for personal use.
Are you tired of hearing about Cloud Computing without having any hands-on experience? Here's your chance. IBM has recently launched its IBM Development and Test Cloud beta. This gives you a "sandbox" to play in. Here's a few steps to get started:
Generate a "key pair". There are two keys. A "public" key that will reside in the cloud, and a "private" key that you download to your personal computer. Don't lose this key.
Request an IP address. This step is optional, but I went ahead and got a static IP, so I don't have to type in long hostnames like "vm353.developer.ihost.com".
Request storage space. Again, this step is optional, but you can request a 50GB, 100GB and 200GB LUN. I picked a 200GB LUN. Note that each instance comes with some 10 to 30GB storage already. The advantage to a storage LUN is that it is persistent, and you can mount it to different instances.
Start an "instance". An "instance" is a virtual machine, pre-installed with whatever software you chose from the "asset catalog". These are Linux images running under Red Hat Enterprise Virtualization (RHEV) which is based on Linux's kernel virtual machine (KVM). When you start an instance, you get to decide its size (small, medium, or large), whether to use your static IP address, and where to mount your storage LUN. On the examples below, I had each instance with a static IP and mounted the storage LUN to /media/storage subdirectory. The process takes a few minutes.
So, now that you are ready to go, what instance should you pick from the catalog? Here are three examples to get you started:
IBM WebSphere sMASH Application Builder
Base OS server to run LAMP stack
Next, I decided to try out one of the base OS images. There are a lot of books on Linux, Apache, MySQL and PHP (LAMP) which represents nearly 70 percent of the web sites on the internet. This instance let's you install all the software from scratch. Between Red Hat and Novell SUSE distributions of Linux, Red Hat is focused on being the Hypervisor of choice, and SUSE is focusing on being the Guest OS of choice. Most of the images on the "asset catalog" are based on SLES 10 SP2. However, there was a base OS image of Red Hat Enterprise Linux (RHEL) 5.4, so I chose that.
To install software, you either have to find the appropriate RPM package, or download a tarball and compile from source. To try both methods out, I downloaded tarballs of Apache Web Server and PHP, and got the RPM packages for MySQL. If you just want to learn SQL, there are instances on the asset catalog with DB2 and DB2 Express-C already pre-installed. However, if you are already an expert in MySQL, or are following a tutorial or examples based on MySQL from a classroom textbook, or just want a development and test environment that matches what your company uses in production, then by all means install MySQL.
This is where my SSH client comes in handy. I am able to login to my instance and use "wget" to fetch the appropriate files. An alternative is to use "SCP" (also part of PuTTY) to do a secure copy from your personal computer up to the instance. You will need to do everything via command line interface, including editing files, so I found this [VI cheat sheet] useful. I copied all of the tarballs and RPMs on my storage LUN ( /media/storage ) so as not to have to download them again.
Compiling and configuring them is a different matter. By default, you login as an end user, "idcuser" (which stands for IBM Developer Cloud user). However, sometimes you need "root" level access. Use "sudo bash" to get into root level mode, and this allows you to put the files where they need to be. If you haven't done a configure/make/make install in awhile, here's your chance to relive those "glory days".
In the end, I was able to confirm that Apache, MySQL and PHP were all running correctly. I wrote a simple index.php that invoked phpinfo() to show all the settings were set correctly. I rebooted the instance to ensure that all of the services started at boot time.
Rational Application Developer over VDI
This last example, I started an instance pre-installed with Rational Application Developer (RAD), which is a full Integrated Development Environment (IDE) for Java and J2EE applications. I used the "NX Client" to launch a virtual desktop image (VDI) which in this case was Gnome on SLES 10 SP2. You might want to increase the screen resolution on your personal computer so that the VDI does not take up the entire screen.
From this VDI, you can launch any of the programs, just as if it were your own personal computer. Launch RAD, and you get the familiar environment. I created a short Java program and launched it on the internal WebSphere Application Server test image to confirm it was working correctly.
If you are thinking, "This is too good to be true!" there is a small catch. The instances are only up and running for 7 days. After that, they go away, and you have to start up another one. This includes any files you had on the local disk drive. You have a few options to save your work:
Copy the files you want to save to your storage LUN. This storage LUN appears persistent, and continues to exist after the instance goes away.
Take an "image" of your "instance", a function provided in the IBM Developer and Test Cloud. If you start a project Monday morning, work on it all week, then on Friday afternoon, take an "image". This will shutdown your instance, and backup all of the files to your own personal "asset catalog" so that the next time you request an instance, you can chose that "image" as the starting point.
Another option is to request an "extension" which gives you another 7 days for that instance. You can request up to five unique instances running at the same time, so if you wanted to develop and test a multi-host application, perhaps one host that acts as the front-end web server, another host that does some kind of processing, and a third host that manages the database, this is all possible. As far as I can tell, you can do all the above from either a Windows, Mac or Linux personal computer.
Getting hands-on access to Cloud Computing really helps to understand this technology!
Well it's Tuesday again, and you know what that means.. IBM announcements! Today, IBM announces that next Monday marks the 60th anniversary of first commercial digital tape storage system! I am on the East coast this week visiting clients, but plan to be back in Tucson in time for the cake and fireworks next Monday.
1925 - masking tape (which 3M sold under its newly announced Scotch® brand)
1930 - clear cellulose-based tape (today, when people say Scotch tape, they usually are referring to the cellulose version)
1935 - Allgemeine Elektrizitatsgesellschaft (AEG) presents Magnetophon K1, audio recording on analog tape
1942 - Duct tape
1947 - Bing Crosby adopts audio recording for his radio program. This eliminated him doing the same program live twice per day, perhaps the first example of using technology for "deduplication".
According to the IBM Archives the [IBM 726 tape drive was formally announced May 21, 1952]. It was the size of a refrigerator, and the tape reel was the size of a large pizza. The next time you pull a frozen pizza from your fridge, you can remember this month's celebration!
When I first joined IBM in 1986, there were three kinds of IBM tape. The round reel called 3420, and the square cartridge called 3480, and the tubes that contained a wide swath of tape stored in honeycomb shelves called the [IBM 3850 Mass Storage System].
My first job at IBM was to work on DFHSM, which was specifically started in 1977 to manage the IBM 3850, and later renamed to the DFSMShsm component of the DFSMS element of the z/OS operating system. This software was instrumental in keeping disk and tape at high 80-95 percent utilization rates on mainframe servers.
While visiting a client in Detroit, the client loved their StorageTek tape automation silo, but didn't care for the StorageTek drives inside were incompatible with IBM formats. They wanted to put IBM drives into the StorageTek silos. I agreed it was a good idea, and brought this back to the attention of development. In a contentious meeting with management and engineers, I presented this feedback from the client.
Everyone in the room said IBM couldn't do that. I asked "Why not?" The software engineers I spoke to already said they could support it. With StorageTek at the brink of Chapter 11 bankruptcy, I argued that IBM drives in their tape automation would ease the transition of our mainframe customers to an all-IBM environment.
Was the reason related to business/legal concerns, or was their a hardware issue? It turned out to be a little of both. On the business side, IBM had to agree to work with StorageTek on service and support to its mutual clients in mixed environments. On the technical side, the drive had to be tilted 12 degrees to line up with the robotic hand. A few years later, the IBM silo-compatible 3592 drive was commercially available.
Rather than put StorageTek completely out of business, it had the opposite effect. Now that IBM drives can be put in StorageTek libraries, everyone wanted one, basically bringing StorageTek back to life. This forced IBM to offer its own tape automation libraries.
In 1993, I filed my first patent. It was for the RECYCLE function in DFHSM to consolidate valid data from partial tapes to fresh new tapes. Before my patent, the RECYCLE function selected tapes alphabetically, by volume serial (VOLSER). My patent evaluated all tapes based on how full they were, and sorted them least-full to most-full, to maximize the return of cartridges.
Different tape cartridges can hold different amounts of data, especially with different formats on the same media type, with or without compression, so calculating the percentage full turned out to be a tricky algorithm that continues to be used in mainframe environments today.
The patent was popular for cross-licensing, and IBM has since filed additional patents for this invention in other countries to further increase its license revenue for intellectual property.
In 1997, IBM launched the IBM 3494 Virtual Tape Server (VTS), the first virtual tape storage device, blending disk and tape to optimal effect. This was based off the IBM 3850 Mass Storage Systems, which was the first virtual disk system, that used 3380 disk and tape to emulate the older 3350 disk systems.
In the VTS, tape volume images would be emulated as files on a disk system, then later moved to physical tape. We would call the disk the "Tape Volume Cache", and use caching algorithms to decide how long to keep data in cache, versus destage to tape. However, there were only a few tape drives, and sometimes when the VTS was busy, there were no tape drives available to destage the older images, and the cache would fill up.
I had already solved this problem in DFHSM, with a function called pre-migration. The idea was to pre-emptively copy data to tape, but leave it also on disk, so that when it needed to be destaged, all we had to do was delete the disk copy and activate the tape copy. We patented using this idea for the VTS, and it is still used in the successor models of IBM Sysem Storage TS7740 virtual tape libraries today.
Today, tape continues to be the least expensive storage medium, about 15 to 25 times less expensive, dollar-per-GB, than disk technologies. A dollar of today's LTO-5 tape can hold 22 days worth of MP3 music at 192 Kbps recording. A full TS1140 tape cartridge can hold 2 million copies of the book "War and Peace".
(If you have not read the book, Woody Allen took a speed reading course and read the entire novel in just 20 minutes. He summed up the novel in three words: "It involves Russia." By comparison, in the same 20 minutes, at 650MB/sec, the TS1140 drive can read this novel over and over 390,000 times.)
If you have your own "war stories" about tape, I would love to hear them, please consider posting a comment below.
Tonight PBS plans to air Season 38, Episode 6 of NOVA, titled [Smartest Machine On Earth]. Here is an excerpt from the station listing:
"What's so special about human intelligence and will scientists ever build a computer that rivals the flexibility and power of a human brain? In "Artificial Intelligence," NOVA takes viewers inside an IBM lab where a crack team has been working for nearly three years to perfect a machine that can answer any question. The scientists hope their machine will be able to beat expert contestants in one of the USA's most challenging TV quiz shows -- Jeopardy, which has entertained viewers for over four decades. "Artificial Intelligence" presents the exclusive inside story of how the IBM team developed the world's smartest computer from scratch. Now they're racing to finish it for a special Jeopardy airdate in February 2011. They've built an exact replica of the studio at its research lab near New York and invited past champions to compete against the machine, a big black box code -- named Watson after IBM's founder, Thomas J. Watson. But will Watson be able to beat out its human competition?"
Like most supercomputers, Watson runs the Linux operating system. The system runs 2,880 cores (90 IBM Power 750 servers, four sockets each, eight cores per socket) to achieve 80 [TeraFlops]. TeraFlops is the unit of measure for supercomputers, representing a trillion floating point operations. By comparison, Hans Morvec, principal research scientist at the Robotics Institute of Carnegie Mellon University (CMU) estimates that the [human brain is about 100 TeraFlops]. So, in the three seconds that Watson gets to calculate its response, it would have processed 240 trillion operations.
Several readers of my blog have asked for details on the storage aspects of Watson. Basically, it is a modified version of IBM Scale-Out NAS [SONAS] that IBM offers commercially, but running Linux on POWER instead of Linux-x86. System p expansion drawers of SAS 15K RPM 450GB drives, 12 drives each, are dual-connected to two storage nodes, for a total of 21.6TB of raw disk capacity. The storage nodes use IBM's General Parallel File System (GPFS) to provide clustered NFS access to the rest of the system. Each Power 750 has minimal internal storage mostly to hold the Linux operating system and programs.
When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, "The actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1TB." For performance reasons, various subsets of the data are replicated in RAM on different functional groups of cluster nodes. The entire system is self-contained, Watson is NOT going to the internet searching for answers.
“In times of universal deceit, telling the truth will be a revolutionary act.”
-- George Orwell
Well, it has been over two years since I first covered IBM's acquisition of the XIV company. Amazingly, I still see a lot of misperceptions out in the blogosphere, especially those regarding double drive failures for the XIV storage system. Despite various attempts to [explain XIV resiliency] and to [dispel the rumors], there are still competitors making stuff up, putting fear, uncertainty and doubt into the minds of prospective XIV clients.
Clients love the IBM XIV storage system! In this economy, companies are not stupid. Before buying any enterprise-class disk system, they ask the tough questions, run evaluation tests, and all the other due diligence often referred to as "kicking the tires". Here is what some IBM clients have said about their XIV systems:
“3-5 minutes vs. 8-10 hours rebuild time...”
-- satisfied XIV client
“...we tested an entire module failure - all data is re-distributed in under 6 hours...only 3-5% performance degradation during rebuild...”
-- excited XIV client
“Not only did XIV meet our expectations, it greatly exceeded them...”
In this blog post, I hope to set the record straight. It is not my intent to embarrass anyone in particular, so instead will focus on a fact-based approach.
Fact: IBM has sold THOUSANDS of XIV systems
XIV is "proven" technology with thousands of XIV systems in company data centers. And by systems, I mean full disk systems with 6 to 15 modules in a single rack, twelve drives per module. That equates to hundreds of thousands of disk drives in production TODAY, comparable to the number of disk drives studied by [Google], and [Carnegie Mellon University] that I discussed in my blog post [Fleet Cars and Skin Cells].
Fact: To date, no customer has lost data as a result of a Double Drive Failure on XIV storage system
This has always been true, both when XIV was a stand-alone company and since the IBM acquisition two years ago. When examining the resilience of an array to any single or multiple component failures, it's important to understand the architecture and the design of the system and not assume all systems are alike. At it's core, XIV is a grid-based storage system. IBM XIV does not use traditional RAID-5 or RAID-10 method, but instead data is distributed across loosely connected data modules which act as independent building blocks. XIV divides each LUN into 1MB "chunks", and stores two copies of each chunk on separate drives in separate modules. We call this "RAID-X".
Spreading all the data across many drives is not unique to XIV. Many disk systems, including EMC CLARiiON-based V-Max, HP EVA, and Hitachi Data Systems (HDS) USP-V, allow customers to get XIV-like performance by spreading LUNs across multiple RAID ranks. This is known in the industry as "wide-striping". Some vendors use the terms "metavolumes" or "extent pools" to refer to their implementations of wide-striping. Clients have coined their own phrases, such as "stripes across stripes", "plaid stripes", or "RAID 500". It is highly unlikely that an XIV will experience a double drive failure that ultimately requires recovery of files or LUNs, and is substantially less vulnerable to data loss than an EVA, USP-V or V-Max configured in RAID-5. Fellow blogger Keith Stevenson (IBM) compared XIV's RAID-X design to other forms of RAID in his post [RAID in the 21st Centure].
Fact: IBM XIV is designed to minimize the likelihood and impact of a double drive failure
The independent failure of two drives is a rare occurrence. More data has been lost from hash collisions on EMC Centera than from double drive failures on XIV, and hash collisions are also very rare. While the published worst-case time to re-protect from a 1TB drive failure for a fully-configured XIV is 30 minutes, field experience shows XIV regaining full redundancy on average in 12 minutes. That is 40 times less likely than a typical 8-10 hour window for a RAID-5 configuration.
A lot of bad things can happen in those 8-10 hours of traditional RAID rebuild. Performance can be seriously degraded. Other components may be affected, as they share cache, connected to the same backplane or bus, or co-dependent in some other manner. An engineer supporting the customer onsite during a RAID-5 rebuild might pull the wrong drive, thereby causing a double drive failure they were hoping to avoid. Having IBM XIV rebuild in only a few minutes addresses this "human factor".
In his post [XIV drive management], fellow blogger Jim Kelly (IBM) covers a variety of reasons why storage admins feel double drive failures are more than just random chance. XIV avoids load stress normally associated with traditional RAID rebuild by evenly spreading out the workload across all drives. This is known in the industry as "wear-leveling". When the first drive fails, the recovery is spread across the remaining 179 drives, so that each drive only processes about 1 percent of the data. The [Ultrastar A7K1000] 1TB SATA disk drives that IBM uses from HGST have specified 1.2 million hours mean-time-between-failures [MTBF] would average about one drive failing every nine months in a 180-drive XIV system. However, field experience shows that an XIV system will experience, on average, one drive failure per 13 months, comparable to what companies experience with more robust Fibre Channel drives. That's innovative XIV wear-leveling at work!
Fact: In the highly unlikely event that a DDF were to occur, you will have full read/write access to nearly all of your data on the XIV, all but a few GB.
Even though it has NEVER happened in the field, some clients and prospects are curious what a double drive failure on an XIV would look like. First, a critical alert message would be sent to both the client and IBM, and a "union list" is generated, identifying all the chunks in common. The worst case on a 15-module XIV fully loaded with 79TB data is approximately 9000 chunks, or 9GB of data. The remaining 78.991 TB of unaffected data are fully accessible for read or write. Any I/O requests for the chunks in the "union list" will have no response yet, so there is no way for host applications to access outdated information or cause any corruption.
(One blogger compared losing data on the XIV to drilling a hole through the phone book. Mathematically, the drill bit would be only 1/16th of an inch, or 1.60 millimeters for you folks outside the USA. Enough to knock out perhaps one character from a name or phone number on each page. If you have ever seen an actor in the movies look up a phone number in a telephone booth then yank out a page from the phone book, the XIV equivalent would be cutting out 1/8th of a page from an 1100 page phone book. In both cases, all of the rest of the unaffected information is full accessible, and it is easy to identify which information is missing.)
If the second drive failed several minutes after the first drive, the process for full redundancy is already well under way. This means the union list is considerably shorter or completely empty, and substantially fewer chunks are impacted. Contrast this with RAID-5, where being 99 percent complete on the rebuild when the second drive fails is just as catastrophic as having both drives fail simultaneously.
Fact: After a DDF event, the files on these few GB can be identified for recovery.
Once IBM receives notification of a critical event, an IBM engineer immediately connects to the XIV using remote service support method. There is no need to send someone physically onsite, the repair actions can be done remotely. The IBM engineer has tools from HGST to recover, in most cases, all of the data.
Any "union" chunk that the HGST tools are unable to recover will be set to "media error" mode. The IBM engineer can provide the client a list of the XIV LUNs and LBAs that are on the "media error" list. From this list, the client can determine which hosts these LUNs are attached to, and run file scan utility to the file systems that these LUNs represent. Files that get a media error during this scan will be listed as needing recovery. A chunk could contain several small files, or the chunk could be just part of a large file. To minimize time, the scans and recoveries can all be prioritized and performed in parallel across host systems zoned to these LUNs.
As with any file or volume recovery, keep in mind that these might be part of a larger consistency group, and that your recovery procedures should make sense for the applications involved. In any case, you are probably going to be up-and-running in less time with XIV than recovery from a RAID-5 double failure would take, and certainly nowhere near "beyond repair" that other vendors might have you believe.
Fact: This does not mean you can eliminate all Disaster Recovery planning!
To put this in perspective, you are more likely to lose XIV data from an earthquake, hurricane, fire or flood than from a double drive failure. As with any unlikely disaster, it is best to have a disaster recovery plan than to hope it never happens. All disk systems that sit on a single datacenter floor are vulnerable to such disasters.
For mission-critical applications, IBM recommends using disk mirroring capability. IBM XIV storage system offers synchronous and asynchronous mirroring natively, both included at no additional charge.
If you store your VMware bits on external SAN or NAS-based disk storage systems, this post is for you. The subject of the post, VM Volumes, is a potential storage management game changer!
Fellow blogger Stephen Foskett mentioned VM Volumes in his [Introducing VMware vSphere Storage Features] presentation at IBM Edge 2012 conference. His session on VMware's storage features included VMware APIs for Array Integration (VAAI), VMware Array Storage Awareness (VASA), vCenter plug-ins, and a new concept he called "vVol", now more formally known as VM Volumes. This post provides a follow-up to this, describing the VM Volumes concepts, architecture, and value proposition.
"VM Volumes" is a future architecture that VMware is developing in collaboration with IBM and other major storage system vendors. So far, very little information about VM Volumes has been released. At VMworld 2012 Barcelona, VMware highlights VM Volumes for the first time and IBM demonstrates VM Volumes with the IBM XIV Storage System (more about this demo below). VM Volumes is worth your attention -- when it becomes generally available, everyone using storage arrays will have to reconsider their storage management practices in a VMware environment -- no exaggeration!
But enough drama. What is this all about?
(Note: for the sake of clarity, this post refers to block storage only. However, the VM Volumes feature applies to NAS systems as well. Special thanks to Yossi Siles and the XIV development team for their help on this post!)
The VM Volumes concept is simple: VM disks are mapped directly to special volumes on a storage array system, as opposed to storing VMDK files on a vSphere datastore.
The following images illustrate the differences between the two storage management paradigms.
You may still be asking yourself: bottom line, how will I benefit from VM Volumes?
Well, take a VM snapshot for example. With VM Volumes, vSphere can simply offload the operation by invoking a hardware snapshot of the hardware volume. This has significant implications:
VM-Granularity: Only the right VMs are copied (with datastores, backing up or cloning individual-VM portions of hardware snapshot of a datastore would require more complex configuration, tools and work)
Hardware Offload: No ESXi server resources are consumed
XIV advantage: With XIV, snapshots consume no space upfront and are completed instantly.
Here's the first takeaway: With VM Volumes, advanced storage services (which cost a lot when you buy a storage array), will become available at an individual VM level. In a cloud world, this means that applications can be provisioned easily with advanced storage services, such as snapshots and mirroring.
Now, let's take a closer look at another relevant scenario where VM Volumes will make a lot of difference - provisioning an application with special mirroring requirements:
VM Volumes case: The application is ordered via the private cloud portal. The requestor checks a box requesting an asynchronous mirror. He changes the default RPO for his needs. When the request is submitted, the process wraps up automatically: Volumes are created on one of the storage arrays, configured with a mirror and RPO exactly as specified. A few minutes later, the requestor receives an automatic mail pointing to the application virtual machine.
Datastores case #1: As may be expected, a datastore that is mirrored with the special RPO does not exist. As a result, the automated workflow sets a pending status on the request, creates an urgent ticket to a VMware administrator and aborts. When the VMware admin handles that ticket, she re-assigns the ticket to the storage administrator, asking for a new volume which is mirrored with the special RPO, and mapped to the right ESXi cluster. The next day, the volume is created; the ticket is re-assigned to the storage admin, with the new LUN being pointed to. The VMware administrator follows and creates the datastore on top of it. Since the automated workflow was aborted, the admin re-assigns the ticket to the cloud administrator, who sometime later completes the application provisioning manually.
Datastores case #2: Luckily for the requestor, a datastore that is mirrored with the special RPO does exist. However, that particular datastore is consuming space from a high performance XIV Gen3 system with SSD caching, while the application does not require that level of performance, so the workflow requires a storage administrator approval. The approval is given to save time, but the storage administrator opens a ticket for himself to create a new volume on another array, as well as a follow-up ticket for the VMware admin to create a new datastore using the new volume and migrate the application to the other datastore. In this case, provisioning was relatively rapid, but required manual follow up, involving the two administrators.
Here's the second takeaway: With VM Volumes, management is simplified, and end-to-end automation is much more applicable. The reason is that there are no datastores. Datastores physically group VMs that may otherwise be totally unrelated, and require close coordination between storage and VMware administrators.
Now, the above mainly focuses on the VMware or cloud administrator perspective. How does VM Volumes impact storage management?
VM's are the new hosts: Today, storage administrators have visibility of physical hosts in their management environment. In a non-virtualized environment, this visibility is very helpful. The storage administrator knows exactly which applications in a data center are storage-provisioned or affected by storage management operations because the applications are running on well-known hosts. However, in virtualized environments the association of an application to a physical host is temporary. To keep at least the same level of visibility as in physical environments, VMs should become part of the storage management environment, like hosts. Hosts are still interesting, for example to manage physical storage mapping, but without VM visibility, storage administrators will know less about their operation than they are used to, or need to. VM Volumes enables such visibility, because volumes are provided to individual VMs. The XIV VM Volumes demonstration at VMworld Barcelona, although experimental, shows a view of VM volumes, in XIV's management GUI.
Here's a screenshot:
That's not all!
Storage Profiles and Storage Containers: A Storage Profile is a vSphere specification of a set of storage services. A storage profile can include properties like thin or thick provisioning, mirroring definition, snapshot policy, minimum IOPS, etc.
Storage administrators define a portfolio of supported storage services, maintained as a set of storage profiles, and published (via VASA integration) to vSphere.
VMware or cloud administrators define the required storage profiles for specific applications
VMware and storage administrators need to coordinate the typical storage requirements and the automatically-available storage services. When a request to provision an application is made, the associated storage profiles are matched against the published set of available storage profiles. The matching published profiles will be used to create volumes, which will be bound to the application VMs. All that will happen automatically.
Note that when a VM is created today, a datastore must be specified. With VM Volumes, a new management entity called Storage Container (also known as Capacity Pool) replaces the use of datastore as a management object. Each Storage Container exposes a subset of the available storage profiles, as appropriate. The storage container also has a capacity quota.
Here are some more takeaways:
New way to interface vSphere and storage management: Storage administrators structure and publish storage services to vSphere via storage profiles and storage containers.
Automated provisioning, out of the box: The provisioning process automatically matches application-required storage profiles against storage profiles available from the specified storage containers. There is no need to build custom scripts and custom processes to automate storage provisioning to applications
The XIV advantage:
XIV services are very simple to define and publish. The typical number of available storage profiles would be low. It would also be easy to define application storage profiles.
XIV provides consistent high performance, up to very high capacity utilization levels, without any maintenance. As a result, automated provisioning (which inherently implies less human attention) will not create an elevated risk of reduced performance.
Note: A storage vendor VASA provider is required to support VM Volumes, storage profiles, storage containers and automated provisioning. The IBM Storage VASA provider runs as a standalone service that needs to be deployed on a server.
To summarize the VM Volumes value proposition:
Streamline cloud operation by providing storage services at VM and application level, enabling end-to-end provisioning automation, and unifying VMware and storage administration around volumes and VMs.
Increase storage array ROI, improve vSphere scalability and response time, and reduce cloud provisioning lag, by offloading VM-level provisioning, failover, backup, storage migration, storage space recycling, monitoring, and more, to the storage array, using advanced storage operations such as mirroring and snapshots.
Simplify the adoption of VM Volumes using XIV, with smaller and simpler sets of storage profiles. Apply XIV's supreme fast cloning to individual VMs, and keep automation risks at bay with XIV's consistent high performance.
Until you can get your hands on a VM Volumes-capable environment, the VMware and IBM developer groups will be collaborating and working hard to realize this game-changing feature. The above information is definitely expected to trigger your questions or comments, and our development teams are eager to learn from them and respond. Enter your comments below, and I will try to answer them, and help shape the next post on this subject. There's much more to be told.
This month, I am pleased to announce the new [IBM STG Executive Briefing Center] website, representing a huge improvement over the previous website we had been using over the past two years. STG refers to IBM's Systems and Technology Group, the division that focuses on servers, storage, switches and the system software that makes them run. This new website is for the dozen STG EBCs that span the globe. The new website reminds me of this famous quote:
"Perfection is achieved, not when there is nothing left to add, but when there is nothing left to take away"
-- Antoine de Saint-Exupery
Let's take a quick look at what makes it so much better.
The previous website required registration. At every briefing, those of us who work in the EBCs had to pass around a sign-up sheet for email addresses from each attendee so that we could send them an invitation to register for the site. We would have a hard time reading people's handwriting, resulting in some emails coming back rejected.
Inspired by self-service gas stations, automated teller machines, and the many self-service portals of Cloud Computing, the new website has everything up-front, without registration. IBM Business Partners and sales representatives can easily request a briefing at any of the dozen briefing centers represented!
IBM-managed and IBM-hosted
We had a difficult time explaining to our attendees why our previous website was hosted on a lone machine and maintained by a third party. Think about it, IBM manages the data centers of over 400 clients. IBM has provided web hosting to the most mission critical workloads, with high levels of availability and reliability, and is recognized as one of the "Big 5" Cloud companies. I have done web design myself in my career, and we were terribly disappointed with the third party chosen to create and maintain our previous website, constantly having to point out errors in their HTML and CSS.
For the new website, IBM took back control. Staff from each EBC, myself included, came up with a simple page to bring the essence of each location to life. Special thanks to my colleage Hal Jennings, from the Austin EBC, for bringing this altogether!
Despite two years of manually registering attendees to use the previous website, Google Analytics showed that few people visited, and the few that did spent little time exploring the vast repository of content.
The new website is vastly simpler. The front page points to all twelve EBCs, and a single mouse click gets you to the location you are interested in, with all the details you need to make a decision to book a briefing, and the contact information to make it happen.
Elimination of Wasted and Duplicate Effort
In the previous website, we spent as much as 15 hours just to create, voice over, edit and produce a single 15-minute recorded presentation. Less than six percent of the previous website visitors watched more than five minutes of these videos, making us feel that most of our effort was wasted.
The EBC staff kept wasting their time, month after month, thanks to all-stick, no-carrot tactics that mandated minimums for contributions for more and more content that nobody was ever looking at. Even more disappointing was that much of our work duplicated the formal responsibilities of our IBM Marketing team. They weren't happy about this either, causing confusion between the roles of our two teams.
Finally, we said enough was enough! The new STG EBC website is a marvel in minimalism. If you want to see presentations, videos, expert profiles, or partake in on-going conversations, I welcome you to visit the [IBM Expert Network], the [IBM Storage YouTube Channel], and the [Storage Community] where they belong.
Can Structured Query Language [SQL] be considered a storage protocol?
Several months ago, I was asked to review a book on SQL, titled appropriately enough "The Complete Idiot's Guide to SQL", by Steven Holzner, Ph.D. As a published author myself, I get a lot of these requests, and I agreed in this case, given that SQL was invented by IBM, and is a good fundamental skill to have for Business Analytics and Database Management.
(FTC Disclosure: I work for IBM but was not part of the SQL development team. I was provided a copy of this book for free to review it. I was not paid to mention this book, nor told what to write. I do not know the author personally nor anyone that works for his publicist. All of my opinions of the book in this blog post are my own.)
Despite an agreed-upon standard for SQL, each relational database management system (RDBMS) has decided to customize it for their own purposes. First, SQL can be quite wordy, so some RDBMS have made certain keywords optional. Second, RDBMS offer extra features by adding keywords or programming language extentions, options or parameters above and beyond what the SQL standard calls for. Third, the SQL standard has changed over the years, and some RDBMS have opted to keep some backward compatibility with their prior releases. Fourth, some RDBMS want to discourage people from easily porting code from one RDBMS to another, known in the industry as vendor lock-in.
Throughout my career, I have managed various databases, including Informix, DB2, MySQL, and Microsoft SQL Server, so I am quite familiar with the differences in SQL and the problems and implications that arise.
Most authors who want to write about SQL typically make a choice between (a) stick to the SQL standard, and expect the reader to customize the examples to their particular DBMS; or (b) stick to a single RDBMS implemenation, and offer examples that may not work on other RDBMS.
I found the book "The Complete Idiot's Guide to SQL" covered the basics quite well, but with an odd twist. The basics include creating databases and tables, defining columns, inserting and deleting rows, updating fields, and performing queries or joins. The odd twist is that Steven does not make the typical choice above, but rather shows how the various DBMS are different than standard SQL syntax, with actual working examples for different RDBMS.
You might be thinking to yourself that only an idiot would work in a place that had to require knowledge of multiple RDBMS. The sad truth is that most of the medium and large companies I speak to have two or more in production. This is either through acquisitions, or in some cases, individual business units or departments implementing their own via the [Shadow IT].
(For those who want to learn SQL and try out the examples in this book, IBM offers a free version of DB2 called [DB2-C Express] that runs on Windows, Linux, Mac OS, and Solaris.)
Last week, while I was in Russia for the [Edge Comes to You] event, I was interviewed by a journalist from [Storage News] on various topics. One question stuck me as strange. He asked why I did not mention IBM's acquisition of Netezza in my keynote session about storage. I had to explain that Netezza was not in the IBM System Storage product line, it is in a different group, under Business Analytics, where it belongs.
While it is true that Netezza can store data, because it has storage components inside, the same could also be said about nearly every other piece of IT equipment, from servers with internal disk, to digital cameras, smart phones and portable music players. They can all be considered storage devices, but doing so would undermine what differentiates them from one another.
Which brings me back to my original question: Should we consider SQL to be a storage protocol? For the longest time, IT folks only considered block-based interfaces as storage protocols, then we added file-based interfaces like CIFS and NFS, and we also have object-based interfaces, such as IBM's Object Access Method (OAM) and the System Storage Archive Manager (SSAM) API. Could SQL interfaces be the next storage protocol?
Let me know what you think on this. Leave a comment below.
This week, I am in beautiful Sao Paulo, Brazil, teaching Top Gun class to IBM Business Partners and sales reps. Traditionally, we have "Tape Thursday" where we focus on our tape systems, from tape drives, to physical and virtual tape libraries. IBM is the number #1 tape vendor, and has been for the past eight years.
(The alliteration doesn't translate well here in Brazil. The Portuguese word for tape is "fita", and Thursday here is "quinta-feira", but "fita-quinta-feira" just doesn't have the same ring to it.)
In the class, we discussed how to handle common misperceptions and myths about tape. Here are a few examples:
Myth 1: Tape processing is manually intensive
In my July 2007 blog post [Times a Million], I coined the phrase "Laptop Mentality" to describe the problem most people have dealing with data center decisions. Many folks extend linearly their experiences using their PCs, workstations or laptops to apply to the data center, unable to comprehend large numbers or solutions that take advantage of the economies of scale.
For many, the only experience dealing with tape was manual. In the 1980s, we made "mix tapes" on little cassettes, and in the 1990s we recorded our favorite television shows on VHS tapes in the VCR. Today, we have playlists on flash or disk-based music players, and record TV shows on disk-based video recorders like Tivo. The conclusion is that tapes are manual, and disk are not.
Manual processing of tapes ended in 1987, with the introduction of a silo-like tape library from StorageTek. IBM quickly responded with its own IBM 3495 Tape Library Data Server in 1992. Today, clients have many tape automation choices, from the smallest IBM TS2900 Tape Autoloader that has one drive and nine cartridges, all the way to the largest IBM TS3500 multiple-library shuttle complex that can hold exabytes of data. These tape automation systems eliminate most of the manual handling of cartridges in day-to-day operations.
Myth 2: Tape media is less reliable than disk media
For any storage media to be unreliable is to return the wrong information that is different than what was originally stored. There are only two ways for this to happen: if you write a "zero" but read back a "one", or write a "one" and read a "zero". This is called a bit error. Every storage media has a "bit error rate" that is the average likelihood for some large amount of data written.
According to the latest [LTO Bit Error rates, 2012 March], today's tape expects only 1 bit error per 10E17 bits written (about 100 Petabytes). This is 10 times more reliable than Enterprise SAS disk (1 bit per 10E16), and 100 times more reliable than Enterprise-class SATA disk (1 bit per 10E15).
Tape is the media used in "black boxes" for airplanes. When an airplane crashes, the black box is retrieved and used to investigate the causes of the crash. In 1986, the Space Shuttle Challenger exploded 73 seconds after take-off. The tapes in the black box sat on the ocean floor for six weeks before being recovered. Amazingly, IBM was able to successfully restore [90 percent of the block data, and 100 percent of voice data].
Analysts are quite upset when they are quoted out of context, but in this case, Gartner never said anything closely similar to this. Nor did the other analysts that Curtis investigated for similar claims. What Garnter did say was that disk provides an attractive alternative storage media for backup which can increase the performance of the recovery process.
Back in the 1990s, Savur Rao and I developed a patent to help backup DB2 for z/OS by using the FlashCopy feature of IBM's high-end disk system. The software method to coordinate the FlashCopy snapshots with the database application and maintain multiple versions was implemented in the DFSMShsm component of DFSMS. A few years later, this was part of a set of patents IBM cross-licensed to Microsoft for them to implement a similar software for Windows called Data Protection Manager (DPM). IBM has since introduced its own version for distributed systems called IBM Tivoli FlashCopy Manager that runs not just on Windows, but also AIX, Linux, HP-UX and Solaris operating systems.
Curtis suspects the "71 percent" citation may have been propogated by an ambitious product manager of Microsoft's Data Protection Manager, back in 2006, perhaps to help drive up business to their new disk-based backup product. Certainly, Microsoft was not the only vendor to disparage tape in this manner.
A few years ago, an [EMC failure brought down the State of Virginia] due to not just a component failure it its production disk system, but then made it worse by failing to recover from the disk-based remote mirror copy. Fortunately, the data was able to be restored from tape over the next four days. If you wonder why nobody at EMC says "Tape is Dead" anymore, perhaps it is because tape saved their butts that week.
(FTC Disclosure: I work for IBM and this post can be considered a paid, celebrity endorsement for all of the IBM tape and software products mentioned on this post. I own shares of stock in both IBM and Google, and use Google's Gmail for my personal email, as well as many other Google services. While IBM, Google and Microsoft can be considered competitors to each other in some areas, IBM has working relationships with both companies on various projects. References in this post to other companies like EMC are merely to provide illustrative examples only, based on publicly available information. IBM is part of the Linear Tape Open (LTO) consortium.)
Myth 4: Vendors and Manufacturers are no longer investing in tape technology
IBM and others are still investing Research and Development (R&D) dollars to improve tape technology. What people don't realize is that much of the R&D spent on magnetic media can be applied across both disk and tape, such as IBM's development of the Giant Magnetoresistance read/write head, or [GMR] for short.
Most recently, IBM made another major advancement with tape with the introduction of the Linear Tape File Systems (LTFS). This allows greater portability to share data between users, and between companies, but treating tape cartridges much like USB memory sticks or pen drives. You can read more in my post [IBM and Fox win an Emmy for LTFS technology]!
Next month, IBM celebrates the 60th anniversary for tape. It is good to see that tape continues to be a vibrant part of the IT industry, and to IBM's storage business!
Well, it's Tuesday again, and you know what that means!
This Thursday is the Thanksgiving holiday here in the United States, so instead of announcing IBM products, I wanted to announce the general availability of my latest book, [Inside System Storage: Volume III].
This book includes blog posts from May 2008 to March 2009, along with the ever popular behind-the-scenes commentary on what was going on during IBM's launch of the Information Infrastructure initiative.
Do you know someone who celebrates Chanukah, Christmas, Kwanza, or the Winter Solstice, and have a hard time finding the right gift?
Do you know a client or IBM Business Partner that would appreciate a nominally-priced gift to thank them for their business?
Do you know someone newly hired into IBM or another IT company that could benefit from behind-the-scenes insight and commentary?
As with the other two volumes, Inside System Storage: Volume III is available in your choice of paperback, hardcover, and eBook (Adobe PDF) format.
In the spirit of Thanksgiving, I would like to thank my editor, Susan Pollard, who put in the extra effort, working evenings and weekends, to get this book done in time for the upcoming holiday season. For those outside the United States, there is an American tradition to shop in brick-and-mortar stores on Black Friday (the day after Thanksgiving) and to shop on-line for books like mine on Cyber Monday (the Monday after Thanksgiving).
I would also like to thank my publisher, Lulu.com, for upgrading me to "Spotlight" level, so now I have a spotlight page titled [Books Written by Tony Pearson], making it easy for you to order any of my books in various formats.
And last, but not least, I would like to thank all my friends and family that were supportive these past few difficult months while I was putting this book together.
Next month, I will be in Las Vegas, Dec 4-8, speaking at Gartner's [Data Center Conference]. If you order a book today, and bring it with you to the IBM booth at the Solution Expo, I can sign it for you!