Tony Pearson is a Master Inventor and Senior IT Architect for the IBM Storage product line at the
IBM Executive Briefing 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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This week was the IBM Pulse 2011 converence in Las Vegas, Nevada, with over 7,000 attendees. I wasn't there, and my on-the-scene correspondent was too busy running the hands-on lab to get out and attend sessions. Fortunately, I was able to watch some of the [IBM Software live stream], and here are my thoughts and observations.
Fellow inventor [Dean Kamen] was the keynote speaker. His inventions help people, making the world a better place. Here are three examples I found interesting during his talk:
Helping third world countries
Dean started out with his favorite quote:
"A problem well defined is a problem half-solved." - John Dewey
Dean mentioned that we are fortunate, having both potable drinking water and a reliable supply of electricity, but 2 to 4 billion people on the planet do not. Sponsored by Coca-Cola, Dean and his team of innovators were able to come up with small units that can be placed in a village or town. One unit takes in wet liquid and produces potable drinking water. The other unit takes combustible materials, like cow dung, and products electricity. Each unit is roughly the size of half a standard server rack. What does Coca-Cola get out of this? New "vending machines"! By combining drinking water with flavored syrups, they can create soft drinks on demand.
Dean's opinion was that if you want something done, you need to work with large corporations, as governments are mired in beauracracy and rules. I agree. When I first joined IBM, I was introduced to [TRIZ] which was a systematic method for solving problems. IBM's best and brightest are working to solve some of the toughest computer science challenges. For more on TRIZ, see this blog post about [TRIZ in BusinessWeek].
Helping injured veterans
Dean Kamen is well known for inventing the two-wheeled [Segway Personal Transporter], but his company, [DEKA], makes all kinds of things, mostly medical equipment. To help wounded soldiers returning from Iraq or Afghanistan without one or both arms, Dean and his team developed a robotic arm that has enough motor dexterity to pick up a raisin or grape off the table without dropping or squashing it. Dean has appeared several times on the Colbert Report, and here is a video of the robotic arm:
I have myself enjoyed riding a Segway. A local place in Tucson uses them to lead tourists through downtown Tucson and the University of Arizona campus.
Helping young students to learn science and technology
Dean wrapped up his talking by talking about his passion about "For Inspiration and Recognition of Science and Technology" or [FIRST]. Modeled after sports competitions, FIRST encourages teams of kids to build robots that perform specific tasks. Every year, companies and universities sponsor teams by purchasing robot kits from FIRST. Teams compete in regional competitions, and then the best of those go on to compete in a stadium in Atlanta, Georgia, hosting 76,000 people cheering for their teams.
Unlike other school sports (Football, Basketball, Baseball, etc.) where a student is more likely to win the lottery than get a successful career as a professional athlete, every student involved in FIRST competitions can "go pro". A study of FIRST success tracked students who participated in competitions, and found a substantial improvement in percentage of those students attending college and working as science and engineering professionals.
I am a big fan of encouraging kids of all ages to learn more about science, technology, engineering and math [STEM]. Back in 2009, I blogged about my involvement with [One Laptop Per Child] and [Junior FIRST Lego League]. I've gotten a great reaction to my latest challenge, to build a Watson Jr. in your own basement, based on my [step-by-step] instructions.
If you attended IBM Pulse this week, please comment on your thoughts and observations!
My series last week on IBM Watson (which you can read [here], [here], [here], and [here]) brought attention to IBM's Scale-Out Network Attached Storage [SONAS]. IBM Watson used a customized version of SONAS technology for its internal storage, and like most of the components of IBM Watson, IBM SONAS is commercially available as a stand-alone product.
Like many IBM products, SONAS has gone through various name changes. First introduced by Linda Sanford at an IBM SHARE conference in 2000 under the IBM Research codename Storage Tank, it was then delivered as a software-only offering SAN File System, then as a services offering Scale-out File Services (SoFS), and now as an integrated system appliance, SONAS, in IBM's Cloud Services and Systems portfolio.
If you are not familiar with SONAS, here are a few of my previous posts that go into more detail:
This week, IBM announces that SONAS has set a world record benchmark for performance, [a whopping 403,326 IOPS for a single file system]. The results are based on comparisons of publicly available information from Standard Performance Evaluation Corporation [SPEC], a prominent performance standardization organization with more than 60 member companies. SPEC publishes hundreds of different performance results each quarter covering a wide range of system performance disciplines (CPU, memory, power, and many more). SPECsfs2008_nfs.v3 is the industry-standard benchmark for NAS systems using the NFS protocol.
(Disclaimer: Your mileage may vary. As with any performance benchmark, the SPECsfs benchmark does not replicate any single workload or particular application. Rather, it encapsulates scores of typical activities on a NAS storage system. SPECsfs is based on a compilation of workload data submitted to the SPEC organization, aggregated from tens of thousands of fileservers, using a wide variety of environments and applications. As a result, it is comprised of typical workloads and with typical proportions of data and metadata use as seen in real production environments.)
The configuration tested involves SONAS Release 1.2 on 10 Interface Nodes and 8 Storage Pods, resulting a single file system over 900TB usable capacity.
10 Interface Nodes; each with:
Maximum 144 GB of memory
One active 10GbE port
8 Storage Pods; each with:
2 Storage nodes and 240 drives
Drive type: 15K RPM SAS hard drives
Data Protection using RAID-5 (8+P) ranks
Six spare drives per Storage Pod
IBM wanted a realistic "no compromises" configuration to be tested, by choosing:
Regular 15K RPM SAS drives, rather than a silly configuration full of super-expensive Solid State Drives (SSD) to plump up the results.
Moderate size, typical of what clients are asking for today. The Goldilocks rule applies. This SONAS is not a small configuration under 100TB, and nowhere close to the maximum supported configuration of 7,200 disks across 30 Interface Nodes and 30 Storage Pods.
Single file system, often referred to as a global name space, rather than using an aggregate of smaller file systems added together that would be more complicated to manage. Having multiple file systems often requires changes to applications to take advantage of the aggregate peformance. It is also more difficult to load-balance your performance and capacity across multiple file systems. Of course, SONAS can support up to 256 separate file systems if you have a business need for this complexity.
The results are stunning. IBM SONAS handled three times more workload for a single file system than the next leading contender. All of the major players are there as well, including NetApp, EMC and HP.
The IBM Challenge was a big success. One of the contestants, Ken Jennings, [welcomes our new computer overlords]. Congratulations are in order to the IBM Research team who pulled off this Herculean effort!
Some folks have poked fun at some of the odd responses and wager amounts from the IBM Watson computer during the three-day tournament. Others were surprised as I was that the impressive feat was done with less than 1TB of stored data. Here is what John Webster wrote in CNET yesterday, in hist article [What IBM's Watson says to storage systems developers]:
"All well and good. But here's what I find most interesting as a result of what IBM has done in response to the Grand Challenge that motivated Watson's creators. We know, from Tony Pearson's blog, that the foundation of Watson's data storage system is a modified IBM SONAS cluster with a total of 21.6TB of raw capacity. But Pearson also reveals another very significant, and to me, surprising data point: "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 1 Terabyte."
What Pearson just said is that the data set Watson actually uses to reach his push-the-button decision would fit on a 1TB drive. So much for big data?"
To better appreciate how difficult the challenge was, and how a small amount of data can answer a billion different questions, I thought I would cover Business Intelligence, Data Retrieval and Text Mining concepts.
"In this paper, business is a collection of activities carried
on for whatever purpose, be it science, technology,
commerce, industry, law, government, defense, et cetera.
The communication facility serving the conduct of a business
(in the broad sense) may be referred to as an intelligence
system. The notion of intelligence is also defined
here, in a more general sense, as the ability to apprehend
the interrelationships of presented facts in such a way as
to guide action towards a desired goal."
Ideally, when you need "Business Intelligence" to help you make a better decision, you perform data retrieval from a structured database for the specific information you are looking for. In other cases, you might be looking for insight, patterns or trends. In that case, you go "data mining" against your structured databases.
Here's a simple example. John runs a fruit stand. One day, he kept track of how many apples and oranges were bought by men and women. How many questions can we ask against this small set of data? Let's count them:
How many apples were sold to men?
How many apples were sold to women?
How many oranges were sold to men?
How many oranges were sold to women?
But wait! For each row and column, we can combine them into totals.
How many apples were sold in total?
How many oranges were sold in total?
How many fruit in total were sold to men?
How many fruit in total were sold to women?
How many fruit in total were sold?
But wait, there's more! Each row and column can be evaluated for relative percentages, as well as percentages of each cell compared to the total. You could make five relevant pie-charts from this data. This results in 16 more questions, such as:
Of the fruit purchased by men, what percentage for apples?
Of all the apples purchased, what percentage by women?
And that's not including more ethereal questions, such as:
Are there gender-specific preferences for different types of fruit?
What type of fruit do men prefer?
This is just for a small set, two market segments (by gender) and two products (apples and oranges). However, if you have many market segments (perhaps by age group, zip code, etc.) and many products, the number of queries that can be supported is huge. For small sets of data, you can easily do this with a spreadsheet program like IBM Lotus Symphony or Microsoft Excel.
But why limit yourself to two dimensions? The above example was just for one day's worth of activity, if John captures this data for every day for historical and seasonal trending, it can be represented as a three-dimensional cube. The number of queries becomes astronomical. This is the basis for Online Analytical Processing (OLAP), and three-dimensional tables are often referred to as [OLAP cubes].
Back in 1970, IBM invented the Structured Query Language [SQL], and today, nearly all modern relational databases support this, including IBM DB2, Informix, Microsoft SQL Server, and Oracle DB. SQL poses two challenges. First, you had to structure the data in advance to the way you expect to perform your ad-hoc queries. Deciding the groups and categories in advance can limit the way information is recorded and captured.
Second, you had to be skilled at SQL to phrase your queries correctly to retrieve the data you are after. What ended up happening was that skilled SQL programmers would develop "canned reports" with fixed SQL parameters, so that less-skilled business decision makers could base their decisions from these reports.
IBM has fully integrated stacks to help process structured data, combining servers, storage, and advanced analytics software into a complete appliance. IBM offers the [Smart Analytics System] for robust, customized deployments, and recently acquired [Netezza] for pre-configured, and more rapid deployments.
However, the bigger problem is that more than 80 percent of information is not structured!
Semi-structured data like email provides some searchable fields like From and Subject. The rest of the information is unstructured, such as text files, photographs, video and audio. To look for specific information in unstructured sources can be like looking for a needle in a haystack, and trying to get insight, patterns or trends involves text mining.
This, in effect, is what IBM Watson was able to perform so well this week. Finding the needle in the haystacks of unstructured data from 200 million pages of text stored in its system, combined with the ability to apprehend the interrelationships of meaning and subtle nuance, resulted in an impressive technology demonstration. Certainly, this new technology will be powerful for a variety of use cases across a broad set of industries!
The Tucson Executive Briefing Center hosted 20 dignitaries from local companies and academia.
This is a historic competition, an exhibition match pitting a computer against the top two celebrated Jeopardy champions:
Brad Rutter, won $3.2 million USD on Jeopardy!, winning 5 days on the show, and then three later tournamets.
Ken Jennings, winning $2.5 million in a 74-day winning streak on Jeopardy!
One of the members of the audience had never seen an episode of Jeopardy! in his life.
(Note: there are NO SPOILERS in this blog post. If you have not yet watched the show, you are safe to continue reading the rest of this post. I will not
disclose the correct responses to any of the clues nor how well each contestant scored.)
Calline Sanchez, IBM Director, Systems Storage Development for Data Protection and Retention, kicked off today's ceremonies.
The IBM Watson computer, named after IBM founder Thomas J. Watson, has been developed over the past 4 years by a team of IBM scientists who set out to accomplish a grand challenge - build a computing system that rivals a human's ability to answer questions posed in natural language with speed, accuracy and confidence. IBM Research labs in the United States, Japan, China and Israel [collaborated with Artificial Intelligence (AI) experts at eight universities], including Massachusetts Institute of Technology (MIT), University of Texas (UT) at Austin, University of Southern California (USC), Rensselaer Polytechnic Institute (RPI), University at Albany (UAlbany), University of Trento (Italy), University of Massachusetts Amherst, and Carnegie Mellon University.
(Disclaimer: I attended the University of Texas at Austin. My father attended Carnegie Mellon University.)
Last week, NOVA on PBS had a special episode on the making of IBM Watson, you can [watch it online] on their website. Delaney Turner, IBM Social Media Communications Manager for Business Analytics Software, has posted [his observations of Nova].
Since IBM Watson is the size of 10 refrigerators and weighs over 14,000 pounds, it was easier to design the Jeopardy! set at the TJ Watson Research lab in Yorktown Heights, NY, than to ship it over to California where the show is normally recorded. Two of the visual designers that worked on this set, as well as on the visual appearance of Watson, live in Tucson and were part of our audience today.
The IBM Challenge consists of a two-game tournament, where the scores of both games will be added to determine winner rankings. The producers of Jeopardy! will give $1 million dollars USD to first place, $300,000 to second place, and $200,000 to third place. Regardless of outcome, [IBM will donate all of its winings to charity]. The two human contestants plan to donate half of their earnings to their favorite charities as well.
Jeopardy! The IBM Challenge
Alex Trebek introduces IBM Watson, explaining that it can neither hear nor see. It will receive all information electronically. Categories and clues will be sent as text files via TCP/IP over Ethernet at the same time the two human contestants see them so that all have the same time to think about the right answer.
Watson has two rows of five racks, back to back. This was done so that cold air could rise up from holes in the tile floors around the unit, and all the hot air would be forced into the center and up to the ceiling return. This technique is known as "hot aisle/cold aisle" design. Alex Trebek opens one of the rack doors to show a series of 4U-high IBM Power 750 servers.
The avatar is a representation of Watson, as the machine itself is too big to fit behind the podium. The avatar is IBM's "Smarter Planet" logo with orbiting streaks and circles. It shows "Green" when it has high confidence, and orange when it gets an answer wrong. When busy thinking, the streaks and circles speed up, the closest we will see to "watching a computer sweat."
During the show, an "Answer panel" shows Watson's top three candidate responses, with confidence level compared to its current "buzz threshold".
Watson knows what it knows, and knows what it doesn't know. Here is an [Interactive Watson Game] on New York Times website to give you an idea of how the answer panel works. I was impressed with how close all three candidate answers were. In a question about Olympic swimmers, all three candidates are Olympic swimmers. In a question about the novel "Les Miserables", all three candidates were characters of that novel.
Well, IBM Watson did well, but missed answered some questions incorrectly. This [parody Slate video] pokes fun at this. Here were some discussions we had after the show ended:
IBM did not do well in categories that required [abductive reasoning]. For example, to identify two or three things that happened in different years, and then postulate that what they all have in common is a specific decade (such as the 1950s) is difficult.
Watson does not hear the wrong answers from the two human contestants. For one question, Ken buzzes in first, guesses wrong, then Watson buzzes in with the same exact response. Alex Trebek rebukes Watson with "No, Ken just said that!" Brad would learn from their mistakes and guess correctly for the score.
Watson is provided the correct answer after a contestant guesses it correctly, or if nobody does, when Alex provides the correct response. This is sent as a text message to Watson immediately, so that it can use this information to adjust its algorithms and machine-learning for future clues in that same category. This was evident in the "Answer panel" on the fourth and fifth attempts on the category of "Decades".
With this demonstration, IBM Research has advanced science by leaps and bounds for the Articial Intelligence community. IBM is a leader in Business Analytics, and this technology will find uses in a variety of industries. The average knowledge worker spends 30 percent of her time looking for information on corporate data repositories. By demonstrating a computer that can provide answers quickly, employees will be more productive, make stronger business decisions, and have greater insight.
Day 1 was only able to cover the first round of Game 1. This allowed more time to talk about the history and technology of IBM Watson. Tomorrow, the contestants will finish Game 1 and head into Game 2.
"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 1 Terabyte (TB). 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."
I had several readers ask me to explain the significance of the "Terabyte". I'll work my way up.
A bit is simply a zero (0) or one (1). This could answer a Yes/No or True/False question.
Most computers have standardized a byte as a collection of 8 bits. There are 256 unique combinations of ones and zeros possible, so a byte could be used to storage a 2-digit integer, or a single upper or lower case character in the English alphabet. In pratical terms, a byte could store your age in years, or your middle initial.
The Kilobyte is a thousand bytes, enough to hold a few paragraphs of text. A typical written page could be held in 4 KB, for example.
The IBM Challenge to play on Jeopardy! is being compared to the historic 1969 moon landing. To land on the moon, Apollo 11 had the "Apollo Guidance Computer" (AGC) which had 74KB of fixed read-only memory, and 2KB of re-writeable memory. Over [3500 IBM employees were involved] to get the astronauts to the moon and safely back to earth again.
The importance of this computer was highlighted in a [lecture by astronaut David Scott] who said: "If you have a basketball and a baseball 14 feet apart, where the baseball represents the moon and the basketball represents the Earth, and you take a piece of paper sideways, the thinness of the paper would be the corridor you have to hit when you come back."
The Megabyte is a thousand KB, or a million bytes. The 3.5-inch floppy diskette, mentioned in my post [A Boxfull of Floppies] could hold 1.44MB, or about 360 pages of text.
In the article [Wikipedia as a printed book], the printing of a select 400 articles resulted in a book 29 inches thick. Those 5,000 pages would consume about 20 MB of space.
One of my favorite resources I use to search is the Internet Movie Data Base [IMDB]. Leaving out the photos and videos, the [text-only portion of the IMDB database is just over 600 MB], representing nearly all of the actors, awards, nominations, television shows and movies. A standard CD-ROM can hold 700MB, so the text portion of the IMDB could easily fit on a single CD.
The Gigabyte is a thousand MB, or a billion bytes. My Thinkpad T410 laptop has 4GB of RAM and 320GB of hard disk space. My laptop comes with a DVD burner, and each DVD can hold up to 4.7GB of information.
The popular Wikipedia now has some 17 million articles, of which 3.5 million are in English language. It would only take [14GB of space to hold the entire English portion] of Wikipedia. That is small enough to fit on twenty CDs, three DVDs, an Apple iPad or my cellphone (a Samsung Galaxy S Vibrant).
Perhaps you are thinking, "Someone should offer Wikipedia pre-installed on a small handheld!" Too late. The [The Humane Reader] is able to offer 5,000 books and Wikipedia in a small device that connects to your television. This would be great for people who do not have access to the internet, or for parents who want their kids to do their homework, but not be online while they are doing it.
In the latest 2009 report of [How Much Information?] from the University of California, San Diego, the average American consumes 34 GB of information. This includes all the information from radio, television, newspapers, magazines, books and the internet that a person might look at or listen to throughout the day. This project is sponsored by IBM and others to help people understand the nature of our information-consuption habits.
Back in 1992, I visited a client in Germany. Their 90 GB of disk storage attached to their mainframe was the size of three refrigerators, and took five full-time storage administrators to manage.
The Terabyte is a thousand GB, or a trillion bytes. It is now possible to buy external USB drive for your laptop or personal computer that holds 1TB or more. However, at 40MB/sec speeds that USB 2.0 is capable of, it would take seven hours to do a bulk transfer in or out of the device.
IBM offers 1TB and 2TB disk drives in many of our disk systems. In 2008, IBM was preparing to announce the first 1TB tape drive. However, Sun Microsystems announced their own 1TB drive the day before our big announcement, so IBM had to rephrase the TS1130 announcement to [The World's Fastest 1TB tape drive!]
A typical academic research library will hold about 2TB of information. For the [US Library of Congress] print collection is considered to be about 10TB, and their web capture team has collected 160TB of digital data. If you are ever in the Washington DC, I strongly recommend a visit to the Library of Congress. It is truly stunning!
Full-length computer animated movies, like [Happy Feet], consume about 100TB of disk storage during production. IBM offers disk systems that can hold this much data. For example, the IBM XIV can hold up to 151 TB of usable disk space in the size of one refrigerator.
A Key Performance Indicator (KPI) for some larger companies is the number of TB that can be managed by a full-time employee, referred to as TB/FTE. Discussions about TB/FTE are available from IT analysts including [Forrester Research] and [The Info Pro].
The website [Ancestry.com] claims to have over 540 million names in its genealogical database, with a storage of 600TB, with the inclusion of [US census data from 1790 to 1930]. The US government took nine years to process the 1880 census, so for the 1890 census, it rented equipment from Herman Hollerith's Tabulating Machine Company. This company would later merge with two others in 1911 to form what is now called IBM.
A Petabyte is thousand TB, or a quadrillion bytes. It is estimated that all printed materials on Earth would represent approximately 200 PB of information.
IBM's largest disk system, the Scale-Out Network Attach Storage (SONAS) comprised of up to 7,200 disk drives, which can hold over 11 PB of information. A smaller 10-frame model, the same size as IBM Watson, with six interface nodes and 19 storage pods, could hold over 7 PB of information.
For those of us in the IT industry, 1TB is small potatoes. I for one, was expecting it to be much bigger. But for everyone else, the equivalent of 200 million pages of text that IBM Watson has loaded inside is an incredibly large repository of information. I suspect IBM Watson probably contains the complete works of Shakespeare as well as other fiction writers, the IMDB database, all 3.5 million articles of Wikipedia, religious texts like the Bible and the Quran, famous documents like the Magna Carta and the US Constitution, and reference books like a Dictionary, a Thesaurus, and "Gray's Anatomy". And, of course, lots and lots of lists.
For those on Twitter, follow [@ibmwatson] these next three days during the challenge.