The technology industry is full of trade-offs. Take for example solar cells that convert sunlight to electricity. Every hour, more energy hits the Earth in the form of sunlight than the entire planet consumes in an entire year. The general trade-off is between energy conversion efficiency versus abundance of materials:
- Get 9-11 percent efficiency using rare materials like indium (In), gallium (Ga) or cadmium (Cd).
- Get only 6.7 percent efficiency using abundant materials like copper (Cu), tin (Sn), zinc (Zn), sulfur (S), and selenium (Se)
IBM has eliminated this trade-off with a record-setting breakthrough last week, demonstrating 9.6 percent efficiency [thin film solar cells using earth-abundant materials].
A second trade-off is exemplified by EMC's recent GeoProtect announcement. This appears similar to the geographic dispersal method introduced by a company called [CleverSafe]. The trade-off is between the amount of space to store one or more copies of data and the protection of data in the event of disaster. Here's an excerpt from fellow blogger Chuck Hollis (EMC) titled ["Cloud Storage Evolves"]:
"Imagine a average-sized Atmos network of 9 nodes, all in different time zones around the world. And imagine that we were using, say, a 6+3 protection scheme.
The implication is clear: any 3 nodes could be completely lost: failed, destroyed, seized by the government, etc.
-- and the information could be completely recovered from the surviving nodes."
For organizations worried about their information falling into the wrong hands (whether criminal or government sponsored!), any subset of the nodes would yield nothing of value -- not only would the information be presumably encrypted, but only a few slices of a far bigger picture would be lost.
Seized by the government? falling into the wrong hands? Is EMC positioning ATMOS as "Storage for Terrorists"? I can certainly appreciate the value of being able to protect 6PB of data with only 9PB of storage capacity, instead of keeping two copies of 6PB each, the trade-off means that you will be accessing the majority of your data across your intranet, which could impact performance. But, if you are in an illicit or illegal business that could have a third of your facilities "seized by the government", then perhaps you shouldn't house your data centers there in the first place. Having two copies of 6PB each, in two "friendly nations", might make more sense.
(In reality, companies often keep way more than just two copies of data. It is not unheard of for companies to keep three to five copies scattered across two or three locations. Facebook keeps SIX copies of photographs you upload to their website.)
ChuckH argues that the governments that seize the three nodes won't have a complete copy of the data. However, merely having pieces of data is enough for governments to capture terrorists. Even if the striping is done at the smallest 512-byte block level, those 512 bytes of data might contain names, phone numbers, email addresses, credit cards or social security numbers. Hackers and computer forensics professionals take advantage of this.
You might ask yourself, "Why not just encrypt the data instead?" That brings me to the third trade-off, protection versus application performance. Over the past 30 years, companies had a choice, they could encrypt and decrypt the data as needed, using server CPU cycles, but this would slow down application processing. Every time you wanted to read or update a database record, more cycles would be consumed. This forced companies to be very selective on what data they encrypted, which columns or fields within a database, which email attachments, and other documents or spreadsheets.
An initial attempt to address this was to introduce an outboard appliance between the server and the storage device. For example, the server would write to the appliance with data in the clear, the appliance would encrypt the data, and pass it along to the tape drive. When retrieving data, the appliance would read the encrypted data from tape, decrypt it, and pass the data in the clear back to the server. However, this had the unintended consequences of using 2x to 3x more tape cartridges. Why? Because the encrypted data does not compress well, so tape drives with built-in compression capabilities would not be able to shrink down the data onto fewer tapes.
(I covered the importance of compressing data before encryption in my previous blog post
[Sock Sock Shoe Shoe].)
Like the trade-off between energy efficiency and abundant materials, IBM eliminated the trade-off by offering compression and encryption on the tape drive itself. This is standard 256-bit AES encryption implemented on a chip, able to process the data as it arrives at near line speed. So now, instead of having to choose between protecting your data or running your applications with acceptable performance, you can now do both, encrypt all of your data without having to be selective. This approach has been extended over to disk drives, so that disk systems like the IBM System Storage DS8000 and DS5000 can support full-disk-encryption [FDE] drives.
Certainly, something to think about!
technorati tags: , sunlight, solar cells, electricity, indium, gallium, cadmium, copper, tin, zinc, sulfur, selenium, thin+film, efficiency, EMC, Chuck Hollis, GeoProtect, Cleversafe, governement, seizure, Facebook, terrorists, encryption, forensics, hackers, protection, performance, disk, tape
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?"
Craig Rhinehart offers
[10 Things You Need to Know About the Technology Behind Watson].
An artist has come up with this clever
Dr. Jon Lenchner from IBM Research has a series of posts on
[How Watson "sees", "hears", and "speaks"] and [Selected Nuances].
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.
On ZDnet, Steven J. Vaughan-Nichols welcomes our new [Linux Penguin Jeopardy overlords]. I have to say I share his enthusiasm!
technorati tags: IBM, Nova, Watson, #ibmwatson, Jeopardy, POWER7, p750, supercomputer, TeraFlops, disk, SONAS, GPFS, SAS, Craig Rhinehart, Jon Lenchner, Hans Morvec, Carnegie Mellon University, CMU
In my post [What is the Smartest Machine on Earth?], I described the storage inside [IBM Watson], the computer that will compete against two humans on the quiz show Jeopardy!
"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.
- Kilobyte (KB)
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."
- Megabyte (MB)
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.
The first commercial disk system, the [350 Disk Storage Unit, introduced in 1956 for the IBM RAMAC computer], could hold only 5 MB and was the size of two refrigerators. While 5MB might not seem like much today, it is enough to hold the [Complete works of Shakespeare]. That's right, all 42 plays, poems and sonnets.
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.
- Gigabyte (GB)
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.
- Terabyte (TB)
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!]
In his book [The Singularity is Near: When Humans Transcend Biology], Ray Kurzweil estimates the human brain's memory can hold about 1.25 TB of information. This would make IBM Watson about 80 percent human.
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.
- Petabyte (PB)
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.
IBM's automated [TS3500 tape library with high-density frames] can hold up to 60 PB of compressed tape data. A smaller 10-frame model, the size of IBM Watson, could hold up to 36 PB of data.
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.
technorati tags: IBM, Watson, Challenge, Jeopardy, @ibmwatson, #ibmwatson
A reader of my blog asked me what seemed like a simple enough question:
Whatever happened to Lotus Approach? I loved that personal db. (thoughit's been awhile...)
Of course, researching an answer, I encountered some interesting new information. Interestingly, everyone tries to "read between the lines" and tries to determine what solution is best.
From a colleague from Lotus:
You can still get [Lotus Approach] as part of Smartsuite.
However, I have to assume his real question is ... "what is the quick and easy way for me to build a lightweight database app like Microsoft Access that I can distribute as a standalone executable?"
To which I would say "Lotus has a program called Approach, which is part of Lotus SmartSuite, which some people still use. However, a lot of the focus in IBM now centers around the lightweight Cloudscape database which IBM acquired from Informix, which is now known as the [open source project called Derby]. Many IBM and Lotus products, such as Lotus Expeditor use the JDBC connection to Derby, which allows you to use Windows, Linux, Flash, etc. ... with no vendor lock in".
I am familiar with Cloudscape, and I evaluated it as a potential database for IBM TotalStorage Productivity Center, when I was the lead architect defining the version 1 release. It runs entirely on Java, which is both a plus and minus. Plus in that it runs anywhere Java runs, but a minus in that it is not optimized for high performance or large scalability. Because of this, we decided instead on using the full commercial DB2 database instead for Productivity Center.
Not to be undone, my colleagues over at DB2 offered a different alternative, [DB2 Express-C], which runs on a variety of Windows, Linux-x86, and Linux on POWER platforms. It is "free" as in beer, not free as in speech, which means you can download and use it today at no charge, and even ship products with it included, but you are not allowed to modify and distribute altered versions of it, as you can with "free as in speech" open source code, as in the case of Derby above (see [Apache License 2.0"] for details).
(If you have no idea what I am talking about in my distinction between "free speech" and "free beer", see Simon Phipps' article[Perspective: Free speech, free beer and free software] orthe definition from the [Free Software Foundation].)
As I see it, DB2 Express-C has two key advantages. First, if you like the free version, you can purchase a "support contract" for those that need extra hand-holding, or are using this as part of a commercial business venture. Second,for those who do prefer vendor lock-in, it is easyto upgrade Express-C to the full IBM DB2 database product, so if you are developing a product intended for use with DB2, you can develop it first with DB2 Express-C, and migrate up to full DB2 commercial version when you are ready.
This is perhaps more information than you probably expected for such a simple question. Meanwhile, I am stilltrying to figure out MySQL as part of my [OLPC volunteer project].
technorati tags: IBM, Lotus, Approach, SmartSuite, TotalStorage, Productivity Center, Cloudscape, Apache, Derby, free, speech, beer, DB2, Express-C, Windows, Linux, POWER, open source
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.
Let's start with Business Intelligence.
[Seth Grimes] pointed me to this quote from [A Business Intelligence System], written by Hans Peter Luhn back in October 1958 IBM Journal.
"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?
|Total||63||50%||63||50%||126||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.
(Photo courtesy of [OLAP, Cubes and Multidimensional Analysis] by Andrew Fryer.)
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.
IBM is a leader in Business Analytics and has made great progress in dealing with unstructured data. This includes [IBM OmniFind Enterprise Edition], [IBM e-Discovery Manager] and [IBM Cognos Business Intelligence].
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!
To learn more, read the Arizona Daily Star's article [After 'Jeopardy!' win, IBM program steps out].
technorati tags: IBM, Watson, Jeopardy, Challenge, John Webster, CNET, BI, data mining, Text Mining, OLAP, Arizona, Daily Star