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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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.
“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.
Have you ever noticed that sometimes two movies come out that seem eerily similar to each other, released by different studios within months or weeks of each other? My sister used to review film scripts for a living, she would read ten of them and have to pick her top three favorites, and tells me that scripts for nearly identical concepts came all the time. Here are a few of my favorite examples:
1994: [Wyatt Earp] and [Tombstone] were Westerns recounting the famed gunfight at the O.K. Corral. Tombstone, Arizona is near Tucson, and the gunfight is recreated fairly often for tourists.
1998: [Armageddon] and [Deep Impact] were a pair of disaster movies dealing with a large rock heading to destroy all life on earth. I was in Mazatlan, Mexico to see the latter, dubbed in Spanish as "Impacto Profundo".
1998: [A Bug's Life] and [Antz] were computer-animated tales of the struggle of one individual ant in an ant colony.
2000: [Mission to Mars] and [Red Planet] were sci-fi pics exploring what a manned mission to our neighboring planet might entail.
This is different than copy-cat movies that are re-made or re-imagined many years later based on the previous successes of an original. Ever since my blog post [VPLEX: EMC's Latest Wheel is Round] in 2010 comparing EMC's copy-cat product that came our seven years after IBM's SAN Volume Controller (SVC), I've noticed EMC doesn't talk about VPLEX that much anymore.
This week, IBM announced [XIV Gen3 Solid-State Drive support] and our friends over at EMC announced [VFCache SSD-based PCIe cards]. Neither of these should be a surprise to anyone who follows the IT industry, as IBM had announced its XIV Gen3 as "SSD-Ready" last year specifically for this purpose, and EMC has been touting its "Project Lightning" since last May.
Fellow blogger Chuck Hollis from EMC has a blog post [VFCache means Very Fast Cache indeed] that provides additional detail. Chuck claims the VFCache is faster than popular [Fusion-IO PCIe cards] available for IBM servers. I haven't seen the performance spec sheets, but typically SSD is four to five times slower than the DRAM cache used in the XIV Gen3. The VFCache's SSD is probably similar in performance to the SSD supported in the IBM XIV Gen3, DS8000, DS5000, SVC, N series, and Storwize V7000 disk systems.
Nonetheless, I've been asked my opinions on the comparison between these two announcements, as they both deal with improving application performance through the use of Solid-State Drives as an added layer of read cache.
(FTC Disclosure: I am both a full-time employee and stockholder of the IBM Corporation. The U.S. Federal Trade Commission may consider this blog post as a paid celebrity endorsement of IBM servers and storage systems. This blog post is based on my interpretation and opinions of publicly-available information, as I have no hands-on access to any of these third-party PCIe cards. I have no financial interest in EMC, Fusion-IO, Texas Memory Systems, or any other third party vendor of PCIe cards designed to fit inside IBM servers, and I have not been paid by anyone to mention their name, brands or products on this blog post.)
The solutions are different in that IBM XIV Gen3 the SSD is "storage-side" in the external storage device, and EMC VFCache is "server-side" as a PCI Express [PCIe] card. Aside from that, both implement SSD as an additional read cache layer in front of spinning disk to boost performance. Neither is an industry first, as IBM has offered server-side SSD since 2007, and IBM and EMC have offered storage-side SSD in many of their other external storage devices. The use of SSD as read cache has already been available in IBM N series using [Performance Accelerator Module (PAM)] cards.
IBM has offered cooperative caching synergy between its servers and its storage arrays for some time now. The predecessor to today's POWER7-based were the iSeries i5 servers that used PCI-X IOP cards with cache to connect i5/OS applications to IBM's external disk and tape systems. To compete in this space, EMC created their own PCI-X cards to attach their own disk systems. In 2006, IBM did the right thing for our clients and fostered competition by entering in a [Landmark agreement] with EMC to [license the i5 interfaces]. Today, VIOS on IBM POWER systems allows a much broader choice of disk options for IBM i clients, including the IBM SVC, Storwize V7000 and XIV storage systems.
Can a little SSD really help performance? Yes! An IBM client running a [DB2 Universal Database] cluster across eight System x servers was able to replace an 800-drive EMC Symmetrix by putting eight SSD Fusion-IO cards in each server, for a total of 64 Solid-State drives, saving money and improving performance. DB2 has the Data Partitioning Feature that has multi-system DB2 configurations using a Grid-like architecture similar to how XIV is designed. Most IBM System x and BladeCenter servers support internal SSD storage options, and many offer PCIe slots for third-party SSD cards. Sadly, you can't do this with a VFCache card, since you can have only one VFCache card in each server, the data is unprotected, and only for ephemeral data like transaction logs or other temporary data. With multiple Fusion-IO cards in an IBM server, you can configure a RAID rank across the SSD, and use it for persistent storage like DB2 databases.
Here then is my side-by-side comparison:
IBM XIV Gen3 SSD Caching
Selected x86-based models of Cisco UCS, Dell PowerEdge, HP ProLiant DL, and IBM xSeries and System x servers
All of these, plus any other blade or rack-optimized server currently supported by XIV Gen3, including Oracle SPARC, HP Titanium, IBM POWER systems, and even IBM System z mainframes running Linux
Operating System support
Linux RHEL 5.6 and 5.7, VMware vSphere 4.1 and 5.0, and Windows 2008 x64 and R2.
All of these, plus all the other operating systems supported by XIV Gen3, including AIX, IBM i, Solaris, HP-UX, and Mac OS X
FCP and iSCSI
Vendor-supplied driver required on the server
Yes, the VFCache driver must be installed to use this feature.
No, IBM XIV Gen3 uses native OS-based multi-pathing drivers.
External disk storage systems required
None, it appears the VFCache has no direct interaction with the back-end disk array, so in theory the benefits are the same whether you use this VFCache card in front of EMC storage or IBM storage
XIV Gen3 is required, as the SSD slots are not available on older models of IBM XIV.
Shared disk support
No, VFCache has to be disabled and removed for vMotion to take place.
Yes! XIV Gen3 SSD caching shared disk supports VMware vMotion and Live Partition Mobility.
Support for multiple servers
An advantage of the XIV Gen3 SSD caching approach is that the cache can be dynamically allocated to the busiest data from any server or servers.
Support for active/active server clusters
Aware of changes made to back-end disk
No, it appears the VFCache has no direct interaction with the back-end disk array, so any changes to the data on the box itself are not communicated back to the VFCache card itself to invalidate the cache contents.
None identified. However, VFCache only caches blocks 64KB or smaller, so any sequential processing with larger blocks will bypass the VFCache.
Yes! XIV algorithms detect sequential access and avoid polluting the SSD with these blocks of data.
Number of SSD supported
One, which seems odd as IBM supports multiple Fusion-IO cards for its servers. However, this is not really a single point of failure (SPOF) as an application experiencing a VFCache failure merely drops down to external disk array speed, no data is lost since it is only read cache.
6 to 15 (one per XIV module) for high availability.
Pin data in SSD cache
Yes, using split-card mode, you can designate a portion of the 300GB to serve as Direct-attached storage (DAS). All data written to the DAS portion will be kept in SSD. However, since only one card is supported per server and the data is unprotected, this should only be used for ephemeral data like logs and temp files.
No, there is no option to designate an XIV Gen3 volume to be SSD-only. Consider using Fusion-IO PCIe card as a DAS alternative, or another IBM storage system for that requirement.
Pre-sales Estimating tools
Yes! CDF and Disk Magic tools are available to help cost-justify the purchase of SSD based on workload performance analysis.
IBM has the advantage that it designs and manufactures both servers and storage, and can design optimal solutions for our clients in that regard.
A client asked me to explain "Nearline storage" to them. This was easy, I thought, as I started my IBM career on DFHSM, now known as DFSMShsm for z/OS, which was created in 1977 to support the IBM 3850 Mass Storage System (MSS), a virtual storage system that blended disk drives and tape cartridges with robotic automation. Here is a quick recap:
Online storage is immediately available for I/O. This includes DRAM memory, solid-state drives (SSD), and always-on spinning disk, regardless of rotational speed.
Nearline storage is not immediately available, but can be made online quickly without human intervention. This includes optical jukeboxes, automated tape libraries, as well as spin-down massive array of idle disk (MAID) technologies.
Offline storage is not immediately available, and requires some human intervention to bring online. This can include USB memory sticks, CD/DVD optical media, shelf-resident tape cartridges, or other removable media.
Sadly, it appears a few storage manufacturers and vendors have been misusing the term "Nearline" to refer to "slower online" spinning disk drives. I find this [June 2005 technology paper from Seagate], and this [2002 NetApp Press Release], the latter of which included this contradiction for their "NearStore" disk array. Here is the excerpt:
"Providing online access to reference information—NetApp nearline storage solutions quickly retrieve and replicate reference and archive information maintained on cost-effective storage—medical images, financial models, energy exploration charts and graphs, and other data-intensive records can be stored economically and accessed in multiple locations more quickly than ever"
Which is it, "online access" or "nearline storage"?
If a client asked why slower drives consume less energy or generate less heat, I could explain that, but if they ask why slower drives must have SATA connections, that is a different discussion. The speed of a drive and its connection technology are for the most part independent. A 10K RPM drive can be made with FC, SAS or SATA connection.
I am opposed to using "Nearlne" just to distinguish between four-digit speeds (such as 5400 or 7200 RPM) versus "online" for five-digit speeds (10,000 and 15,000 RPM). The difference in performance between 10K RPM and 7200 RPM spinning disks is miniscule compared to the differences between solid-state drives and any spinning disk, or the difference between spinning disk and tape.
I am also opposed to using the term "Nearline" for online storage systems just because they are targeted for the typical use cases like backup, archive or other reference information that were previously directed to nearline devices like automated tape libraries.
Can we all just agree to refer to drives as "fast" or "slow", or give them RPM rotational speed designations, rather than try to incorrectly imply that FC and SAS drives are always fast, and SATA drives are always slow? Certainly we don't need new terms like "NL-SAS" just to represent a slower SAS connected drive.
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!
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.
A reader from New Zealand expressed concern some corporate bloggers were [using the earthquake for marketing]. He lost someone close to him in Christchurch, and is unable to reach a friend living in Japan, so I am sorry for his loss. I plan to be in Australia and New Zealand to teach a Top Gun class May 15-27, so hopefully I will be able to meet him in person when I am down there.
"Earmarking funds is a really good way of hobbling relief organizations and ensuring that they have to leave large piles of money unspent in one place while facing urgent needs in other places. ... Meanwhile, the smaller and less visible emergencies where NGOs can do the most good are left unfunded.
In the specific case of Japan, there's all the more reason not to donate money. Japan is a wealthy country which is responding to the disaster, among other things, by printing hundreds of billions of dollars' worth of new money."
Another reader mentioned that the last surviving American WW-II vet died the same week. WTF? IBM and Japan have been allies for quite a while now, and there is no reason to bring up past wars except to compare the scope and magnitude of the cleanup effort. (Update: Frank Buckles was the last surviving WW-I vet, but also served in WW-II).
Many readers felt that charity begins at home, and there are plenty of worthy causes right here in the USA to donate to instead. Inspired by last year's movie [Waiting for Superman], my girlfriend started a project called [Centers for My Super Stars] for her first grade class on DonorsChoose.org. For those not familiar with this website, DonorsChoose.org uses the cloud to connect school teachers in need of supplies with rich people to donate funds towards these projects. If you want to contribute to her project, [donate here].
"And speaking of class, there just happens to be a baseball team in Sendai, Japan. The Golden Eagles. Their stadium was severely damaged from the earthquake. Wouldn't you think some of them lug nuts who run American baseball would bring the Golden Eagles and their opponents over to the United States when the Japanese season starts -- play some games over here and raise money to help the Japanese? Wouldn't you think they could just once stop that national pastime stuff and help the international pastime?"
As you can see, different readers have different opinions on this. We are all on this world together, and both our economy and our ecology are more interconnected than you might think. Let's build a smarter planet.
Here I am, day 11 of a 17-day business trip, on my last leg of the trip this week, in Kuala Lumpur in Malaysia. I have been flooded with requests to give my take on EMC's latest re-interpretation of storage virtualization, VPLEX.
I'll leave it to my fellow IBM master inventor Barry Whyte to cover the detailed technical side-by-side comparison. Instead, I will focus on the business side of things, using Simon Sinek's Why-How-What sequence. Here is a [TED video] from Garr Reynold's post
[The importance of starting from Why].
Let's start with the problem we are trying to solve.
Problem: migration from old gear to new gear, old technology to new technology, from one vendor to another vendor, is disruptive, time-consuming and painful.
Given that IT storage is typically replaced every 3-5 years, then pretty much every company with an internal IT department has this problem, the exception being those companies that don't last that long, and those that use public cloud solutions. IT storage can be expensive, so companies would like their new purchases to be fully utilized on day 1, and be completely empty on day 1500 when the lease expires. I have spoken to clients who have spent 6-9 months planning for the replacement or removal of a storage array.
A solution to make the data migration non-disruptive would benefit the clients (make it easier for their IT staff to keep their data center modern and current) as well as the vendors (reduce the obstacle of selling and deploying new features and functions). Storage virtualization can be employed to help solve this problem. I define virtualization as "technology that makes one set of resources look and feel like a different set of resources, preferably with more desirable characteristics.". By making different storage resources, old and new, look and feel like a single type of resource, migration can be performed without disrupting applications.
Before VPLEX, here is a breakdown of each solution:
Non-disruptive tech refresh, and a unified platform to provide management and functionality across heterogeneous storage.
Non-disruptive tech refresh, and a unified platform to provide management and functionality between internal tier-1 HDS storage, and external tier-2 heterogeneous storage.
Non-disruptive tech refresh, with unified multi-pathing driver that allows host attachment of heterogeneous storage.
New in-band storage virtualization device
Add in-band storage virtualization to existing storage array
New out-of-band storage virtualization device with new "smart" SAN switches
SAN Volume Controller
HDS USP-V and USP-VM
For IBM, the motivation was clear: Protect customers existing investment in older storage arrays and introduce new IBM storage with a solution that allows both to be managed with a single set of interfaces and provide a common set of functionality, improving capacity utilization and availability. IBM SAN Volume Controller eliminated vendor lock-in, providing clients choice in multi-pathing driver, and allowing any-to-any migration and copy services. For example, IBM SVC can be used to help migrate data from an old HDS USP-V to a new HDS USP-V.
With EMC, however, the motivation appeared to protect software revenues from their PowerPath multi-pathing driver, TimeFinder and SRDF copy services. Back in 2005, when EMC Invista was first announced, these three software represented 60 percent of EMC's bottom-line profit. (Ok, I made that last part up, but you get my point! EMC charges a lot for these.)
Back in 2006, fellow blogger Chuck Hollis (EMC) suggested that SVC was just a [bump in the wire] which could not possibly improve performance of existing disk arrays. IBM showed clients that putting cache(SVC) in front of other cache(back end devices) does indeed improve performance, in the same way that multi-core processors successfully use L1/L2/L3 cache. Now, EMC is claiming their cache-based VPLEX improves performance of back-end disk. My how EMC's story has changed!
So now, EMC announces VPLEX, which sports a blend of SVC-like and Invista-like characteristics. Based on blogs, tweets and publicly available materials I found on EMC's website, I have been able to determine the following comparison table. (Of course, VPLEX is not yet generally available, so what is eventually delivered may differ.)
Scalable, 1 to 4 node-pairs
One size fits all, single pair of CPCs
SVC-like, 1 to 4 director-pairs
Works with any SAN switches or directors
Required special "smart" switches (vendor lock-in)
SVC-like, works with any SAN switches or directors
Broad selection of IBM Subsystem Device Driver (SDD) offered at no additional charge, as well as OS-native drivers Windows MPIO, AIX MPIO, Solaris MPxIO, HP-UX PV-Links, VMware MPP, Linux DM-MP, and comercial third-party driver Symantec DMP.
Limited selection, with focus on priced PowerPath driver
Invista-like, PowerPath and Windows MPIO
Read cache, and choice of fast-write or write-through cache, offering the ability to improve performance.
No cache, Split-Path architecture cracked open Fibre Channel packets in flight, delayed every IO by 20 nanoseconds, and redirected modified packets to the appropriate physical device.
SVC-like, Read and write-through cache, offering the ability to improve performance.
Space-Efficient Point-in-Time copies
SVC FlashCopy supports up to 256 space-efficient targets, copies of copies, read-only or writeable, and incremental persistent pairs.
Like Invista, No
Remote distance mirror
Choice of SVC Metro Mirror (synchronous up to 300km) and Global Mirror (asynchronous), or use the functionality of the back-end storage arrays
No native support, use functionality of back-end storage arrays, or purchase separate product called EMC RecoverPoint to cover this lack of functionality
Limited synchronous remote-distance mirror within VPLEX (up to 100km only), no native asynchronous support, use functionality of back-end storage arrays
Provides thin provisioning to devices that don't offer this natively
Like Invista, No
SVC Split-Cluster allows concurrent read/write access of data to be accessed from hosts at two different locations several miles apart
I don't think so
PLEX-Metro, similar in concept but implemented differently
Non-disruptive tech refresh
Can upgrade or replace storage arrays, SAN switches, and even the SVC nodes software AND hardware themselves, non-disruptively
Tech refresh for storage arrays, but not for Invista CPCs
Tech refresh of back end devices, and upgrade of VPLEX software, non-disruptively. Not clear if VPLEX engines themselves can be upgraded non-disruptively like the SVC.
Heterogeneous Storage Support
Broad support of over 140 different storage models from all major vendors, including all CLARiiON, Symmetrix and VMAX from EMC, and storage from many smaller startups you may not have heard of
Invista-like. VPLEX claims to support a variety of arrays from a variety of vendors, but as far as I can find, only DS8000 supported from the list of IBM devices. Fellow blogger Barry Burke (EMC) suggests [putting SVC between VPLEX and third party storage devices] to get the heterogeneous coverage most companies demand.
Back-end storage requirement
Must define quorum disks on any IBM or non-IBM back end storage array. SVC can run entirely on non-IBM storage arrays
HP SVSP-like, requires at least one EMC storage array to hold metadata
SVC 2145-CF8 model supports up to four solid-state drives (SSD) per node that can treated as managed disk to store end-user data
Invista-like. VPLEX has an internal 30GB SSD, but this is used only for operating system and logs, not for end-user data.
In-band virtualization solutions from IBM and HDS dominate the market. Being able to migrate data from old devices to new ones non-disruptively turned out to be only the [tip of the iceberg] of benefits from storage virtualization. In today's highly virtualized server environment, being able to non-disruptively migrate data comes in handy all the time. SVC is one of the best storage solutions for VMware, Hyper-V, XEN and PowerVM environments. EMC watched and learned in the shadows, taking notes of what people like about the SVC, and decided to follow IBM's time-tested leadership to provide a similar offering.
EMC re-invented the wheel, and it is round. On a scale from Invista (zero) to SVC (ten), I give EMC's new VPLEX a six.
From New York, Rolf went to London, Paris, Madrid, Morocco, Cairo, South Africa, Bangkok Thailand, Malaysia, Singapore, New Zealand, Australia, and then back to United States. I was hoping to run into him while I was in Australia and New Zealand last month, but our schedules did not line up.
Travelingwithout baggage is more than just a convenience, it is a metaphor for the philosophy that we should keep only what we need, and leave behind what we don't. This was the approach taken by IBM in the design of the IBM Storwize V7000 midrange disk system.
The IBM Storwize V7000 disk system consists of 2U enclosures. Controller enclosures have dual-controllers and drives. Expansion enclosures have just drives. Enclosures can have either 24 smaller form factor (SFF) 2.5-inch drives, or twelve larger 3.5-inch drives. A controller enclosure can be connected up to nine expansion enclosures.
The drives are all connected via 6 Gbps SAS, and come in a variety of speeds and sizes: 300GB Solid-State Drive (SSD); 300GB/450GB/600GB high-speed 10K RPM; and 2TB low-speed 7200 RPM drives. The 12-bay enclosures can be intermixed with 24-bay enclosures on the same system, and within an enclosure different speeds and sizes can be intermixed. A half-rack system (20U) could hold as much as 480TB of raw disk capacity.
This new system, freshly designed entirely within IBM, competes directly against systems that carry a lot of baggage, including the HDS AMS, HP EVA, an EMC CLARiiON CX4 systems. Instead, we decided to keep the what we wanted from our other successful IBM products.
Inspired by our successful XIV storage system, IBM has developed a web-based GUI that focuses on ease-of-use. This GUI uses the latest HTML5 and dojo widgets to provide an incredible user experience.
Borrowed from our IBM DS8000 high-end disk systems, state-of-the-art device adapters provide 6 Gbps SAS connectivity with a variety of RAID levels: 0, 1, 5, 6, and 10.
From our SAN Volume Controller, the embedded [ SVC 6.1 firmware] provides all of the features and functions normally associated with enterprise-class systems, including Easy Tier sub-LUN automated tiering between Solid-State Drives and Spinning disk, thin provisioning, external disk virtualization, point-in-time FlashCopy, disk mirroring, built-in migration capability, and long-distance synchronous and asynchronous replication.
Finally, the various "internal NDA" that kept me from publishing this sooner have expired, so now I have the long-awaited [Inside System Storage: Volume II], documenting IBM's transformation in its storage strategy, including behind-the-scenes commentary about IBM's acquisitions of XIV and Diligent. Available initially in paperback form. I am still working on the hard cover and eBook editions.
For those who have not yet read my first book, Inside System Storage: Volume I, it is still available from my publisher Lulu, in [hard cover], [paperback] and [eBook] editions.
IBM System Storage DS8800
A lesson IBM learned long ago was not to make radical changes to high-end disk systems, as clients who run mission-critical applications are more concerned about reliability, availability and serviceability than they are performance or functionality. Shipping any product before it was ready meant painfully having to fix the problems in the field instead.
(EMC apparently is learning this same lesson now with their VMAX disk system. Their Engenuity code from Symmetrix DMX4 was ported over to new CLARiiON-based hardware. With several hundred boxes in the field, they have already racked up over 150 severity 1 problems, roughly half of these resulted in data loss or unavailability issues. For the sake of our mutual clients that have both IBM servers and EMC disk, I hope they get their act together soon.)
To avoid this, IBM made incremental changes to the successful design and architecture of its predecessors. The new DS8800 shares 85 percent of the stable microcode from the DS8700 system. Functions like Metro Mirror, Global Mirror, and Metro/Global Mirror, are compatible with all of the previous models of the DS8000 series, as well as previous models of the IBM Enterprise Storage Server (ESS) line.
The previous models of DS8000 series were designed to take in cold air from both front and back, and route the hot air out the top, known as chimney design. However, many companies are re-arranging their data centers into separate cold aisles and hot aisles. The new DS8800 has front-to-back cooling to help accommodate this design.
My colleague Curtis Neal would call the rest of this a "BFD" announcement, which of course stands for "Bigger, Faster and Denser". The new DS8800 scales-up to more drives than its DS8700 predecessor, and can scale-out from a single-frame 2-way system to a multi-frame 4-way system. IBM has upgraded to faster 5GHz POWER6+ processors, with dual-core 8 Gbps FC and FICON host adapters, 8 Gbps device adapters, and 6 Gbps SAS connectivity to smaller form factor (SFF) 2.5-inch SAS drives. IBM Easy Tier will provide sub-LUN automated tiering between Solid-State Drives and spinning disk. The denser packaging with SFF drives means that we can pack over 1000 drives in only three frames, compared to five frames required for the DS8700.
The [IBM System Storage SAN Volume Controller] software release v6.1 brings Easy Tier sub-LUN automated tiering to the rest of the world. IBM Easy Tier moves the hottest, most active extents up to Solid-State Drives (SSD) and moves the coldest, least active down to spinning disk. This works whether the SSD is inside the SVC 2145-CF8 nodes, or in the managed disk pool.
Tired of waiting for EMC to finally deliver FAST v2 for your VMAX? It has been 18 months since they first announced that someday they would have sub-LUN automatic tiering. What is taking them so long? Why not virtualize your VMAX with SVC, and you can have it sooner!
SVC 6.1 also upgrades to a sexy new web-based GUI, which like the one for the IBM Storwize V7000, is based on the latest HTML5 and dojo widget standards. Inspired by the popular GUI from the IBM XIV Storage System, this GUI has greatly improved ease-of-use.
"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.