Continuing my coverage of the [IBM Storage Innovation Executive Summit], that occurred May 9 in New York City, this is my fifth in a series of blog posts on this event.
- Smart Archiving
Doug Balog, IBM VP and Business Level Executive for Storage, presented Smart Archiving. Citing research by Jon Toigo, Doug indicated that 40 percent of data on disk should be archived. Sadly, a vast majority of companies continue to use their backups as archives. There is a better way to do archives, to address the needs of four use cases:
The IBM Information Archive for email, files and eDiscovery offers full text indexing. A well-deployed archive strategy can save up to 60 percent in backup costs, and reduce backup times by 80 percent. IBM offers advanced analytics and visualization for archive data.
An analysis of a global insurance company found that they kept, on average, 120 copies of every email sent. This was the combination of an average of 12 copies of the email, multipled by 10 backups of the email repository.
Banjercito, a bank in Mexico, has a 10-year retention requirement from government regulations.
The new LTFS Library Edition allows Library-based access to files stored on tape cartridges. The new TS3500 Library Connector means that a single system of connected tape libraries can hold up to 2.7 Exabytes (EB) of data.
- Archive Industry Perspectives
Steve Duplessie from Enterprise Strategy Group [ESG] gave his views on the challenges of volume, access and cost. His definition for archive: the long term retention of information on a separate environment for compliance, eDiscovery and business reference purposes. Steve advocates a purpose-built solutiion for archive. There are three major challenges for implementing an archive solution:
- Getting Participation -- Steve feels that key stakeholders have inappropriate expectations of what archive is, or can be.
- Define Tasks -- Steve argues that archive is very much a process-oriented approach, and tasks must fit business process and procedures
- Prepare for Future Content Types -- the frequent change of standard and proprietary data types poses a real challenge for long term retention of data
For example, the Financial Industry Regulatory Authority [FINRA] oversee 4,000 brokerage firms, and 600,000 broker/dealers. They have mandated the storing of digital data related to stock trades, and this can include text messages, voice messages, and emails. They continue to expand this definition, so soon this could include tweets on Twitter, for example.
Steve feels there are four key requirements for archive:
- Support for email, such as an email application plug-in
- Off-line access to archived data
- Support for mobile devices, such as smartphones
- Basic search capabilities
Companies are starting to take archive seriously. About 35 percent of firms surveyed have adopted archive, and another 36 percent plan to in the next 12-24 months. Enterprise archive has grown over 200 percent from 2007 to 2009. Steve agrees that not everything needs to be stored on disk. Retention periods greater than six years dictates the need for tape.
Current systems may not meet today's requirements. Data loss and downtime costs have skyrocketed. Data Protection and Retention projects can represent a gold mine of savings, new capabilities can greatly lower costs, allowing companies to shift resources over to revenue generation.
- Big Data, New Physics and Geospatial Super-Food
I would vote this the best session of the day! For all those confused on what the heck "Big Data" means, Jeff has the best explanation. Jeff Jonas is an IBM Distinguished Engineer and the Chief Scientist of Entity Analytics. He had just finished his 17th marathon on Saturday, and his fingers were bandaged.
Jeff had founded the Systems Research and Design (SR&D) company, known for creating NORA (non-obvious relationship awareness) used by Las Vegas casinos to identify fraud. SR&D was acquired by IBM back in 2005. Jeff is focused on sensemaking of streams. He feels many companies are suffering from "Enterprise Amnesia".
"The data must find the data .. and the relevance must find the user."
-- Jeff Jonas
Jeff's metaphor to Big Data is a jigsaw puzzle without the picture on the outside of the box. To demonstrate his point, he presented a pile of jigsaw puzzle pieces and asked four teenagers to put the puzzle together without the advantage of the picture on the box. What he had not told them was that he mixed four different puzzles together, removing out 10 to 20 percent of the pieces from each puzzle. He also added some duplicate pieces from a second identical puzzle, and just to make things fun, included a dozen pieces from a sixth puzzle just to mess with their heads. Within a few hours, the kids had managed to figure out that there were four puzzles, that there were duplicate pieces, and that there were some pieces that did not fit any of the four puzzles.
"You can't squeeze knowledge from a pixel."
-- Jeff Jonas
This approach favors false negatives. New observations reverse out old conceptions. As the picture emerges, this provides added focus on new information. More data can provide better predictions. "Bad" data, including misspelled words and mis-coded categories, was often discarded or corrected on the basis of "Garbage-In, Garbage Out", but can now be useful in a Big Data perspective.
Take for example the 600 billion recordings of the "location data" captured on cell phones every day. With regular triangulation of cell phone towers, the information can pinpoint you within 60 meters, add GPS and this improved to within 20 meters, and add Wi-Fi is further improved to 10 meters. While this data is "de-identified" so as not to identify individual users, the process of re-identification is relatively trivial. Jeff's system is able to predict a person will be next Thursday at 5:35pm with 87 percent accuracy.
Thus, Big Data represents an asset, accumulation of context. Real-time analytics can be a competitive advantage. These streams of data will need persistent storage and massive I/O capabilities. In one example, Jeff processed 4,200 separate sources of information and was able to identify "dead votes". These are votes cast by people that died in years prior, indicating voter fraud.
Jeff's latest project, codenamed G2, will tackle not just people, but everything from proteins to asteroids.
Normally, the worst time slot is the hour after lunch, but these presentations kept people's attention.
technorati tags: IBM, Summit, NYC, Doug Balog, Smart Archive, Information Archive, Banjercito, Steve Duplessie, ESG, , FINRA, Big Data, Jeff Jonas, NORA, SRD, Enterprise Amnesia, cell phone, location data
Wrapping up my week's coverage of the IBM Pulse 2011 conference, I have had several people ask me to explain IBM's latest initiative, Smarter Computing, which IBM launched this week at this conference. Having led the IT industry through the Centralized Computing era and the Distributed Computing era, IBM is now well-positioned to help companies, governments and non-profit organizations to enter the new Smarter Computing era, focused on insight and discovery.
|Centralized Computing||Distributed Computing||Smarter Computing|
- Thousands of IT professionals
- Mainframe servers
- Effiicent, but only the largest companies and governments had them
- Millions of office workers
- Personal computers (PC)
- Innovative, extending the reach to small and medium-sized businesses, but resulted in server sprawl and increased TCO
- Billions of people
- Smart phones and other handheld devices
- Efficient and Innovative, combining the best of centralized and distributed computing
|1952 to 1980||1981 to 2010||2011 and beyond|
To help clients with this transition, IBM's Smarter Computing initiative has three main components. This is a corporate-wide strategy, with systems, software and services all working together to realize results.
- Big Data
The first component is Big Data. This combines three different sources of data:
- Traditional structured data in OLTP databases and OLAP data warehouses, using data management solutions like DB2 and IBM Netezza.
- Unstructured data, including text documents, images, audio, and video, processed with massive parallelism using IBM BigInsights and Apache Hadoop.
- Real-Time Analytics Processing (RTAP) of incoming data, including video surveillance, social media, RFID chips, smart meters, and traffic control systems, processed with IBM InfoSphere Streams
Of course, Big Data will bring new opportunities on the storage front, which I will save for a future post!
- Optimized Systems
Rather than general purpose IT equipment, we have now the scale and scope to specialize with systems optimized for particular workloads, the second component of the Smarter Computing initiative. Of course, IBM has been delivering integrated stacks of systems, software and services for decades now, but it is important to remind people of this, as IBM now has a spate of competitors all trying to follow IBM's lead in this arena.
As with Big Data, the focus on Optimized Systems has impacted IBM's strategy on storage as well. I'll save that discussion for a future post as well!
I am glad that nearly all of the storage vendors have standardized to a common definition for Cloud, the third component of Smarter Computing, which shows that this concept has matured:
Cloud computing is a pay-per-use model for enabling network access to a pool of computing resources that can be provisioned and released rapidly with minimal management effort or service provider interaction.
-- U.S. National Institute of Standards and Technology [nist.gov]
Of course, Cloud is just an evolution of IBM's Service Bureau business of the 1960s and 1970s, renting out time-sharing on mainframe systems, Grid Computing of the 1980s, and Application Service Providers that popped up in the 1990s. While the [butchers, bakers and candlestick makers] that IBM competes against might focus their efforts on just private cloud or just public cloud, IBM recognizes the reality is that different clients will need different solutions. Rather than rip-and-replace, IBM will help clients transition to cloud via inclusive solutions that adopt a hybrid approach:
- Traditional enterprise with private cloud deployments, using solutions like IBM CloudBurst, SONAS and Information Archive
- Traditional enterprise with public cloud services to handle seasonable peaks, providing offsite resiliency, and solutions for a mobile workforce
- Hybrid clouds that blend private and public cloud services, to handle seasonal peak workloads, remote and branch offices
IBM's emphasis on IT Infrastructure Library (ITIL), Tivoli and Maximo products will play well in this space to provide integrated service management across traditional and cloud deployments. This is why IBM decided to launch Smarter Computing initiative at Pulse 2011 conference, the industry's premiere conference on intergrated service management.
The IBM Watson that competed on Jeopardy! is an excellent example of all three components of Smarter Computing at work.
- IBM Watson was able to respond to Jeopardy! clues within three seconds, processing a combination of database searches with DB2 and text-mining analytics of unstructured data with IBM BigInsights.
- IBM Watson combined servers, software and storage into an integrated supercomputer that was optimized for one particular workload: playing Jeopardy!
- IBM Watson used many technologies prevalent in private and public cloud computing systems, storing its data on a modified version of SONAS for storage, using xCat administration tools, networking across 10GbE Ethernet, and massive parallel processing through lots of PowerVM guest images.
technorati tags: IBM, Pulse, ibmpulse, Centralized Computing, Distributed Computing, Smarter Computing, Big Data, Optimized Systems, Cloud Computing, SONAS, Netezza, DB2, InfoSphere, BigInsights, SPSS, Data Warehouse, Structured Data, Unstructured Data, Watson, CloudBurst, Information Archive
It's Tuesday again, and that means one thing.... IBM Announcements! On the heels of [last week's announcements], IBM announced some additional products of interest to storage administrators.
- IBM Information Archive
Back in 2008, IBM [unveiled the Information Archive]. This storage solution provides automated policy-based tiering between disk and tape, with non-erasable non-rewriteable enforcement to protect against unethical tampering of data. The initial release supported [both files and object storage], with support for different collections, each with its own set of policies for management. However, it only supported NFS initially for the file protocol. Today, IBM announces the addition of CIFS protocol support, which will be especially helpful in healthcare and life sciences, as much of the medical equipment is designed for CIFS protocol storage.
Also, Information Archive will now provide a full index and search feature capability to help with e-Discovery. Searches and retrievals can be done in the background without disrupting applications or the archiving operations.
To learn more, read the [announcement letter].
- IBM Tivoli Storage Manager
IBM Tivoli Storage Manager for Virtual Environments V6.2 extends capabilities that currently exist in IBM Tivoli Storage Manager. TSM backup/archive clients run fine on guest operating systems, but now this new extension improves backup for VMware environments. TSM provides incremental block-level backups utilizing VMware's vStorage APIs for Data Protection and Changed Block Tracking features.
To minimize impact to the VMware host, TSM for VE make use of non-disruptive snapshots and offload the backup processing to a vStorage backup server. This supports file-level recovery, volume-level recovery, and full VM recovery. Of course, since it is based on TSM v6, you get advanced storage efficiency features such as compression and deduplication to minimize consumption of disk storage pools.
To learn more, see the [announcement letter].
- IBM Tivoli Monitoring for Virtual Servers V6.2.3
IBM Tivoli Monitor has been extended to support virtual servers, including VMware, Linux KVM, and Citrix XenServer. This can help with capacity planning, performance monitoring, and availability. Tivoli Monitor will help you understand the relationships between physical and virtual resources to help isolate problems to the correct resource, reducing the time it takes for debug issues between servers and storage. See the
Next week is [IBM Pulse2011 Conference] in Las Vegas, February 27 to March 2. Sorry, I don't plan to be there this year. It is looking to be a great conference, with fellow inventor Dean Kamen as the keynote speaker. For a blast from the past, read my blog posts from Pulse2008 [Main Tent sessions] and [Breakout sessions].
technorati tags: IBM, #ibmpulse, Information Archive, Tivoli, TSM, Tivoli Monitor, VMware, LInux, KVM, Citrix, XenServer
Wrapping up my week's theme of storage optimization, I thought I would help clarify the confusion between data reduction and storage efficiency. I have seen many articles and blog posts that either use these two terms interchangeably, as if they were synonyms for each other, or as if one is merely a subset of the other.
- Data Reduction is LOSSY
By "Lossy", I mean that reducing data is an irreversible process. Details are lost, but insight is gained. In his paper, [Data Reduction Techniques", Rajana Agarwal defines this simply:
"Data reduction techniques are applied where the goal is to aggregate or amalgamate the information contained in large data sets into manageable (smaller) information nuggets."
Data reduction has been around since the 18th century.
Take for example this histogram from [SearchSoftwareQuality.com]. We have reduced ninety individual student scores, and reduced them down to just five numbers, the counts in each range. This can provide for easier comprehension and comparison with other distributions.
The process is lossy. I cannot determine or re-create an individual student's score from these five histogram values.
This next example, complements of [Michael Hardy], represents another form of data reduction known as ["linear regression analysis"]. The idea is to take a large set of data points between two variables, the x axis along the horizontal and the y axis along the vertical, and find the best line that fits. Thus the data is reduced from many points to just two, slope(a) and intercept(b), resulting in an equation of y=ax+b.
The process is lossy. I cannot determine or re-create any original data point from this slope and intercept equation.
In this last example, from [Yahoo Finance], reduces millions of stock trades to a single point per day, typically closing price, to show the overall growth trend over the course of the past year.
The process is lossy. Even if I knew the low, high and closing price of a particular stock on a particular day, I would not be able to determine or re-create the actual price paid for individual trades that occurred.
- Storage Efficiency is LOSSLESS
By contrast, there are many IT methods that can be used to store data in ways that are more efficient, without losing any of the fine detail. Here are some examples:
- Thin Provisioning: Instead of storing 30GB of data on 100GB of disk capacity, you store it on 30GB of capacity. All of the data is still there, just none of the wasteful empty space.
- Space-efficient Copy: Instead of copying every block of data from source to destination, you copy over only those blocks that have changed since the copy began. The blocks not copied are still available on the source volume, so there is no need to duplicate this data.
- Archiving and Space Management: Data can be moved out of production databases and stored elsewhere on disk or tape. Enough XML metadata is carried along so that there is no loss in the fine detail of what each row and column represent.
- Data Deduplication: The idea is simple. Find large chunks of data that contain the same exact information as an existing chunk already stored, and merely set a pointer to avoid storing the duplicate copy. This can be done in-line as data is written, or as a post-process task when things are otherwise slow and idle.
When data deduplication first came out, some lawyers were concerned that this was a "lossy" approach, that somehow documents were coming back without some of their original contents. How else can you explain storing 25PB of data on only 1PB of disk?
(In some countries, companies must retain data in their original file formats, as there is concern that converting business documents to PDF or HTML would lose some critical "metadata" information such as modificatoin dates, authorship information, underlying formulae, and so on.)
Well, the concern applies only to those data deduplication methods that calculate a hash code or fingerprint, such as EMC Centera or EMC Data Domain. If the hash code of new incoming data matches the hash code of existing data, then the new data is discarded and assumed to be identical. This is rare, and I have only read of a few occurrences of unique data being discarded in the past five years. To ensure full integrity, IBM ProtecTIER data deduplication solution and IBM N series disk systems chose instead to do full byte-for-byte comparisons.
- Compression: There are both lossy and lossless compression techniques. The lossless Lempel-Ziv algorithm is the basis for LTO-DC algorithm used in IBM's Linear Tape Open [LTO] tape drives, the Streaming Lossless Data Compression (SLDC) algorithm used in IBM's [Enterprise-class TS1130] tape drives, and the Adaptive Lossless Data Compression (ALDC) used by the IBM Information Archive for its disk pool collections.
Last month, IBM announced that it was [acquiring Storwize. It's Random Access Compression Engine (RACE) is also a lossless compression algorithm based on Lempel-Ziv. As servers write files, Storwize compresses those files and passes them on to the destination NAS device. When files are read back, Storwize retrieves and decompresses the data back to its original form.
To read independent views on IBM's acquisition, read Lauren Whitehouse (ESG) post [Remote Another Chair, Chris Mellor (The Register) article [Storwize Swallowed], or Dave Raffo (SearchStorage.com) article [IBM buys primary data compression].
As with tape, the savings from compression can vary, typically from 20 to 80 percent. In other words, 10TB of primary data could take up from 2TB to 8TB of physical space. To estimate what savings you might achieve for your mix of data types, try out the free [Storwize Predictive Modeling Tool].
So why am I making a distinction on terminology here?
Data reduction is already a well-known concept among specific industries, like High-Performance Computing (HPC) and Business Analytics. IBM has the largest marketshare in supercomputers that do data reduction for all kinds of use cases, for scientific research, weather prediction, financial projections, and decision support systems. IBM has also recently acquired a lot of companies related to Business Analytics, such as Cognos, SPSS, CoreMetrics and Unica Corp. These use data reduction on large amounts of business and marketing data to help drive new sources of revenues, provide insight for new products and services, create more focused advertising campaigns, and help understand the marketplace better.
There are certainly enough methods of reducing the quantity of storage capacity consumed, like thin provisioning, data deduplication and compression, to warrant an "umbrella term" that refers to all of them generically. I would prefer we do not "overload" the existing phrase "data reduction" but rather come up with a new phrase, such as "storage efficiency" or "capacity optimization" to refer to this category of features.
IBM is certainly quite involved in both data reduction as well as storage efficiency. If any of my readers can suggest a better phrase, please comment below.
technorati tags: IBM, data reduction, storage efficiency, histogram, linear regression, thin provisioning, data deduplication, lossy, lossless, EMC, Centera, hash collisions, Information Archive, LTO, LTO-DC, SLDC, ALDC, compression, deduplication, Storwize, supercomputers, HPC, analytics
Continuing this week's discussion on IBM announcements, today I'll cover our integrated systems.
The problem with spreading out these announcements across several days' worth of blog posts is that others beat you to the punch. Fellow blogger Richard Swain (IBM) has his post [Move that File], and TechTarget's Dave Raffo has an article titled [
"IBM SONAS gains policy-driven tiering, gateway to IBM XIV Storage System"].
By combining multiple components into a single "integrated system", IBM can offer a blended disk-and-tape storage solutions. This provides the best of both worlds, high speed access using disk, while providing lower costs and more energy efficiency with tape. According to a study by the Clipper Group, tape can be 23 times less expensive than disk over a 5 year total cost of ownership (TCO).
The two we introduced recently were the [IBM Information Archive] and the Scale-Out Network Attached Storage (SONAS). This week, IBM announced some enhancements as SONAS v1.1.1 release. SONAS is the productized version of IBM's Scale-Out File Services (SoFS), which I discussed in my posts [Area Rugs versus Wall-to-Wall Carpeting] and [More details about IBM's Clustered Scalable NAS].
- ILM and HSM data movement
I have covered Information Lifecycle Management several times in this post, including my posts [ILM for my iPod], [Times a Million], and [Using ILM to Save Trees], to name a few.
I've also covered Hierarchical Storage Management, such as my post [Seven Tiers of Storage at ABN Amro], and my role as lead architect for DFSMS on z/OS in general, and DFSMShsm in particular.
However, some explanation might be warranted in the use of these two terms in regards to SONAS. In this case, ILM refers to policy-based file placement, movement and expiration on internal disk pools. This is actually a GPFS feature that has existed for some time, and was tested to work in this new configuration. Files can be individually placed on either SAS (15K RPM) or SATA (7200 RPM) drives. Policies can be written to move them from SAS to SATA based on size, age and days non-referenced.
HSM is also a form of ILM, in that it moves data from SONAS disk to external storage pools managed by IBM Tivoli Storage Manager. A small stub is left behind in the GPFS file system indicating the file has been "migrated". Any reference to read or update this file will cause the file to be "recalled" back from TSM to SONAS for processing. The external storage pools can be disk, tape or any other media supported by TSM. Some estimate that as much as 60 to 80 percent of files on NAS have low reference and should be stored on tape instead of disk, and now SONAS with HSM makes that possible.
This distinction allows the ILM movement to be done internally, within GPFS, and the HSM movement to be done externally, via TSM. Both ILM and HSM movement take advantage of the GPFS high-speed policy engine, which can process 10 million files per node, run in parallel across all interface nodes. Note that TSM is not required for ILM movement. In effect, SONAS brings the policy-based management features of DFSMS for z/OS mainframe to all the rest of the operating systems that access SONAS.
- HTTP and NIS support
In addition to NFS v2, NFS v3, and CIFS, the SONAS v1.1.1 adds the HTTP protocol. Over time, IBM plans to add more protocols in subsequent releases. Let me know which protocols you are interested in, so I can pass that along to the architects designing future releases!
SONAS v1.1.1 also adds support for Network Information Service (NIS), a client/server based model for user administration. In SONAS, NIS is used for netgroup and ID mapping only. Authentication is done via Active Directory, LDAP or Samba PDC.
- Asynchronous Replication
SONAS already had synchronous replication, which was limited in distance. Now, SONAS v1.1.1 provides asynchronous replication, using rsync, at the file level. This is done over Wide Area Network (WAN) across to any other SONAS at any distance.
- Hardware enhancements
Interface modules can now be configured with either 64GB or 128GB of cache. Storage now supports both 450GB and 600GB SAS (15K RPM) and both 1TB and 2TB SATA (7200 RPM) drives. However, at this time, an entire 60-drive drawer must be either all one type of SAS or all one type of SATA. I have been pushing the architects to allow each 10-pack RAID rank to be independently selectable. For now, a storage pod can have 240 drives, 60 drives of each type of disk, to provide four different tiers of storage. You can have up to 30 storage pods per SONAS, for a total of 7200 drives.
An alternative to internal drawers of disk is a new "Gateway" iRPQ that allows the two storage nodes of a SONAS storage pod to connect via Fibre Channel to one or two XIV disk systems. You cannot mix and match, a storage pod is either all internal disk, or all external XIV. A SONAS gateway combined with external XIV is referred to as a "Smart Business Storage Cloud" (SBSC), which can be configured off premises and managed by third-party personnel so your IT staff can focus on other things.
See the Announcement Letters for the SONAS [hardware] and [software] for more details.
For those who are wondering how this positions against IBM's other NAS solution, the IBM System Storage N series, the rule of thumb is simple. If your capacity needs can be satisfied with a single N series box per location, use that. If not, consider SONAS instead. For those with non-IBM NAS filers that realize now that SONAS is a better approach, IBM offers migration services.
Both the Information Archive and the SONAS can be accessed from z/OS or Linux on System z mainframe, from "IBM i", AIX and Linux on POWER systems, all x86-based operating systems that run on System x servers, as well as any non-IBM server that has a supported NAS client.
technorati tags: , IBM, Announcements, SONAS, SoFS, Information+Archive, Richard Swain, TechTarget, ILM, HSM, storage tiers, GPFS, TSM, HTTP, NIS, TSM, NAS, iRPQ, XIV, SBSC, z/OS, Linux, AIX