Tony Pearson is a Master Inventor and Senior IT Architect for the IBM Storage product line at the
IBM Systems Client Experience Center in Tucson Arizona, and featured contributor
to IBM's developerWorks. In 2016, Tony celebrates his 30th year anniversary with IBM Storage. He is
author of the Inside System Storage series of books. This blog is for the open exchange of ideas relating to storage and storage networking hardware, software and services.
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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.
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.
Well, I'm back safely from my tour of Asia. I am glad to report that Tokyo, Beijing and Kuala Lumpur are pretty much how I remember them from the last time I was there in each city. I have since been fighting jet lag by watching the last thirteen episodes of LOST season 6 and the series finale.
Recently, I have started seeing a lot of buzz on the term "Storage Federation". The concept is not new, but rather based on the work in database federation, first introduced in 1985 by [A federated architecture for information management] by Heimbigner and McLeod. For those not familiar with database federation, you can take several independent autonomous databases, and treat them as one big federated system. For example, this would allow you to issue a single query and get results across all the databases in the federated system. The advantage is that it is often easier to federate several disparate heterogeneous databases than to merge them into a single database. [IBM Infosphere Federation Server] is a market leader in this space, with the capability to federate DB2, Oracle and SQL Server databases.
Storage expansion: You want to increase the storage capacity of an existing storage system that cannot accommodate the total amount of capacity desired. Storage Federation allows you to add additional storage capacity by adding a whole new system.
Storage migration: You want to migrate from an aging storage system to a new one. Storage Federation allows the joining of the two systems and the evacuation from storage resources on the first onto the second and then the first system is removed.
Safe system upgrades: System upgrades can be problematic for a number of reasons. Storage Federation allows a system to be removed from the federation and be re-inserted again after the successful completion of the upgrade.
Load balancing: Similar to storage expansion, but on the performance axis, you might want to add additional storage systems to a Storage Federation in order to spread the workload across multiple systems.
Storage tiering: In a similar light, storage systems in a Storage Federation could have different capacity/performance ratios that you could use for tiering data. This is similar to the idea of dynamically re-striping data across the disk drives within a single storage system, such as with 3PAR's Dynamic Optimization software, but extends the concept to cross storage system boundaries.
To some extent, IBM SAN Volume Controller (SVC), XIV, Scale-Out NAS (SONAS), and Information Archive (IA) offer most, if not all, of these capabilities. EMC claims its VPLEX will be able to offer storage federation, but only with other VPLEX clusters, which brings up a good question. What about heterogenous storage federation? Before anyone accuses me of throwing stones at glass houses, let's take a look at each IBM solution:
IBM SAN Volume Controller
The IBM SAN Volume Controller has been doing storage federation since 2003. Not only can IBM SAN Volume Controller bring together storage from a variety of heterogenous storage, the SVC cluster itself can be a mix of different hardware models. You can have a 2145-8A4 node pair, 2145-8G4 node pair, and the new 2145-CF8 node pair, all combined together into a single SVC cluster. Upgrading SVC hardware nodes in an SVC cluster is always non-disruptive.
IBM XIV storage system
The IBM XIV has two kinds of independent modules. Data modules have processor, cache and 12 disks. Interface modules are data modules with additional processor, FC and Ethernet (iSCSI) adapters. Because these two modules play different roles in an XIV "colony", that number of each type is predetermined. Entry-level six-module systems have 2 interface and 4 data modules. Full 15-module systems have 6 interface and 9 data modules. Individual modules can be added or removed non-disruptively in an XIV.
IBM Scale-Out NAS
The SONAS is comprised of three kinds of nodes that work together in concert. A management node, one or more interface nodes, and two or more storage nodes. The storage nodes are paired to manage up to 240 nodes in a storage pod. Individual interface or data nodes can be added or removed non-disruptively in the SONAS. The underlying technology, the General Parallel File System, has been doing storage federation since 1996 for some of the largest top 500 supercomputers in the world.
IBM Information Archive (IA)
For the IA, there are 1, 2 or 3 nodes, which manages a set of collections. A collection can either be file-based using industry-standard NAS protocols, or object-based using the popular System Storage™ Archive Manager (SSAM) interface. Normally, you have as many collections as you have nodes, but nodes are powerful enough to manage two collections to provide N-1 availability. This allows a node to be removed, and a new node added into the IA "colony", in a non-disruptive manner.
Even in an ant colony, there are only a few types of ants, with typically one queen, several males, and lots of workers. But all the ants are red. You don't see colonies that mix between different species of ants. For databases, federation was a way to avoid the much harder task of merging databases from different platforms. For storage, I am surprised people have latched on to the term "federation", given our mixed results in the other "federations" we have formed, which I have conveniently (IMHO) ranked from least effective to most effective:
The Union of Soviet Socialist Republics (USSR)
My father used to say, "If the Soviet Union were in charge of the Sahara desert, they would run out of sand in 50 years." The [Soviet Union] actually lasted 68 years, from 1922 to 1991.
The United Nations (UN)
After the previous League of Nations failed, the UN was formed in 1945 to facilitate cooperation in international law, international security, economic development, social progress, human rights, and the achieving of world peace by stopping wars between countries, and to provide a platform for dialogue.
The European Union (EU)
With the collapse of the Greek economy, and the [rapid growth of debt] in the UK, Spain and France, there are concerns that the EU might not last past 2020.
The United States of America (USA)
My own country is a federation of states, each with its own government. California's financial crisis was compared to the one in Greece. My own state of Arizona is under boycott from other states because of its recent [immigration law]. However, I think the US has managed better than the EU because it has evolved over the past 200 years.
The Organization of the Petroleum Exporting Countries [OPEC]
Technically, OPEC is not a federation of cooperating countries, but rather a cartel of competing countries that have agreed on total industry output of oil to increase individual members' profits. Note that it was a non-OPEC company, BP, that could not "control their output" in what has now become the worst oil spill in US history. OPEC was formed in 1960, and is expected to collapse sometime around 2030 when the world's oil reserves run out. Matt Savinar has a nice article on [Life After the Oil Crash].
United Federation of Planets
The [Federation] fictitiously described in the Star Trek series appears to work well, an optimistic view of what federations could become if you let them evolve long enough.
Given the mixed results with "federation", I think I will avoid using the term for storage, and stick to the original term "scale-out architecture".
Well it's Tuesday again, and you know what that means.. IBM announcements! Today, IBM announces that next Monday marks the 60th anniversary of first commercial digital tape storage system! I am on the East coast this week visiting clients, but plan to be back in Tucson in time for the cake and fireworks next Monday.
1925 - masking tape (which 3M sold under its newly announced Scotch® brand)
1930 - clear cellulose-based tape (today, when people say Scotch tape, they usually are referring to the cellulose version)
1935 - Allgemeine Elektrizitatsgesellschaft (AEG) presents Magnetophon K1, audio recording on analog tape
1942 - Duct tape
1947 - Bing Crosby adopts audio recording for his radio program. This eliminated him doing the same program live twice per day, perhaps the first example of using technology for "deduplication".
According to the IBM Archives the [IBM 726 tape drive was formally announced May 21, 1952]. It was the size of a refrigerator, and the tape reel was the size of a large pizza. The next time you pull a frozen pizza from your fridge, you can remember this month's celebration!
When I first joined IBM in 1986, there were three kinds of IBM tape. The round reel called 3420, and the square cartridge called 3480, and the tubes that contained a wide swath of tape stored in honeycomb shelves called the [IBM 3850 Mass Storage System].
My first job at IBM was to work on DFHSM, which was specifically started in 1977 to manage the IBM 3850, and later renamed to the DFSMShsm component of the DFSMS element of the z/OS operating system. This software was instrumental in keeping disk and tape at high 80-95 percent utilization rates on mainframe servers.
While visiting a client in Detroit, the client loved their StorageTek tape automation silo, but didn't care for the StorageTek drives inside were incompatible with IBM formats. They wanted to put IBM drives into the StorageTek silos. I agreed it was a good idea, and brought this back to the attention of development. In a contentious meeting with management and engineers, I presented this feedback from the client.
Everyone in the room said IBM couldn't do that. I asked "Why not?" The software engineers I spoke to already said they could support it. With StorageTek at the brink of Chapter 11 bankruptcy, I argued that IBM drives in their tape automation would ease the transition of our mainframe customers to an all-IBM environment.
Was the reason related to business/legal concerns, or was their a hardware issue? It turned out to be a little of both. On the business side, IBM had to agree to work with StorageTek on service and support to its mutual clients in mixed environments. On the technical side, the drive had to be tilted 12 degrees to line up with the robotic hand. A few years later, the IBM silo-compatible 3592 drive was commercially available.
Rather than put StorageTek completely out of business, it had the opposite effect. Now that IBM drives can be put in StorageTek libraries, everyone wanted one, basically bringing StorageTek back to life. This forced IBM to offer its own tape automation libraries.
In 1993, I filed my first patent. It was for the RECYCLE function in DFHSM to consolidate valid data from partial tapes to fresh new tapes. Before my patent, the RECYCLE function selected tapes alphabetically, by volume serial (VOLSER). My patent evaluated all tapes based on how full they were, and sorted them least-full to most-full, to maximize the return of cartridges.
Different tape cartridges can hold different amounts of data, especially with different formats on the same media type, with or without compression, so calculating the percentage full turned out to be a tricky algorithm that continues to be used in mainframe environments today.
The patent was popular for cross-licensing, and IBM has since filed additional patents for this invention in other countries to further increase its license revenue for intellectual property.
In 1997, IBM launched the IBM 3494 Virtual Tape Server (VTS), the first virtual tape storage device, blending disk and tape to optimal effect. This was based off the IBM 3850 Mass Storage Systems, which was the first virtual disk system, that used 3380 disk and tape to emulate the older 3350 disk systems.
In the VTS, tape volume images would be emulated as files on a disk system, then later moved to physical tape. We would call the disk the "Tape Volume Cache", and use caching algorithms to decide how long to keep data in cache, versus destage to tape. However, there were only a few tape drives, and sometimes when the VTS was busy, there were no tape drives available to destage the older images, and the cache would fill up.
I had already solved this problem in DFHSM, with a function called pre-migration. The idea was to pre-emptively copy data to tape, but leave it also on disk, so that when it needed to be destaged, all we had to do was delete the disk copy and activate the tape copy. We patented using this idea for the VTS, and it is still used in the successor models of IBM Sysem Storage TS7740 virtual tape libraries today.
Today, tape continues to be the least expensive storage medium, about 15 to 25 times less expensive, dollar-per-GB, than disk technologies. A dollar of today's LTO-5 tape can hold 22 days worth of MP3 music at 192 Kbps recording. A full TS1140 tape cartridge can hold 2 million copies of the book "War and Peace".
(If you have not read the book, Woody Allen took a speed reading course and read the entire novel in just 20 minutes. He summed up the novel in three words: "It involves Russia." By comparison, in the same 20 minutes, at 650MB/sec, the TS1140 drive can read this novel over and over 390,000 times.)
If you have your own "war stories" about tape, I would love to hear them, please consider posting a comment below.
Continuing on the [IBM Storage Launch of February 9], John Sing has offered to write the following guest post about the [announcement] of IBM Scale Out Network Attached Storage [IBM SONAS]. John and I have known each other for a while, traveled the world to work with clients and speak at conferences. He is an Executive IT Consultant on the SONAS team.
Guest Post written by John Sing, IBM San Jose, California
What is IBM SONAS? It’s many things, so let’s start with this list:
It’s IBM’s delivery of a productized, pre-packaged Scale Out NAS global virtual file server, delivered in a easy-to-use appliance
IBM’s solution for large enterprise file-based storage requirements, where massive scale in capacity and extreme performance is required, especially for today’s modern analytics-based Competitive Advantage IT applications
Scales to many petabytes of usable storage and billions of files in a single global namespace
Provides integrated central management, central deployment of petabyte levels of storage
Modular commercial-off-the-shelf [COTS] building blocks. I/O, storage, network capacity scale independently of each other. Up to 30 interface nodes and 60 storage nodes, in an IBM General Parallel File System [GPFS]-based cluster. Each 10Gb CEE interface node port is capable of streaming at 900 MB/sec
Files are written in block-sized chunks, striped over as many multiple disk drives in parallel – aggregating throughput on a massive scale (both read and write), as well as providing auto-tuning, auto-balancing
Functionality delivered via one program product, IBM SONAS Software, which provides all of above functions, along with clustered CIFS, NFS v2/v3 with session auto-failover, FTP, high availability, and more
IBM SONAS makes automated tiered storage achievable and realistic at petabyte levels:
Integrated high performance parallel scan engine capable of identifying files at over 10 million files per minute per node
Integrated parallel data movement engine to physically relocate the data within tiered storage
And we’re just scratching the surface. IBM has plans to deploy additional protocols, storage hardware options, and software features.
However, the real question of interest should be, “who really needs that much storage capacity and throughput horsepower?”
The answer may surprise you. IMHO, the answer is: almost any modern enterprise that intends to stay competitive. Hmmm…… Consider this: the reason that IT exists today is no longer to simply save cost (that may have been true 10 years ago). Everyone is reducing cost… but how much competitive advantage is purchased through “let’s cut our IT budget by 10% this year”?
Notice that in today’s world, there are (many) bright people out there, changing our world every day through New Intelligence Competitive Advantage analytics-based IT applications such as real time GPS traffic data, real time energy monitoring and redirection, real time video feed with analytics, text analytics, entity analytics, real time stream computing, image recognition applications, HDTV video on demand, etc. Think of how GPS industry, cell phone / Twitter / Facebook, iPhone and iPad applications, as examples, are creating whole new industries and markets almost overnight.
Then start asking yourself, “What's behind these Competitive Advantage IT applications – as they are the ones that are driving all my storage growth? Why do they need so much storage? What do those applications mean for my storage requirements?”
To be “real-time”, long-held IT paradigms are being broken every day. Things like “data proximity”: we can no longer can extract terabytes of data from production databases and load them to a data warehouse – where’s the “real-time” in that? Instead, today’s modern analytics-based applications demand:
Multiple processes and servers (sometimes numbering in the 100s) simultaneously ….
Running against hundreds of terabytes of data of live production data, streaming in from expanding number of smarter sensors, input devices, users
Producing digital image-intensive results that must be programatically sent to an ever increasing number of mobile devices in geographically dispersed storage
Requiring parallel performance levels, that used to be the domain only of High Performance Computing (HPC)
This is a major paradigm shift in storage – and that is the solution and storage capabilities that IBM SONAS is designed to address. And of course, you should be able to save significant cost through the SONAS global virtual file server consolidation and virtualization as well.
Certainly, this topic warrants more discussion. If you found it interesting, contact me, your local IBM Business Partner or IBM Storage rep to discuss Competitive Advantage IT applications and SONAS further.
Am I dreaming? On his Storagezilla blog, fellow blogger Mark Twomey (EMC) brags about EMC's standard benchmark results, in his post titled [Love Life. Love CIFS.]. Here is my take:
A Full 180 degree reversal
For the past several years, EMC bloggers have argued, both in comments on this blog, and on their own blogs, that standard benchmarks are useless and should not be used to influence purchase decisions. While we all agree that "your mileage may vary", I find standard benchmarks are useful as part of an overall approach in comparing and selecting which vendors to work with, and which architectures or solution approaches to adopt, and which products or services to deploy. I am glad to see that EMC has finally joined the rest of the planet on this. I find it funny this reversal sounds a lot like their reversal from "Tape is Dead" to "What? We never said tape was dead!"
Impressive CIFS Results
The Standard Performance Evaluation Corporation (SPEC) has developed a series of NFS benchmarks, the latest, [SPECsfs2008] added support for CIFS. So, on the CIFS side, EMC's benchmarks compare favorably against previous CIFS tests from other vendors.
On the NFS side, however, EMC is still behind Avere, BlueArc, Exanet, and IBM/NetApp. For example, EMC's combination of Celerra gateways in front of V-Max disk systems resulted in 110,621 OPS with overall response time of 2.32 milliseconds. By comparison, the IBM N series N7900 (tested by NetApp under their own brand, FAS6080) was able to do 120,011 OPS with 1.95 msec response time.
Even though Sun invented the NFS protocol in the early 1980s, they take an EMC-like approach against standard benchmarks to measure it. Last year, fellow blogger Bryan Cantrill (Sun) gives his [Eulogy for a Benchmark]. I was going to make points about this, but fellow blogger Mike Eisler (NetApp) [already took care of it]. We can all learn from this. Companies that don't believe in standard benchmarks can either reverse course (as EMC has done), or continue their downhill decline until they are acquired by someone else.
(My condolences to those at Sun getting laid off. Those of you who hire on with IBM can get re-united with your former StorageTek buddies! Back then, StorageTek people left Sun in droves, knowing that Sun didn't understand the mainframe tape marketplace that StorageTek focused on. Likewise, many question how well Oracle will understand Sun's hardware business in servers and storage.)
What's in a Protocol?
Both CIFS and NFS have been around for decades, and comparisons can sometimes sound like religious debates. Traditionally, CIFS was used to share files between Windows systems, and NFS for Linux and UNIX platforms. However, Windows can also handle NFS, while Linux and UNIX systems can use CIFS. If you are using a recent level of VMware, you can use either NFS or CIFS as an alternative to Fibre Channel SAN to store your external disk VMDK files.
The Bigger Picture
There is a significant shift going on from traditional database repositories to unstructured file content. Today, as much as [80 percent of data is unstructured]. Shipments this year are expected to grow 60 percent for file-based storage, and only 15 percent for block-based storage. With the focus on private and public clouds, NAS solutions will be the battleground for 2010.
So, I am glad to see EMC starting to cite standard benchmarks. Hopefully, SPC-1 and SPC-2 benchmarks are forthcoming?