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Tony Pearson is a Master Inventor and Senior IT Specialist for the IBM System Storage product line at the
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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. You can also follow him on Twitter @az990tony.
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Continuing my coverage of the 30th annual [Data Center Conference]. Here is a recap of the Monday afternoon sessions:
IBM Watson and your Data Center
Steve Sams, IBM VP of Site and Facilities Services, cleverly used IBM Watson as a way to explain how analytics can be used to help manage your data center. Sadly, most of the people at my table missed the connection between IBM Watson and Analytics. How does answering a single trivia question in under three seconds relate to the ongoing operations of a data center? If you were similarly confused, take a peak at my series of IBM Watson blog posts:
The analyst who presented this topic was probably the fastest-speaking Texan I have met. He covered various aspects of Cloud Computing that people need to consider. Why hasn't Cloud taken off sooner? The analyst feels that Cloud Computing wasn't ready for us, and we weren't ready for Cloud Computing. The fundamentals of Cloud Computing have not changed, but we as a society have. Now that many end users are comfortable consuming public cloud resources, from Facebook to Twitter to Gmail, they are beginning to ask for similar from their corporate IT.
Legal issues - see this hour-long video, [Cloud Law & Order], which discusses legal issues related to Cloud Computing.
Employee staffing - need to re-tool and re-train IT employees to start thinking of their IT as a service provider internally.
Hybrid Cloud - rather than struggle choosing between private and public cloud methodologies, consider a combination of both.
University of Rochester Medical Center (URMC) Cracks Code on Data Growth
Often times, the hour is split, 30 minutes of the sponsor talking about various products, followed by 30 minutes of the client giving a user experience. Instead, I decided to let the client speak for 45 minutes, and then I moderated the Q&A for the remaining 15 minutes. This revised format seemed to be well-received!
University of Rochester is in New York, about 60 miles east of Buffalo, and 90 miles from Toronto across Lake Ontario. Six years ago, Rick Haverty joined URMC as the Director of Infrastructure services, managing 130 of the 300 IT personnel at the Medical Center. I met Rick back in May, when he presented at the IBM [Storage Innovation Executive Summit] in New York City.
URMC has DS8000, DS5000, XIV, SONAS, Storwize V7000 and is in the process of deploying Storwize V7000 Unified. He presented how he has used these for continuous operations and high availability, while controlling storage growth and costs.
The Q&A was lively, focusing on how his team manages 1PB of disk storage with just four storage administrators, his choice of a "Vendor Neutral Archive" (VNA), and his experiences with integration.
This was a great afternoon, and I was glad to get all my speaking gigs done early in the week. I would like to thank Rick Haverty of URMC for doing a great job presenting this afternoon!
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.
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.
Well, I had a pleasant vacation. I took a trip up to beautiful Lake Powell in Northern Arizona as part of a "Murder Mystery Dinner" weekend. This trip was organized by AAA and Lake Powell in association with the professionals at [Murder Ink Productions] out of Phoenix.
The trip involved two busloads of people from Tucson and Phoenix driving up to Lake Powell, with a series of meals that introduced all the characters and gave out clues to solve a murder. At the end of the dinner on the last evening, we had to guess who dunnit, how, and why. I solved it, and got this lovely tee-shirt.
More importantly, the trip gave me a chance to read
[The Numerati] by Stephen Baker. The author explains all the different ways that "analysts" are able to crunch through the large volumes of data to gain insight. He has chapters on how this is done for shoppers in retail sales, voters for upcoming elections, patients for medical care, and even matchmaking services like chemistry.com. Like the Murder Mystery Dinner, there are too many suspects and too many clues, but these number-crunchers, which Mr. Baker calls The Numerati, are able to figure out through advanced business analytics.
FTC Notice: I recommend this book. I did not receive any compensation to mention this book on this blog, I did not receive a copy of the book free for this review, and I do not know the author. Everyone in my staff is reading this book, and I borrowed a copy from a co-worker.
If you don't understand how this all works, here is a quick 6-minute [video] on YouTube.