Primary Dedupe where are you?

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I am a bit surprised that primary storage deduplication has not taken off in a big way, unlike the times when the buzz of deduplication first came into being about 4 years ago.

When the first deduplication solutions first came out, it was particularly aimed at the backup data space. It is now more popularly known as secondary data deduplication, the technology has reduced the inefficiencies of backup and helped sparked the frenzy of adulation of companies like Data Domain, Exagrid, Sepaton and Quantum a few years ago. The software vendors were not left out either. Symantec, Commvault, and everyone else in town had data deduplication for backup and archiving.

It was no surprise that EMC battled NetApp and finally won the rights to acquire Data Domain for USD$2.4 billion in 2009. Today, in my opinion, the landscape of secondary data deduplication has pretty much settled and matured. Practically everyone has some sort of secondary data deduplication technology or solution in place.

But then the talk of primary data deduplication hardly cause a ripple when compared a few years ago, especially here in Malaysia. Yeah, the IT crowd is pretty fickle that way because most tend to follow the trend of the moment. Last year was Cloud Computing and now the big buzz word is Big Data.

We are here to look at technologies to solve problems, folks, and primary data deduplication technology solutions should be considered in any IT planning. And it is our job as storage networking professionals to continue to advise customers about what is relevant to their business and addressing their pain points.

I get a bit cheesed off that companies like EMC, or HDS continue to spend their marketing dollars on hyping the trends of the moment rather than using some of their funds to promote good technologies such as primary data deduplication that solve real life problems. The same goes for most IT magazines, publications and other communications mediums, rarely giving space to technologies that solves problems on the ground, and just harping on hypes, fuzz and buzz. It gets a bit too ordinary (and mundane) when they are trying too hard to be extraordinary because everyone is basically talking about the same freaking thing at the same time, over and over again. (Hmmm … I think I am speaking off topic now .. I better shut up!)

We are facing an avalanche of data. The other day, the CEO of Nexenta used the word “data tsunami” but whatever terms used do not matter. There is too much data. Secondary data deduplication solved one part of the problem and now it’s time to talk about the other part, which is data in primary storage, hence primary data deduplication.

What is out there?  Who’s doing what in term of primary data deduplication?

NetApp has their A-SIS (now NetApp Dedupe) for years and they are good in my books. They talk to customers about the benefits of deduplication on their FAS filers. (Side note: I am seeing more benefits of using data compression in primary storage but I am not going to there in this entry). EMC has primary data deduplication in their Celerra years ago but they hardly talk much about it. It’s on their VNX as well but again, nobody in EMC ever speak about their primary deduplication feature.

I have always loved Ocarina Networks ECO technology and Dell don’t give much hoot about Ocarina since the acquisition in  2010. The technology surfaced a few months ago in Dell DX6000G Storage Compression Node for its Object Storage Platform, but then again, all Dell talks about is their Fluid Data Architecture from the Compellent division. Hey Dell, you guys are so one-dimensional! Ocarina is a wonderful gem in their jewel case, and yet all their storage guys talk about are Compellent  and EqualLogic.

Moving on … I ought to knock Oracle on the head too. ZFS has great data deduplication technology that is meant for primary data and a couple of years back, Greenbytes took that and made a solution out of it. I don’t follow what Greenbytes is doing nowadays but I do hope that the big wave of primary data deduplication will rise for companies such as Greenbytes to take off in a big way. No thanks to Oracle for ignoring another gem in ZFS and wasting their resources on pre-sales (in Malaysia) and partners (in Malaysia) that hardly know much about the immense power of ZFS.

But an unexpected source coming from Microsoft could help trigger greater interest in primary data deduplication. I have just read that the next version of Windows Server OS will have primary data deduplication integrated into NTFS. The feature will be available in Windows 8 and the architectural view is shown below:

The primary data deduplication in NTFS will be a feature add-on for Windows Server users. It is implemented as a filter driver on a per volume basis, with each volume a complete, self describing unit. It is cluster aware, and fully crash consistent on all operations.

The technology is Microsoft’s own technology, built from scratch and will be working to position Hyper-V as an strong enterprise choice in its battle for the server virtualization space with VMware. Mind you, VMware already has a big, big lead and this is just something that Microsoft must do-or-die to keep Hyper-V playing catch-up. Otherwise, the gap between Microsoft and VMware in the server virtualization space will be even greater.

I don’t have the full details of this but I read that the NTFS primary deduplication chunk sizes will be between 32KB to 128KB and it will be post-processing.

With Microsoft introducing their technology soon, I hope primary data deduplication will get some deserving accolades because I think most companies are really not doing justice to the great technologies that they have in their jewel cases. And I hope Microsoft, with all its marketing savviness and adeptness, will do some justice to a technology that solves real life’s data problems.

I bid you good luck – Primary Data Deduplication! You deserved better.

Phoenix rising from OpenSolaris ashes

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I got a little nostalgic over the weekend. As I was working on Solaris 11 x86 over the past few weeks, I got a little bit peeved about how much Oracle has changed the OS.

Command like ifconfig doesn’t not appear to be very functional anymore and instead ipadm has taken over most of the configuration options. And when I working with Jumpstart (damn!), it does not work the way that I know anymore. And now AI (Automated Install) has taken over Jumpstart and I got to relearn the whole what-ca-ma-callit. Dang!

I remembered the day when Solaris x86 first came out in the early 90s. I was ecstatic because I could finally test and run Solaris on x86 platform. I could get things running at home and have fun with it. Drivers were limited then (and still is but has gotten much better) but I was happily hacking away together with other Linux distros as the open source revolution was just beginning. After I joined NetApp, things started to change and I abandoned Solaris in favour of Linux as my job, as well as my interest, were on Linux, especially RedHat. I eventually got my RHCE and completely lost touch with Solaris. By 2005, when OpenSolaris was announced under CDDL (Common Development and Distribution License), I was no longer well versed with the developments of Solaris and OpenSolaris.

Enough about my nostalgia because I am beginning to see a young phoenix (a mythical firebird) rising from the mess of what Oracle did with OpenSolaris! Since Oracle purchased Sun in 2010, Oracle has practically burned OpenSolaris to ashes. On August 13 2010, Oracle announced the end of OpenSolaris in an internal memo and it read:

Solaris Engineering,

Today we are announcing a set of decisions regarding the path to
Solaris 11, and answering key pending questions on open source, open
development, software and binary licenses, and how developers and
early adopters will be able to use Solaris 11 technology before its
release in 2011.

As you all know, the term “OpenSolaris” has been used colloquially to
refer to any or all of a collection of source code, a development
model, a web site, a logo, a binary release, a source license, a
community, and many other related things. So it’s taken a while to go
over each issue from an organizational and business perspective, and
align on the correct next step. Therefore, please take the time to
read all of the detail here carefully. We’ll discuss our strategy
first, and then the decisions and changes to our policies and
processes that implement that strategy.

If you want the entire memo (and all the fa-lah-lah that goes with it), go to Steven Stallion’s blog. Incidentally Steven Stallion was the OpenSolaris kernel developer who leaked the memo into the open.

It became pretty obvious that Oracle business suit culture and “is this going to make money?” ways were suffocating talents and innovations of the Sun engineering tribes. Some of the high profile leavers were James Gosling (father of Java) and Jeff Bonwick (father of RAID-Z and led the ZFS development team in Sun). And there were many top talents exodus within 90-120 days after the Oracle acquisition.

The key technologies that went into OpenSolaris (and Solaris) were slowly but surely deprived of their inventors’ and maintainers nourishment. These technologies were:

  • ZFS (Project Pacific)
  • DTrace
  • Zones (aka Solaris Containers, aka Project Kevlar)
  • Fault Management Architecture (FMA)
  • Service Management Facility (SMF)
  • Advanced Network Virtualization (Project Crossbow)
  • Least-privilege

and many more. Some of these technologies were already open under CDDL license but some were still very much proprietary to Sun (I mean, Oracle). It was difficult to use what was available under OpenSolaris CDDL license to rebuild again, especially when the inventors, talents and maintainers are now all scattered in companies like Delphix, Nexenta, Greenbytes, Joyent and so on .

At the end of last year, shortly before Solaris 11 was announced by Oracle, the people who are passionate about OpenSolaris (and Solaris) have got together in full force again. Dubbed “Project Illumos“, the key people who has developed for Sun convened to build a new open-source, Solaris-based operating environment. The proprietary bits that are closely guarded by Oracle are going to be either rebuilt from scratch or ported from BSD into the last OpenSolaris-kernel before Oracle killed it. That kernel was Solaris Nevada, which was supposed to be the successor of Solaris 10.

The Illumos team already has a bootable and working operating environment and new developments are going on at a frantic pace. From the words of Bryan Cantrill (father of DTrace) and now VP of Engineering at Joyent,

“illumos was not designed to be a fork,but rather an entirely open downstream repository of OpenSolaris”

And the talents congregating to the Illumos project (like moths to a flame) are super-stellar. Just have a look at this list:

  • ZFS –> Matt Ahrens, Eric Schrock,  George Wilson, Adam Leventhal, Bill Pijewski and BrendanGregg
  • SMF –> Dan McDonald and Sumit Gupta
  • DTrace –> Bryan Cantrill, Adam Leventhal, Brendan Gregg, Eric Schrock, Dave Pacheco
  • Zones & Jumpstart –> Jerry Jelinek
  • and many, many more.

KVM (the Linux kernel-based virtual machine) is being added into the Illumos operating environment, giving it the final piece of the puzzle.

I cannot help but to feel extremely proud that OpenSolaris (and Solaris) is not dead yet and it’s alive and rising. Oracle cannot lay claim to the source code and the rights of Illumos (according to Bryan Cantrill) without itself abiding to the CDDL licensing and distribution scheme that it had killed off a year ago.

And this is indeed the young phoenix rising!

Why VAAI?

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This is Part 2 of my previous blog about VAAI (vStorage API for Array Integration) with more details about VAAI. VAAI offloads some of the I/O related functions to the VAAI-enable storage array, hence giving the hypervisor more compute and memory resource to do it other functions. And the storage array, upon receiving the VAAI command, will execute whatever that is required of it.

Why is VAAI important? What does it do that makes it so useful and important to the hypervisor?

VAAI is about a set of new SCSI commands. And there are 3 important ones:

  • WRITE-SAME
  • XSET
  • ATS

What exactly do these SCSI commands do?

WRITE-SAME is a SCSI command that instructs the storage array to zeroes the virtual VMDK disks or VMFS LUNs. This usually happens when guest OS require a brand new set of virtual disks and initializing the virtual disks is required. In the past (before VAAI), the hypervisor has to repetitively send 0s to the storage to perform the disks zeroing. As shown in the diagram below, you can see that each zero operation is sent from the hypervisor to the storage.

This back-and-forth of sending 0s and acknowledgments between the hypervisor and the storage is not efficient. With VAAI, the command WRITE-SAME  is sent from the hypervisor to the storage array and the storage array will do the zeroing on the disks and LUNs. The hypervisor will not intervene with the process until it gets and acknowledgment of its completion. See diagram below of how VAAI helps in bulk-zeroing of disks and LUNs in the storage array.

The animated GIFs are the taken from Luke Reed’s blog, a fantastic read.

The second VAAI SCSI command is XSET and it performs hardware accelerated full copy. This command is also known as  XCOPY and it offloads the process of copying the blocks of data that makeup a VMDK file. Such copying operations occur when the hypervisor is doing things like VM cloning, Storage vMotion or VM creation from templates (bulk copying to create many similar VMs in one go).

Again with the courtesy of Luke Reed’s animated GIFs, the diagram below shows a full copy without VAAI

and after implementing VAAI, where the full, bulk copy operations is offloaded to the storage array to execute.

The third and last SCSI command of VAAI is ATS or hardware-assisted locking. ATS stands for Atomic, Test and Set and the command allow the hypervisor to lock only the required blocks rather than the entire LUN.

Without VAAI, the entire LUN temporarily could be locked by the numerous VMFS operations of one single hypervisor and this prevents other hypervisors from accessing the shared LUNs. The ATS API offloads lock management from the host to the storage array and keeps the LUN available by locking only required blocks, not the entire VMFS file system. Please see the pleasing diagrams below of

(without VAAI ATS)

(with VAAI ATS)

And if you want to see the VAAI Hardware Accelerated Full Copy (aka XSET) in action, here’s a little video showing how it is done in an EMC environment.

The primary significance and noticeable benefit is definitely performance. The secondary benefit, though not so obvious, is allowing VMware and its hypervisor to scale because it does not get bogged down by some of the I/O functions that it is not meant to do.

There were some new additions in vSphere 5.0 for VAAI. From its FAQ, it listed in ESX5.0, support for NAS Hardware Acceleration is included with support for the following primitives:

  • Full File Clone – Like the Full Copy VAAI primitive provided for block arrays, this Full File Clone primitive enables virtual disks to be cloned by the NAS device.
  • Native Snapshot Support – Allows creation of virtual machine snapshots to be offloaded the array.
  • Extended Statistics – Enables visibility to space usage on NAS datastores and is useful for Thin Provisioning.
  • Reserve Space – Enables creation of thick virtual disk files on NAS.

So, there you have it folks. Why VAAI? Here’s why.

VAAI to go!

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First of all, let me apologize. I am guilty of not updating my blogs as regularly as I did in the past. Things got a bit crazy after Christmas and I had to juggle several things that demand more of my attention but I am confident things will sort itself out soon enough.

Today’s topic is about VMware’s VAAI (vSphere vStorage API for Array Integration). This feature was announced more than 3 years ago but was only introduced in vSphere 4.1 July 2010 and now with newer enhancements in the latest release of vSphere 5.0.

What is this VAAI and what does this mean from a storage perspective?

When VMware came into prominence in version 3.0/3.5 time, the whole world revolved around the ESX hypervisor. It tried to do everything on its own, in its own proprietary nature. Given its nascent existence then, ESX had to do what it had to do and control everything with its hypervisor universe. Yes, it was a good move then and it did what it was supposed to do. This was back when server virtualization was in its infancy, and resources requirements were less demanding.

Hence when VMware wants to initialize VMs, or create VMDK files on the datastore, or creating clones or snapshots, or even executing VMotion and Storage VMotion, it tends to execute it at the hypervisor level. For example, when creating virtual disks with VMFS, most of the commands to initialization of the disks were done at the VMFS level. Zeroing the virtual disks would mean sending zeroing commands to the actual physical disks on the shared storage. And this would go on back and forth, taxing the CPU cycles and memory on the hypervisor layer, and sending wasteful and unnecessary zeroes over the network to the storage array. This was very inefficient, wasteful and degrades the performance tremendously, especially at the hypervisor layer (compute and memory).

There are also other operations such as virtual disks locking that locks up the entire LUN that housed several datastores. Again, not good.

But VMware took off like a rocket, and quickly established itself as a Tier 1, enterprise server virtualization solution addressing the highest demands of the enterprise. It is also defining the future of Cloud Computing, building exorbitant requirements as it pushes forward. And VMware began to realize that if the hypervisor is to scale, it needs to leave the I/O operations to the “experts”, and the “experts” here being the respective storage array itself.

So, in version 4.1, VAAI (vStorage API for Array Integration) was introduced as an API suite, following 3 other earlier APIs – vStorage API for Site Recovery Manager (SRM), vStorage API for Data Protection and vStorage API for Multipathing.

In a nutshell, as I have mentioned before, VAAI offloads I/O and storage related operations to the VAAI-capable storage array (leave it to the experts) as shown in the diagram below:

 

Of course, the storage vendors themselves has to rework their array OS layer to integrate with the VAAI API. You can say that the VAAI are “hooks” that enhances the storage connectivity and communications with vSphere’s hypervisor. But then again, if you look at it from the other angle, vSphere need the storage vendors more in order for its universe to scale. Good thing VMware has a big, big market share. Imagine if there are no takers for the VAAI APIs. That would be a strange predicament instead.

What is the big deal that we get from VAAI? The significant and noticeable benefit is increase performance. By offloading the I/O functionality and operations to the storage array itself, the hypervisor and the compute and memory resource are not bogged down, resulting in higher performance and better response time to serve its VMs and other VM operations.

I am going off to another meeting and I shall write of VAAI in more details later. Until the next entry, adios and have a great year ahead.

The definition of Cloud Computing … really

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Happy New Year! I am looking forward to the year of 2012.

Lately, I have been involved in Cloud Computing forums and I have been reading articles on Cloud Computing. I even took up a 5-day course on Cloud Computing in order to prepare myself for the inevitable. Yes, Cloud Computing is here to stay, but we joke about it, don’t we? I think the fun word of Cloud Computing is “cloudy“, which is indeed very true.

As I ingest more and more information about Cloud Computing, the definition of how different people has different perspective or opinion about Cloud Computing has never been “cloudier“. It is fuzzy, hazy, and confusing. And in the forums, many were saying that virtualization is Cloud Computing. What do you think?

I found that one definition of Cloud Computing very definitive, yet simple. This definition comes from the National Institute of Standards and Technology (NIST) of the US Department of Commerce. In its publication #800145, NIST defines Cloud Computing to have the following 5 essential characteristics (duplicated in verbatim):

  • On-demand self-service. A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.
  • Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).
  • Resource pooling. The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth.
  • Rapid elasticity. Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.
  • Measured service. Cloud systems automatically control and optimize resource use by leveraging a metering capability1 at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

The 5 essential characteristics are very important in determining whether Virtualization = Cloud Computing, and I know there are a lot of people out there that says that Virtualization equates Cloud Computing. Let’s see the table below:

 

Some readers might argue about the “YES” or “NO” in the above comparison, but I do not want to dwell on the matter. Yes, I believe that many of these things are doable in their own right but with different level of complexity and costs. The objective is to settle the arguments and confusions of Cloud Computing, accept some definitive terms and move on.

As you can see from the table above, Virtualization does not equate to Cloud Computing. We can say that Virtualization enables Cloud Computing to happen. It is the pre-cursor to Cloud Computing.

In Cloud Computing, there are different Service Models. NIST defines 3 different Service Models. They are:

  • Software as a Service (SaaS). The capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure2. The applications are accessible from various client devices through either a thin client interface, such as a web browser (e.g., web-based email), or a program interface. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
  • Platform as a Service (PaaS). The capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages, libraries, services, and tools supported by the provider.3 The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, or storage, but has control over the deployed applications and possibly configuration settings for the application-hosting environment.
  • Infrastructure as a Service (IaaS). The capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, and deployed applications; and possibly limited control of select networking components (e.g., host firewalls).

And NIST went on to define the Deployment Models of Cloud Computing as listed below:

  • Private cloud. The cloud infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business units). It may be owned, managed, and operated by the organization, a third party, or some combination of them, and it may exist on or off premises.
  • Community cloud. The cloud infrastructure is provisioned for exclusive use by a specific community of consumers from organizations that have shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be owned, managed, and operated by one or more of the organizations in the community, a third party, or some combination of them, and it may exist on or off premises.
  • Public cloud. The cloud infrastructure is provisioned for open use by the general public. It may be owned, managed, and operated by a business, academic, or government organization, or some combination of them. It exists on the premises of the cloud provider.
  • Hybrid cloud. The cloud infrastructure is a composition of two or more distinct cloud infrastructures (private, community, or public) that remain unique entities, but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds).

There! Cloud Computing by the definition of NIST. It is simple, easily understood and most importantly, it give us the context of what we are looking for in the sea of confusion. Here’s the link to NIST’s PDF.

We can argue till the cows come home but it is best to stick to a simple definition of Cloud Computing and focus on other more important aspects of the cloud.

I hope to share more of my Cloud Computing experience with you and storage will have a big part to play in it.

2011 in review – my 100th blog entry

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The WordPress.com stats helper monkeys prepared a 2011 annual report for this blog

Here’s an excerpt:

The concert hall at the Syndey Opera House holds 2,700 people. This blog was viewed about 8,200 times in 2011. If it were a concert at Sydney Opera House, it would take about 3 sold-out performances for that many people to see it.

Click here to see the complete report.

Apple chomps Anobit

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A few days ago, Apple paid USD$500 million to buy an Israeli startup, Anobit, a maker of flash storage technology.

Obviously, one of the reasons Apple did so is to move up a notch to differentiate itself from the competition and positions itself as a premier technology innovator. It has won the MP3 war with its iPod, but in the smartphones, tablets and notebooks space, Apple is being challenged strongly.

Today, flash storage technology is prevalent, and the demand to pack more capacity into a small real-estate of flash will eventually lead to reliability issues. The most common type of NAND flash storage is the MLC (multi-level cells) versus the more expensive type called SLC (single level cells).

But physically and the internal-build of MLC and SLC are the exactly the same, except that in SLC, one cell contains 1 bit of data. Obviously this means that 2 or more bits occupy one cell in MLC. That’s the only difference from a physical structure of NAND flash. However, if you can see from the diagram below, SLCs has advantages over MLCs.

 

NAND Flash uses electrical voltage to program a cell and it is always a challenge to store bits of data in a very, very small cell. If you apply too little voltage, the bit in the cell does not register and will result in something unreadable or an error. If you apply too much voltage, the adjacent cells are disturbed and resulting in errors in the flash. Voltage leak is not uncommon.

The demands of packing more and more data (i.e. more bits) into one cell geometry results in greater unreliability. Though the reliability of  the NAND Flash storage is predictable, i.e. we would roughly know when it will fail, we will eventually reach a point where the reliability of MLCs will no longer be desirable if we continue the trend of packing more and more capacity.

That’s when Anobit comes in. Anobit has designed and implemented architectural changes of the way NAND Flash storage is used. The technology in laymen terms comes in 2 stages.

  1. Error reduction – by understanding what causes flash impairment. This could be cross-coupling, read disturbs, data retention impairments, program disturbs, endurance impairments
  2. Error Correction and Signal Processing – Advanced ECC (error-correcting code), and introducing the patented (and other patents pending) Memory Signal Processing (TM) to improve the reliability and performance of the NAND Flash storage as show in the diagram below:

In a nutshell, Anobit’s new and innovative approach will result in

  • More reliable MLCs
  • Better performing MLCs
  • Cheaper NAND Flash technology

This will indeed extend the NAND Flash technology into greater innovation of flash storage technology in the near future. Whatever Apple will do with Anobit’s technology is anybody’s guess but one thing is certain. It’s going to propel Apple into newer heights.

IDC Worldwide Storage Software QView 3Q11

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I did not miss this when the IDC report of worldwide storage software for Q3 2011 was released a couple of weeks ago. I was just too busy to work on it until just now.

The IDC QView report covers 7 functional areas of storage software:

  • Data protection and recovery software
  • Storage replication software
  • Storage infrastructure software
  • Storage management software
  • Device management software
  • Data archiving software
  • File system software

All areas are growing and Q3 grew 9.7% when compared with the figures of 3Q2010. In the overall software market, EMC holds the top position at 24.5% followed by Symantec (15.3%) and IBM (14.0%). Here’s a table to show the overall standings of the storage software vendors.

 

In fact, EMC leads in 3 areas of storage infrastructure management, storage management and device management. But the fastest growing area is data archiving software with a pace of 12.2% following by storage and device management of 11.3%.

HP is not in the table, but IDC reported that the biggest growth is coming from HP with a 38.2% growth, boosted by its acquisition of 3PAR. Watch out for HP in the coming quarters. Also worthy of note is the rate Symantec has been experiencing. Their was only 2.2% and IBM, at #3, is catching up fast. I wonder what’s happening in Symantec having seeing them losing their lofty heights in recent years.

The storage software market is a USD$3.5 billion market and it is the market that storage vendors are placing more importance. This market will grow.

Captain Dynamo Storage System

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My research on file systems brought me to an very interesting piece of article. It is titled “Dynamo: Amazon’s Highly Available Key-Value Store” dated 2007.

Yes, this is an internal storage systems designed and developed in Amazon to scale and support Amazon Web Services (AWS). It is a very complex piece of technology and the paper is highly technical (not for the faint of heart). And of all places, Amazon is probably the last place you think you would find such smart technology, but it’s true. AWS engineers are slowly revealing the many of their innovations (think Amazon Silk browser technology).

And it appears that many of the latest cloud-based computing and services companies such as Amazon, Google and many others have been developing new methods of storing data objects. These methods are very different from the traditional methods of storing data, and many are no longer adopting the relational database model (RDBMS) to scale their business.

The traditional 3-tier architecture often adopted by web-based (before the advent of “cloud”), is evolving. As shown in the diagram below:

the foundation tier is usually a relational database (or a distributed relational database), communicating with the back-end storage (usually a SAN).

All that is changing because the relational database model is not keeping up with the tremendous pace of the proliferation of web-based and cloud-based objects or unstructured data. As explained by Alex Iskold, a writer of ReadWriteWeb, there are scalability issues with the conventional relational database.

 

Before I get to the scalability issues mentioned in the above diagram, let me set the floor for discussion.

For theoretical schoolers of relational database, the term ACID defines and guarantees the transactional reliability of relational databases. ACID stands for Atomicity, Consistency, Isolation and Durability. According to Wikipedia, “transactions provide an “all-or-nothing” proposition, stating that each work-unit performed in a database must either complete in its entirety or have no effect whatsoever. Further, the system must isolate each transaction from other transactions, results must conform to existing constraints in the database, and transactions that complete successfully must get written to durable storage.”

ACID has been the cornerstone of relational database from the very beginning. But as the demands of greater scalability and greater distribution of data, all 4 components of ACID – Atomicity, Consistency, Isolation, Durability – can no longer hold true. Hence, the CAP Theorem.

CAP Theorem (aka Brewer’s Theorem) stands for Consistency, Availability and Partition Tolerance. In the ACM (Association of Computing Machinery) conference in 2000, Eric Brewer of University of California, Berkeley delivered the theorem. It states that it is impossible for a distributed computer system (or a database system) to simultaneously guarantee all 3 components – Consistency, Availability and Partition Tolerance.

Therefore, as the database systems become more and more distributed in cyberspace, the ACID theorem begins to break down. All 4 components of ACID cannot be guaranteed simultaneously anymore as the database systems begin to become more and more distributed.

So when we get back to the diagram, both the concepts on left and right – Master/Slave OR Multiple Peers – will put a tremendous strain on the single, non-distributed relational database.

New data models are surfacing to handling the very distributed data sets. Distributed object-based  “file systems” and NoSQL type of databases are some of the unconventional data storage “systems” that are beginning to surface as viable alternatives to the relational database method in cyberspace. And one of them is the Amazon Dynamo Storage System. (ADSS)

ADSS is a highly available, Amazon-proprietary key-value distributed data store. ADSS has both the properties of distributed hash table and a database and it is used internally to power various Cloud Services in Amazon Web Services (AWS).

 

It behaves like a relational database where it stores data objects to be retrieved. However, the data objects are not stored in a table format of a conventional relational database. Instead, the data is stored in a distributed hash table and data content or value is retrieved with a key, hence a key-value data model.

The data content is stored and retrieved through a simple put and get interface, much like how RESTful would do it. From the article in ReadWriteWeb, here’s how Dynamo works:

  • Physical nodes are thought of as identical and organized into a ring.
  • Virtual nodes are created by the system and mapped onto physical nodes, so that hardware can be swapped for maintenance and failure.
  • The partitioning algorithm is one of the most complicated pieces of the system, it specifies which nodes will store a given object.
  • The partitioning mechanism automatically scales as nodes enter and leave the system.
  • Every object is asynchronously replicated to N nodes.
  • The updates to the system occur asynchronously and may result in multiple copies of the object in the system with slightly different states.
  • The discrepancies in the system are reconciled after a period of time, ensuring eventual consistency.
  • Any node in the system can be issued a put or get request for any key

The Dynamo architecture addresses the CAP Theorem well. It is highly available, where nodes, either physical or virtual,  can be easily swapped without affected the storage services. It is also high performance, nodes (again physical or virtual) can be added to boost the performance. The high performance and highly available components addresses the “A” piece of CAP.

Its distributed nature also allows it to scale to billions and billions of data objects and hence meets the “P” requirement of CAP. The Partitioning Tolerance is definitely there.

However, as stated by CAP Theorem, you can’t have all 3 happening at the same time. Therefore, the “C” or Consistency piece of CAP has to be compromised. That is why Dynamo has been labeled an “eventually consistency” storage system.

As data is stored into ADSS, the changes of the data is propogated and will be asynchronously replicated to other nodes in the system, eventually making all the data objects and its value consistent. However, given the speed of things in cyberspace and the nature of most Cloud Computing services, the consistency piece could be difficult to accomplish and that is OK because in most of the transactions that are distributed, inconsistency is acceptable.

So that’s a bit about the Amazon Dynamo. Alas, we may never get our grubby hands on this piece of cool data storage and management technology, but knowing that Dynamo is powering AWS and its business is an eye-opener for us into the realm of a new technology evolution.

Is there IOPS for Cloud Storage? – Nasuni style

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I was in Singapore last week attending the Cloud Infrastructure Services course.

In the class, one of the foundation components of Cloud Computing is of course, storage. As the students and the instructor talked about Storage, one very interesting argument surfaced. It revolved around the storage, if it was offered on the cloud. A lot of people assumed that Cloud Storage would be for their databases, and their virtual machines, which of course, is true when the communication between the applications, virtual machines and databases are in the local area network of the Cloud Service Provider (CSP).

However, if the storage is offered through the cloud to applications that are sitting on-premise in the customer’s server room, then we have to think twice of how we perceive Cloud Storage. In this aspect, the Cloud Storage offered by the CSP is a Infrastructure-as-a-Service (IaaS), where the key service is Storage. We have to differentiate that this Storage functions as a data container, and usually not for I/O performance reasons.

Though this concept probably will be easily understood by storage professionals like us, this can cause a bit confusion for someone new to the concept of Cloud Computing and Cloud Storage. This confusion, unfortunately, is caused by many of us who are vendors or solution providers, or even publications and magazines. We are responsible to disseminate correct information to customers, but due to our lack of knowledge and experience in this extremely new market of Cloud Storage, we have created the FUDs (Fear, Uncertainty and Doubt) and hype.

Therefore, it is the duty of this blogger to clear the vapourware, and hopefully pass on the right information to accelerate  the adoption of Cloud Storage in the near future. At this moment, given the various factors such as network costs, high network latency and lack of key network technologies similar to LAN in Cloud Computing, Cloud Storage is, most of the time, for data storage containership and archiving only. And there are no IOPS or any performance related statistics related to Cloud Storage. If any engineer or vendor tells you that they have the fastest Cloud Storage in the industry, do me a favour. Give him/her a knock on the head for me!

Of course, as technologies evolve, this could change in the near future. For now, Cloud Storage is a container, NOT a high performance storage in the cloud. It is usually not meant for transactional data. There are many vendors in the Cloud Storage space from real CSPs to storage companies offering re-packaged storage boxes that are “cloud-ready”. A good example of a CSP offering Cloud Storage is Amazon S3 (Simple Storage Service). And storage vendors such as EMC and HDS are repackaging and rebranding their storage technologies as object storage, ready for the cloud. EMC Atmos is really a repackaged and rebranded Centera, with some slight modifications, while HDS , using their Archiving solution, has HCP (aka HCAP). There’s nothing wrong with what EMC and HDS have done, but before the overhyping of the world of Cloud Computing, these platforms were meant for immutable data archiving reasons. Just thought you should know.

One particular company that captured my imagination and addresses the storage performance portion is Nasuni. Of course, they are quite inventive with the Cloud Storage Gateway approach. Nasuni comes up with a Cloud Storage Gateway filer appliance, which can be either a physical 1U server or as a VMware or Hyper-V virtual appliance sitting on-premise at the customer’s site.

The key to this is “on-premise”, which allows access to data much faster because they are locally-cached in the Nasuni filer appliance itself. This Nasuni filer piece addresses the Cloud Storage “performance” piece but Nasuni do not claim any performance statistics with such implementation. The clever bit is that this addresses data or files that are transactional in nature, i.e. NFS or CIFS, to serve data or files “locally”. (I wonder if Nasuni filer has iSCSI as well. Hmmmm….)

In the Nasuni architecture, they “break up” their “Cloud Storage” into 2 pieces. Piece #1 sits on-premise, at the customer site, and acts as a bridge to the Piece #2, that is sitting in a Cloud Storage. From a simplified view, have a look at the diagram below:

 

 

Piece #1 is the component that handles some of the transactional traffic related to files. In a more technical diagram below, you can see that the Nasuni filer addresses the file sharing portion, using the local disks on the filer appliance as a local caching mechanism.

 

Furthermore, older file pieces are whiffed away to the any Cloud Storage using the Cloud Connector interface, hence giving the customer a sense that their storage capacity needs can be limitless if they want to (for a fee, of course). At the same time, the Nasuni filer support thin provisioning and snapshots. How cool is that!

The Cloud Storage piece (Piece #2) is used for the data container and archiving reasons. This component can be sitting and hosted at Amazon S3, Microsoft Azure, Rackspace Cloud Files, Nirvanix Storage Delivery Network and Iron Mountain Archive Services Platform.

The data communication and transfer between the Nasuni filer is secure, encrypted, deduplication and compressed, giving it the efficiency and security that most customers would be concerned about. The diagram below explains the dat communication and data transfer bit.

 

In this manner, the Nasuni filer can replace traditional NAS platforms and can potentially provide a much lower total cost of ownership (TCO) in the long run. Nasuni does not pretend to be a NAS replacement. To me, this concept is very inventive and could potentially change the way we perceive file sharing and file server, obscuring and blurring concept of NAS.

Again, I would like to reiterate that Nasuni does not attempt to say their solution is a NAS or a performance-based Cloud Storage but what they have cleverly packaged seems to be appealing to customers. Their customer base has grown 78% in Q2 of 2011. It’s just too bad they are not here in Malaysia or this part of the world (yet).

IOPS in Cloud Storage? Not yet.