Storage IO straight to GPU

The parallel processing power of the GPU (Graphics Processing Unit) cannot be denied. One year ago, nVidia® overtook Intel® in market capitalization. And today, they have doubled their market cap lead over Intel®,  [as of July 2, 2021] USD$510.53 billion vs USD$229.19 billion.

Thus it is not surprising that storage architectures are changing from the CPU-centric paradigm to take advantage of the burgeoning prowess of the GPU. And 2 announcements in the storage news in recent weeks have caught my attention – Windows 11 DirectStorage API and nVidia® Magnum IO GPUDirect® Storage.

nVidia GPU

Exciting the gamers

The Windows DirectStorage API feature is only available in Windows 11. It was announced as part of the Xbox® Velocity Architecture last year to take advantage of the high I/O capability of modern day NVMe SSDs. DirectStorage-enabled applications and games have several technologies such as D3D Direct3D decompression/compression algorithm designed for the GPU, and SFS Sampler Feedback Streaming that uses the previous rendered frame results to decide which higher resolution texture frames to be loaded into memory of the GPU and rendered for the real-time gaming experience.

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First looks into Interplanetary File System

The cryptocurrency craze has elevated another strong candidate in recent months. Filecoin, is leading the voice of a decentralized Internet, the next generation Web 3.0. In this blog, I am not going to write much about the Filecoin frenzy but the underlying distributed file system that powers this phenomenon – The Interplanetary File System.

[ Note: This is still a very new area for me, and the rest of the content of this blog is still nascent and developing ]

Interplanetary File System

Tremulous Client-Server web architecture

The entire Internet architecture is almost client and server. Your clients like browsers, apps, connect to Web services served from a collection of servers. As Web 3.0 approaches (some say it is already here), the client-server model is no longer perceived as the Internet architecture of choice. Billions, and billions of users, applications, devices relying solely on a centralized service would lead to many impactful consequences, and the reasons for decentralization, away from the client-server architecture models of the Internet are cogent.

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Plotting the Crypto Coin Storage Farm

The recent craze of the Chia cryptocurrency got me excited. Mostly because it uses storage as the determinant for the Proof-of-Work consensus algorithm in a blockchain network. Yes, I am always about storage. 😉

I am not a Bitcoin miner nor am I a Chia coin farmer, and my knowledge and experience in both are very shallow. But I recently became interested in the 2 main activities of Chia – plotting and farming, because they both involved storage. I am writing this blog to find out more and document about my learning experience.

[ NB: This blog does not help you make money. It is just informational from a storage technology perspective. ]

Chia Cryptocurrency

Proof of Space and Time

Bitcoin is based on Proof-of-Work (PoW). In a nutshell, there is a complex mathematical puzzle to be solved. Bitcoin miners compete to solve this puzzle and the process uses high computational processing to solve it. Once solved, the miners are rewarded for their work.

Newer entrants like Filecoin and Chia coin (XCH) use an alternate method which is Proof-of-Space (PoS) to validate and verify the transactions. Instead of miners, Chia coin farmers have to prove to have a legitimate amount of disk and/or memory space to solve a mathematical puzzle, conceptually similar to the one in Bitcoin mining. In the beginning, this was great for folks who have unused disk space that can be “rented” out to store the crypto stuff (Note: I am not familiar with the terminology yet, and I did not want to use the word “crypto tokens” incorrectly). Storj was one of the early vendors that I remember in this space touting this method but I have not followed them for a while. Their business model might have changed.

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Before we say good bye to AFP

The Apple Filing Protocol (AFP) file sharing service in the MacOS Server is gone. The AFP file server capability was dropped in MacOS version 11, aka Big Sur back in December last year. The AFP client is the last remaining piece in MacOS and may see its days numbered as well as the world of file services evolved from the simple local networks and workgroup collaboration of the 80s and 90s, to something more complex and demanding. The AFP’s decline was also probably aided by the premium prices of Apple hardware, and many past users have switched to Windows for frugality and prudence reasons. SMB/CIFS is the network file sharing services for Windows, and AFP is not offered in Windows natively.

MacOS supports 3 of the file sharing protocols natively – AFP, NFS and SMB/CIFSas a client. Therefore, it has the capability to collaborate well in many media and content development environments, and sharing and exchanging files easily, assuming that the access control and permissions and files/folders ownerships are worked out properly. The large scale Apple-only network environment is no longer feasible and many studios that continue to use Macs for media and content development have only a handful of machines and users.

NAS vendors that continue to support AFP file server services are not that many too, or at least those who advertise their support for AFP. iXsystems™ TrueNAS® is one of the few. This blog shows the steps to setup the AFP file services for MacOS clients.

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Is Software Defined right for Storage?

George Herbert Leigh Mallory, mountaineer extraordinaire, was once asked “Why did you want to climb Mount Everest?“, in which he replied “Because it’s there“. That retort demonstrated the indomitable human spirit and probably exemplified best the relationship between the human being’s desire to conquer the physical limits of nature. The software of humanity versus the hardware of the planet Earth.

Juxtaposing, similarities can be said between software and hardware in computer systems, in storage technology per se. In it, there are a few schools of thoughts when it comes to delivering storage services with the notable ones being the storage appliance model and the software-defined storage model.

There are arguments, of course. Some are genuinely partisan but many a times, these arguments come in the form of the flavour of the moment. I have experienced in my past companies touting the storage appliance model very strongly in the beginning, and only to be switching to a “software company” chorus years after that. That was what I meant about the “flavour of the moment”.

Software Defined Storage

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A FreeNAS Compression Tale

David vs Goliath Credit: Miguel Robledo of https://www.artstation.com/miguel_robledo

David vs Goliath

It was an underdog tale worthy of the biblical book of Samuel. When I first caught wind of how FreeNAS™ compression prowess was going against NetApp® compression and deduplication in one use case, I had to find out more. And the results in this use case was quite impressive considering that FreeNAS™ (now known as TrueNAS® CORE) is the free, open source storage operating system and NetApp® Data ONTAP, is the industry leading, enterprise, “king of the hill” storage data management software.

Certainly a David vs Goliath story.

Compression in FreeNAS

Ah, Compression! That technology that is often hidden, hardly seen and often forgotten.

Compression is a feature within FreeNAS™ that seldom gets the attention. It works, and certainly is a mature form of data footprint reduction (DFR) technology, along with data deduplication. It is switched on by default, and is the setting when creating a dataset, as shown below:

Dataset creation with Compression (lz4) turned on

The default compression algorithm is lz4 which is fast but poor in compression ratio compared to gzip and bzip2. However, lz4 uses less CPU cycles to perform its compression and decompression processing, and thus the impact on FreeNAS™ and TrueNAS® is very low.

NetApp® ONTAP, if I am not wrong, uses lzopro as default – a commercial and optimized version of the open source LZO compression library. In addition, NetApp also has their data deduplication technology as well, something OpenZFS has to improve upon in the future.

The DFR report

This brings us to the use case at one of iXsystems™ customers in Taiwan. The data to be reduced are mostly log files at the end user, and the version of FreeNAS™ is 11.2u7. There are, of course, many factors that affect the data reduction ratio, but in this case of 4 scenarios,  the end user has been running this in production for over 2 months. The results:

FreeNAS vs NetApp Data Footprint Reduction

In 2 of the 4 scenarios, FreeNAS™ performed admirably with just the default lz4 compression alone, compared to NetApp® which was running both their inline compression and deduplication.

The intention to post this report is not to show that FreeNAS™ is better in every case. It won’t be, and there are superior data footprint reduction tech out there which can outperform it. But I would expect potential and existing end users to leverage on the compression capability of FreeNAS™ which is getting better all the time.

A better compression algorithm

Followers of OpenZFS are aware of the changing of times with OpenZFS version 2.0. One exciting update is the introduction of the zstd compression algorithm into OpenZFS late last year, and is already in TrueNAS® CORE and Enterprise version 12.x.

What is zstd? zstd is a fast compression algorithm that aims to be as efficient (or better) than gzip, but with better speed closer to lz4, relatively. For a long time, the gzip compression algorithm, from levels 1-9, has been serving very good compression ratio compared to many compression algorithms, lz4 included.

However, the efficiency came at a higher processing price and thus took a longer time. At the other end, lz4 is fast and lightweight, but its reduction ratio efficiency is very poor. zstd intends to be the in-between of gzip and lz4. In the latest results published by Facebook’s github page,

zstd performance benchmark against other compression algorithms

For comparison, zstd (level -1) performed very well against zlib, the data compression library in gzip. It was made known there are 22 levels of compression in zstd but I do not know how many levels are accepted in the OpenZFS development.

At the same time, compression takes advantage of multi-core processing, and actually can speed up disk I/O response because the original dataset to be processed is smaller after the compression reduction.

While TrueNAS® still defaults lz4 compression as of now, you can probably change the default compression with a command

# zfs set compression=zstd-6 pool/dataset

Your choice

TrueNAS® and FreeNAS™ support multiple compression algorithms. lz4, gzip and now zstd. That gives the administrator a choice to assign the right compression algorithm based on processing power, storage savings, and time to get the best out of the data stored in the datasets.

As far as the David vs Goliath tale goes, this real life use case was indeed a good one to share.

 

Layers in Storage – For better or worse

Storage arrays and storage services are built upon by layers and layers beneath its architecture. The physical components of hard disk drives and solid states are abstracted into RAID volumes, virtualized into other storage constructs before they are exposed as shares/exports, LUNs or objects to the network.

Everyone in the storage networking industry, is cognizant of the layers and it is the foundation of knowledge and experience. The public cloud storage services side is the same, albeit more opaque. Nevertheless, both have layers.

In the early 2000s, SNIA® Technical Council outlined a blueprint of the SNIA® Shared Storage Model, a framework describing layers and properties of a storage system and its services. It was similar to the OSI 7-layer model for networking. The framework helped many industry professionals and practitioners shaped their understanding and the development of knowledge in their respective fields. The layering scheme of the SNIA® Shared Storage Model is shown below:

SNIA Shared Storage Model – The layering scheme

Storage vendors layering scheme

While SNIA® storage layers were generic and open, each storage vendor had their own proprietary implementation of storage layers. Some of these architectures are simple, but some, I find a bit too complex and convoluted.

Here is an example of the layers of the Automated Volume Management (AVM) architecture of the EMC® Celerra®.

EMC Celerra AVM Layering Scheme

I would often scratch my head about AVM. Disks were grouped into RAID groups, which are LUNs (Logical Unit Numbers). Then they were defined as Celerra® dvols (disk volumes), and stripes of the dvols were consolidated into a storage pool.

From the pool, a piece of a storage capacity construct, called a slice volume, were combined with other slice volumes into a metavolume which eventually was presented as a file system to the network and their respective NAS clients. Explaining this took an effort because I was the IP Storage product manager for EMC® between 2007 – 2009. It was a far cry from the simplicity of NetApp® ONTAP 7 architecture of RAID groups and volumes, and the WAFL® (Write Anywhere File Layout) filesystem.

Another complicated layered framework I often gripe about is Ceph. Here is a look of how the layers of CephFS is constructed.

Ceph Storage Layered Framework

I work with the OpenZFS filesystem a lot. It is something I am rather familiar with, and the layered structure of the ZFS filesystem is essentially simpler.

Storage architecture mixology

Engineers are bizarre when they get too creative. They have a can do attitude that transcends the boundaries of practicality sometimes, and boggles many minds. This is what happens when they have their own mixology ideas.

Recently I spoke to two magnanimous persons who had the idea of providing Ceph iSCSI LUNs to the ZFS filesystem in order to use the simplicity of NAS file sharing capabilities in TrueNAS® CORE. From their own words, Ceph NAS capabilities sucked. I had to draw their whole idea out in a Powerpoint and this is the architecture I got from the conversation.

There are 3 different storage subsystems here just to provide NAS. As if Ceph layers aren’t complicated enough, the iSCSI LUNs from Ceph are presented as Cinder volumes to the KVM hypervisor (or VMware® ESXi) through the Cinder driver. Cinder is the persistent storage volume subsystem of the Openstack® project. The Cinder volumes/hypervisor datastore are virtualized as vdisks to the respective VMs installed with TrueNAS® CORE and OpenZFS filesystem. From the TrueNAS® CORE, shares and exports are provisioned via the SMB and NFS protocols to Windows and Linux respectively.

It works! As I was told, it worked!

A.P.P.A.R.M.S.C. considerations

Continuing from the layered framework described above for NAS, other aspects beside the technical work have to be considered, even when it can work technically.

I often use a set of diligent data storage focal points when considering a good storage design and implementation. This is the A.P.P.A.R.M.S.C. Take for instance Protection as one of the points and snapshot is the technology to use.

Snapshots can be executed at the ZFS level on the TrueNAS® CORE subsystem. Snapshots can be trigged at the volume level in Openstack® subsystem and likewise, rbd snapshots at the Ceph subsystem. The question is, which snapshot at which storage subsystem is the most valuable to the operations and business? Do you run all 3 snapshots? How do you execute them in succession in a scheduled policy?

In terms of performance, can it truly maximize its potential? Can it churn out the best IOPS, and deliver at wire speed? What is the latency we can expect with so many layers from 3 different storage subsystems?

And supporting this said architecture would be a nightmare. Where do you even start the troubleshooting?

Those are just a few considerations and questions to think about when such a layered storage architecture along. IMHO, such a design was over-engineered. I was tempted to say “Just because you can, doesn’t mean you should

Elegance in Simplicity

Einstein (I think) quoted:

Einstein’s quote on simplicity and complexity

I am not saying that having too many layers is wrong. Having a heavily layered architecture works for many storage solutions out there, where they are often masked with a simple and intuitive UI. But in yours truly point of view, as a storage architecture enthusiast and connoisseur, there is beauty and elegance in simple designs.

The purpose here is to promote better understanding of the storage layers, and how they integrate and interact with each other to deliver the data services to the network. In the end, that is how most storage architectures are built.

 

Do we still need FAST (and its cohorts)?

In a recent conversation with an iXsystems™ reseller in Hong Kong, the topic of Storage Tiering was brought up. We went about our banter and I brought up the inter-array tiering and the intra-array tiering piece.

After that conversation, I started thinking a lot about intra-array tiering, where data blocks within the storage array were moved between fast and slow storage media. The general policy was simple. Find all the least frequently access blocks and move them from a fast tier like the SSD tier, to a slower tier like the spinning drives with different RPM speeds. And then promote the data blocks to the faster media when accessed frequently. Of course, there were other variables in the mix besides storage media and speeds.

My mind raced back 10 years or more to my first encounter with Compellent and 3PAR. Both were still independent companies then, and I had my first taste of intra-array tiering

The original Compellent and 3PAR logos

I couldn’t recall which encounter I had first, but I remembered the time of both events were close. I was at Impact Business Solutions in their office listening to their Compellent pitch. The Kuching boys (thank you Chyr and Winston!) were very passionate in evangelizing the Compellent Data Progression technology.

At about the same time, I was invited by PTC Singapore GM at the time, Ken Chua to grace their new Malaysian office and listen to their latest storage vendor partnership, 3PAR. I have known Ken through my NetApp® days, and he linked me up Nathan Boeger, 3PAR’s pre-sales consultant. 3PAR had their Adaptive Optimization (AO) disk tiering and Dynamic Optimization (DO) technology.

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Discovering OpenZFS Fusion Pool

Fusion Pool excites me, but unfortunately this new key feature of OpenZFS is hardly talked about. I would like to introduce the Fusion Pool feature as iXsystems™ expands the TrueNAS® Enterprise storage conversations.

I would not say that this technology is revolutionary. Other vendors already have the similar concept of Fusion Pool. The most notable (to me) is NetApp® Flash Pool, and I am sure other enterprise storage vendors have the same. But this is a big deal (for me) for an open source file system in OpenZFS.

What is Fusion Pool  (aka ZFS Allocation Classes)?

To understand Fusion Pool, we have to understand the basics of the ZFS zpool. A zpool is the aggregation (borrowing the NetApp® terminology) of vdevs (virtual devices), and vdevs are a collection of physical drives configured with the OpenZFS RAID levels (RAID-0, RAID-1, RAID-Z1, RAID-Z2, RAID-Z3 and a few nested RAID permutations). A zpool can start with one vdev, and new vdevs can be added on-the-fly, expanding the capacity of the zpool online.

There are several types of vdevs prior to Fusion Pool, and this is as of pre-TrueNAS® version 12.0. As shown below, these are the types of vdevs available to the zpool at present.

OpenZFS zpool and vdev types – Credit: Jim Salter and Arstechnica

Fusion Pool is a zpool that integrates with a new, special type of vdev, alongside other normal vdevs. This special vdev is designed to work with small data blocks between 4-16K, and is highly efficient in handling random reading and writing of these small blocks. This bodes well with the OpenZFS file system metadata blocks and other blocks of small files. And the random nature of the Read/Write I/Os works best with SSDs (can be read or write intensive SSDs).

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