Category Archives: Filesystems

AI and the Data Factory

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When I first heard of the word “AI Factory”, the world was blaring Jensen Huang‘s keynote at NVIDIA GTC24. I thought those were cool words, since he mentioned about the raw material of water going into the factory to produce electricity. The analogy was spot on for the AI we are building.

As I engage with many DDN partners and end users in the region, week in, week out, the “AI Factory” word keeps popping into conversations. Yet, many still do not know how to go about building this “AI Factory”. They only know they need to buy GPUs, lots of them. These companies’ AI ambitions are unabated. And IDC predicts that worldwide spending on AI will double by 2028, and yet, the ROI (returns on investment) remains elusive.

At the ground level, based on many conversations so far, the common theme is, the steps to begin building the AI Factory are ambiguous and fuzzy to most. I like to share my views from a data storage point of view. Hence, my take on the Data Factory for AI.

Are you AI-ready?

We have to have a plan but before we take the first step, we must look at where we are standing at the present moment. We know that to train AI, the proverbial step is, we need lots of data. Deep Learning (DL) works with Large Language Models (LLMs), and Generative AI (GenAI), needs tons of data.

If the company knows where they are, they will know which phase is next. So, in the AI Maturity Model (I simplified the diagram below), where is your company now? Are you AI-ready?

Simplified AI Maturity Model

Get the Data Strategy Right

In his interview with CRN, MinIO’s CEO AB Periasamy quoted “For generative AI, they realized that buying more GPUs without a coherent data strategy meant GPUs are going to idle out”. I was struck by his wisdom about having a coherent data strategy because that is absolutely true. This is my starting point. Having the Right Data Strategy.

In the AI world, from a data storage guy, data is the fuel. Data is the raw material that Jensen alluded to, if it was obvious. We have heard this anecdotal quote many times before, even before the AI phenomenon took over. AI is data-driven. Data is vital for the ROI of AI projects. And thus, we must look from the point of the data to make the AI Factory successful.

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Accelerated Data Paths of High Performance Storage is the Cornerstone of building AI

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It has been 2 months into my new role at DDN as a Solutions Architect. With many revolving doors around me, I have been trying to find the essence, the critical cog of the data infrastructure that supports the accelerated computing of the Nvidia GPU clusters. The more I read and engage, a pattern emerged. I found that cog in the supercharged data paths between the storage infrastructure systems and the GPU clusters. I will share more.

To set the context, let me start with a wonderful article I read in CIO.com back in July 2024. It was titled “Storage: The unsung hero of AI deployments“. It was music to my ears because as a long-time practitioner in the storage technology industry, it is time the storage industry gets its credit it deserves.

What is the data path?

To put it simply, a Data Path, from a storage context, is the communication route taken by the data bits between the compute system’s processing and program memory and the storage subsystem. The links and the established sessions can be within the system components such as the PCIe bus or external to the system through the shared networking infrastructure.

High speed accelerated data paths

In the world of accelerated computing such as AI and HPC, there are additional, more advanced technologies to create even faster delivery of the data bits. This is the accelerated data paths between the compute nodes and the storage subsystems. Following on, I share a few of these technologies that are lesser used in the enterprise storage segment.

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The All-Important Storage Appliance Mindset for HPC and AI projects

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I am strong believer of using the right tool to do the job right. I have said this before 2 years ago, in my blog “Stating the case for a Storage Appliance approach“. It was written when I was previously working for an open source storage company. And I am an advocate of the crafter versus assembler mindset, especially in the enterprise and high- performance storage technology segments.

I have joined DDN. Even with DDN that same mindset does not change a bit. I have been saying all along that the storage appliance model should always be the mindset for the businesses’ peace-of-mind.

My view of the storage appliance model began almost 25 years. I came into NAS systems world via Sun Microsystems®. Sun was famous for running NFS servers on general Sun Solaris servers. NFS services on Unix systems. Back then, I remember arguing with one of the Sun distributors about the tenets of running NFS over 100Mbit/sec Ethernet on Sun servers. I was drinking Sun’s Kool-Aid big time.

When I joined Network Appliance® (now NetApp®) in 2000, my worldview of putting software on general purpose servers changed. Network Appliance®, had one product family, the FAS700 (720, 740, 760) family. All NetApp® did was to serve NFS services in the beginning. They were the NAS filers and nothing else.

I was completed sold on the appliance way with NetApp®. Firstly, it was my very first time knowing such network storage services could be provisioned with an appliance concept. This was different from Sun. I was used to managing NFS exports on a Sun SPARCstation 20 to Unix clients in the network.

Secondly, my mindset began to shape that “you have to have the right tool to the job correctly and extremely well“. Well, the toaster toasts bread very well and nothing else. And the fridge (an analogy used by Dave Hitz, I think) does what it does very well too. That is what the appliance does. You definitely cannot grill a steak with a bread toaster, just like you can’t run an excellent, ultra-high performance storage services to serve the demanding AI and HPC applications on a general server platform. You have to have a storage appliance solution for High-Speed Storage.

That little Network Appliance® toaster award given out to exemplary employees stood vividly in my mind. The NetApp® tagline back then was “Fast, Simple, Reliable”. That solidifies my mindset for the high-speed storage in AI and HPC projects in present times.

DDN AI400X2 Turbo Appliance

Costs Benefits and Risks

I like to think about what the end users are thinking about. There are investments costs involved, and along with it, risks to the investments as well as their benefits. Let’s just simplify and lump them into Cost-Benefits-Risk analysis triangle. These variables come into play in the decision making of AI and HPC projects.

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Deploying a MinIO SNMD Object Storage Server in TrueNAS SCALE

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[ Preamble ] This deployment of MinIO SNMD (single node multi drive) object storage server on TrueNAS® SCALE 24.04 (codename “Dragonfish”) is experimental. I am just deploying this in my home lab for the fun of it. Do not deploy in any production environment.

I have been contemplating this for quite a while. Which MinIO deployment mode on TrueNAS® SCALE should I work on? For one, there are 3 modes – Standalone, SNMD (Single Node Multi Drives) and MNMD (Multi Node Multi Drives). Of course, the ideal lab experiment is MNMD deployment, the MinIO cluster, and I am still experimenting this on my meagre lab resources.

In the end, I decided to implement SNMD since this is, most likely, deployed on top of a TrueNAS® SCALE storage appliance instead an x-86 bare-metal or in a Kubernetes cluster on Linux systems. Incidentally, the concept of MNMD on top of TrueNAS® SCALE is “Kubernetes cluster”-like albeit a different container platform. At the same time, if this is deployed in a TrueNAS® SCALE Enterprise, a dual-controller TrueNAS® storage appliance, it will take care of the “MinIO nodes” availability in its active-passive HA architecture of the appliance. Otherwise, it can be a full MinIO cluster spread and distributed across several TrueNAS storage appliances (minimum 4 nodes in a 2+2 erasure set) in an MNMD deployment scheme.

Ideally, the MNMD deployment should look like this:

MinIO distributed multi-node cluster architecture (credit: MinIO)

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Enhancing NAS client resiliency and performance with SMB Multichannel and NFS nconnect

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NAS (network attached storage) is obviously the file-level workhorse for shared resources in the network of any organization. SMB (server message block) for Windows environments and NFS (network file system) for Linux platforms are the 2 most prominent protocols that rule the NAS world. Of course we have SMB implementations in the form of Samba and others in non-Windows, Linux and NFS implementations in Windows as well.

As the versions of both network file sharing protocols iterated, present versions of SMB v3.x and NFS v4.x (NFS v3 on the supported Linux kernel version) on the client-side have evolved well. Both now have enhanced resiliency and performance improvement features. And there is an underlying similarity of both implementations. This blog looks at the client-side architectures of both.

One TCP connection

NAS is a client-server architecture. Over the network, NAS clients (SMB or NFS) access their corresponding NAS server(s) – SMB or NFS server(s) – through the TCP/IP network.

NAS client-server architecture (Credit: https://hypertecsp.com/en-CA/knowledge-base/nas-vs-san/)

One very important key starting point to note is the use of one TCP connection per NAS client to the NAS server relationship. For both SMB and NFS, there is just one TCP link between client and the server even if there are several SMB mapped shares or NFS mount points respectively on the clients.

For a long time, this one TCP connection is sufficient for the NAS traffic. But as the network file accesses grow, this connection becomes both a single point of failure as well as a performance bottleneck.

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FDT – Deduplication Reimagined in OpenZFS

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Deduplication in OpenZFS has been a bugbear for some years now. As data sets get larger, they have become even more difficult in using the present DeDuplication Table (DDT) method. Deduplication in OpenZFS is often derided as overwhelming and sluggish in performance.

Moreover, there is a common folklore passed on and on about allocating 5GB of RAM for every 1TB to dedupe in OpenZFS. I don’t know where this “sizing” came about. Probably derived from something Jeff Bonwick wrote back in the early days of ZFS. But there is some truth to this “rule of thumb”, commonly passed around in the TrueNAS® circles.

Nevertheless, given the exponential growth of data, and the advancement of processing power in modern day computer systems, the OpenZFS development community has decided to revamp the DDT method. Several prominent luminaries from iXsystems™, Klara Systems and the OpenZFS community have got together in mid-2023 to develop FDT or Fast Dedupe Table. And we got to see FDT announced to the world in the most recent OpenZFS Developer Summit in November 2023.

Fast Dedupe Table (FDT)

Fast Dedupe Table (FDT) is a log-based dedupe. In OpenZFS, all the write block I/Os that come into OpenZFS are coalesced into transaction groups (TXGs), hashed and checksummed, before they are committed to persistent media.

The new implementation in FDT is to put these incoming TXGs checksums and hashes into an append-only log structure in persistent storage, and also tracking the hashed changes in an AVL-tree residing in the memory. An AVL tree is a self-balancing binary search tree structure that is very efficient in searching, thus giving FDT the speed in initiating the deduplication lookups and updates.

OpenZFS Fast Dedupe Table (FDT) in a nutshell

The append-only log structure works hand-in-hand with the AVL tree to accept and stage (including intelligent sorting) the hash entries that are coming in after the TXGs writes. Then at a certain marker, that could be at a particular time-based trigger or a high-water mark, then the entries in the logs and AVL tree are flushed to the ZAP (ZFS Attribute Processor) where the actual full map of the OpenZFS blocks reside.

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Proxmox storage with TrueNAS iSCSI volumes

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A few weeks ago, I decided to wipe clean my entire lab setup running Proxmox 6.2. I wanted to connect the latest version of Proxmox VE 8.0-2 using iSCSI LUNs from the TrueNAS® system I have with me. I thought it would be fun to have the configuration steps and the process documented. This is my journal on how to provision a TrueNAS® CORE iSCSI LUN to Proxmox storage. This iSCSI volume in Proxmox is where the VMs will be installed into.

Here is a simplified network diagram of my setup but it will be expanded to a Proxmox cluster in the future with the shared storage.

Proxmox and TrueNAS network setup

Preparing the iSCSI LUN provisioning

The iSCSI LUN (logical unit number) is provisioned as a logical disk volume to the Proxmox node, where the initiator-target relationship and connection are established.

This part assumes that a zvol has been created from the zpool. At the same time, the IQN (iSCSI Qualified Name) should be known to the TrueNAS® storage as it establishes the connection between Proxmox (iSCSI initiator) and TrueNAS (iSCSI target).

The IQN for Proxmox can be found by viewing the content of the /etc/iscsi/initiatorname.iscsi within the Proxmox shell as shown in the screenshot below.

Where to find the Proxmox iSCSI IQN

The green box shows the IQN number of the Proxmox node that starts with iqn.year-month.com.domain:generated-hostname. This will be used during the iSCSI target portal configuration in the TrueNAS® webGUI.

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OpenZFS dRAID has risen!

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We await the 3rd iteration of TrueNAS® SCALE 23.10 codenamed Cobia. 23.10 means October 2023, and we are within weeks of its announcement.

One of the best features I have been waiting for is dRAID or distributed RAID. I have written about it dRAID a couple of years back. It was announced in 2021, in OpenZFS 2.1, but we have not seen an commercial implementation of dRAID … until iXsystems™ TrueNAS® SCALE 23.10. Why am I so excited?

I have followed the technology since Isaac Huang presented dRAID at the OpenZFS Summit in 2015. Through the years ahead, I have seen Isaac presenting dRAID at the summits, and with each iteration, dRAID got closer and closer to be developed into OpenZFS. It was not until 2021, in OpenZFS 2.1 when dRAID became part of filesystem. And now, dRAID is finally in the TrueNAS® SCALE offering.

Knowing RAID resilvering

RAID rebuilding or reconstruction is a painful and potentially risky process. In OpenZFS and ZFS speak, this process is called resilvering. In simple laymen terms, when a drive (or drives) failed in a parity-based RAID volume (eg. RAID-Z1 or RAID-Z2 vdev), the data which was previously in the failed drive is recreated in the newly integrated spare drive. The structural integrity of the RAID volume (and the storage pool) is preserved but the data that was lost is painstakingly remade through the mathematical algorithm of the parity function of the RAID volume.

When hard disk drives were small in capacity like 2TB or less, the RAID resilvering process was probably faster to complete, returning the parity RAID volume to a normal, online state. But today, drives are 22TB and higher, leaving the traditional RAID resilvering process to take days and even weeks. This leads the RAID volume vulnerable to another possible drive failure, weakening the integrity of the RAID volume. Even worse, most of modern day storage arrays have many disk drives, into the thousands even. And yes, solid state drives would probably be faster in the resilvering, but the same mechanics pretty much apply in OpenZFS.

At the same time, the spare drives are assigned physically and designated to the OpenZFS storage pool, and are not part of the vdev until the resilvering process kicks in.

Yes, this is pretty much a physical process that takes time, computing resources and patience. Note the operative word of “physical” here.

dRAID resilvering

dRAID speeds up the RAID resilvering process several folds, returning the RAID volume (or vdev) much faster than traditional OpenZFS RAID resilvering process. It uses a logical (as opposed to physical) RAID layout concept and uses “logical spare drives”. Thus, there will be many spares “blocks” distributed across the entire dRAID zpool, as shown in the diagram below.

Traditional RAID vdev vs dRAID vdev

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Understanding security practices in File Synchronization

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Ho hum. Another day, and another data leak. What else is new?

The latest hullabaloo in my radar was from one of Malaysia’s reverent universities, UiTM, which reported a data leak of 11,891 student applicants’ private details including MyKad (national identity card) numbers of each individual. Reading from the news article, one can deduced that the unsecured link mentioned was probably from a cloud storage service, i.e. file synchronization software such as OneDrive, Google Drive, Dropbox, etc. Those files that can be easily shared via an HTTP/S URL link. Ah, convenience over the data security best practices. 

Cloud File Sync software

It irks me when data security practices are poorly practised. And it is likely that there is ignorance of data security practices in the first place.

It also irks me when many end users everywhere I have encountered tell me their file synchronization software is backup. That is just a very poor excuse of a data protection strategy, if any, especially in enterprise and cloud environments. Convenience, set-and-forget mentality. Out of sight. Out of mind. Right? 

Convenience is not data security. File Sync is NOT Backup

Many users are used to the convenience of file synchronization. The proliferation of cloud storage services with free Gigabytes here and there have created an IT segment based on BYOD, which transformed into EFSS, and now CCP. The buzzword salad involves the Bring-Your-Own-Device, which evolved into Enterprise-File-Sync-&-Share, and in these later years, Content-Collaboration-Platform.

All these are fine and good. The data industry is growing up, and many are leveraging the power of file synchronization technologies, be it on on-premises and from cloud storage services. Organizations, large and small, are able to use these file synchronization platforms to enhance their businesses and digitally transforming their operational efficiencies and practices. But what is sorely missing in embracing the convenience and simplicity is the much ignored cybersecurity housekeeping practices that should be keeping our files and data safe.

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TrueNAS SCALE Clustered SMB to the fore

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iXsystems™ released second iteration of TrueNAS® SCALE software just over a week ago. It is known as version 22.02.2 or Anglefish.2, with the most notable upgrades to HA (High Availability) for SCALE and Clustered SMB capabilities. This is the perfect excuse for me to learn about Clustered SMB and share what I have learned.

TrueNAS SCALEFor the

For the uninformed, Clustered SMB brings highly available SMB file sharing services to mission critical environments. More importantly, Clustered SMB is high availability in a scale-out clustered architecture.

My view beyond HA SMB

I am not familiar with Clustered SMB in a NAS (Network Attached Storage). The world I am more familiar with is either having CIFS/SMB file services on a dual controller storage appliance or running Windows File Sharing on an Microsoft® Clustered Service (MSCS). Typically in these 2 types of HA SMB services, the scale up architecture require a shared access to a consolidated storage volume. Behind the scenes, there are many mechanisms at play to ensure that one, and only one, storage controller or HA host can have write access capabilities at one time. The most common mechanism is the SCSI-3 Persistent Reservation or sometimes known as SCSI fencing, using the SPC-3 (SCSI Primary Command) primitives. The whole objective is to prevent 2 nodes or hosts to writing to the shared storage volume at the same time and other issues like split-brain.

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