Category Archives: Scale-out architecture

Rethinking Storage OKRs for AI Data Infrastructure – Part 2

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[ Preamble: This analysis focuses on my own journey as I incorporate my experiences into this new market segment called AI Data Infrastructure. There are many elements of HPC (High Performance Computing) at play here. Even though things such as speeds and feeds, features and functions crowd many conversations, as many enterprise storage vendors do, these conversations, in my opinion, are secondary. There are more vital and important operational technology and technical elements that an organization has to consider prudently. They involve asking the hard questions beyond the marketing hype and fluff. I call these elements of consideration Storage Objectives and Key Results (OKRs) for AI Data Infrastructure.

I had to break this blog into 2 parts. It has become TL;DR-ish. This is Part 2 ]

This is a continuation from Part 1 of my blog last week. I spoke about the 4 key OKRs (Objectives and Key Results) we look at from the storage point-of-view with regards to AI data infrastructure. To recap, they are:

  • Reliability
  • Speed
  • Power Efficiency
  • Security

Power Efficiency

Patrick Kennedy of ServeTheHome (STH) fame, astutely explained the new generation of data center racks required by NVIDIA® and AMD® in his article “Here is how much Power we expect AMD® and NVIDIA® racks will need in 2027” 2 weeks ago. Today, the NVIDIA® GB200 NVL72 ORv3 rack design takes up 120kW per rack. That’s an insane amount of power consumption that can only go up in the next 2-3 years. That is why power efficiency must be an OKR metric to be deeply evaluated.

When you operate a GPU compute farm, whether it is 8 GPUs or 16,384 GPUs, keep operations tight is vital to ensure that maximum power efficiency is right up there with the rest of the operational OKRs. The element of power consumption becomes a cost factor in the data infrastructure design for AI.

2 very important units of measurements I would look into, and that have become valuable OKRs to achieve are Performance per Watt (Performance/Watt) and Performance per Rack Unit (Performance/RU).

Power Efficiency in Data Center is a Must.

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Rethinking Storage OKRs for AI Data Infrastructure – Part 1

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[ Preamble: This analysis focuses on my own journey as I incorporate my past experiences into this new market segment called AI Data Infrastructure, and gaining new ones.

There are many elements of HPC (High Performance Computing) at play here. Even though things such as speeds and feeds, features and functions crowd many conversations, as many enterprise storage vendors like to do, these conversations, in my opinion, are secondary. There are more vital and important operational technology and technical elements that an organization has to consider prudently, vis-a-vis to ROIs (returns of investments). They involve asking the hard questions beyond the marketing hype and fluff. I call these elements of consideration Storage Objectives and Key Results (OKRs) for AI Data Infrastructure.

I had to break this blog into 2 parts. It has become TL;DR-ish. This is Part 1 ]

I have just passed my 6-month anniversary with DDN. Coming into the High Performance Storage System (HPSS) market segment, with the strong focus on the distributed parallel filesystem of Lustre®, there was a high learning curve for me. I spend over 3 decades in Enterprise Storage, with some of the highest level of storage technologies there were in that market segment. And I have already developed my own approach to enterprise storage, based on the A.P.P.A.R.M.S.C.. That was already developed and honed from 25 years ago.

The rapid adoption of AI has created a technology paradigm shift. Artificial Intelligence (AI) came in and blurred many lines. It also has been evolving my thinking when it comes to storage for AI. There is also a paradigm shift in my thoughts, opinions and experiences as well.

AI has brought HPSS technologies like Lustre® in DDN EXAscaler platform , proven in the Supercomputing world, to a new realm – the AI Data Infrastructure market segment. On the other side, many enterprise storage vendors aspire to be a supplier to the AI Data Infrastructure opportunities as well. This convergence from the top storage performers for Supercomputing, in the likes of DDN, IBM® (through Storage Scale), HPE® (through Cray, which by-the-way often uses the open-source Lustre® edition in its storage portfolio), from the software-defined storage players in Weka IO, Vast Data, MinIO, and from the enterprise storage array vendors such as NetApp®, Pure Storage®, and Dell®.

[ Note that I take care not to name every storage vendor for AI because many either do OEMs or repacking and rebranding of SDS technology into their gear such as HPE® GreenLake for Files and Hitachi® IQ. You can Google to find out who the original vendors are for each respectively. There are others as well. ]

In these 3 simplified categories (HPSS, SDS, Enterprise Storage Array), I have begun to see a pattern of each calling its technology as an “AI Data Infrastructure”. At the same time, I am also developing a new set of storage conversations for the AI Data Infrastructure market segment, one that is based on OKRs (Objectives and Key Results) rather than just features, features and more features that many SDS and enterprise storage vendors like to tout. Here are a few thoughts that we should look for when end users are considering a high-speed storage solution for their AI journey.

AI Data Infrastructure

GPU is king

In the AI world, the GPU infrastructure is the deity at the altar. The utilization rate of the GPUs is kept at the highest to get the maximum compute infrastructure return-on-investment (ROI). Keeping the GPUs resolutely busy is a must. HPSS is very much part of that ecosystem.

These are a few OKRs I would consider the storage or data infrastructure for AI.

  • Reliability
  • Speed
  • Power Efficiency
  • Security

Let’s look at each one of them from the point of view of a storage practitioner like me.

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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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Disaggregation and Composability vital for AI/DL models to scale

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New generations of applications and workloads like AI/DL (Artificial Intelligence/Deep Learning), and HPC (High Performance Computing) are breaking the seams of entrenched storage infrastructure models and frameworks. We cannot continue to scale-up or scale-out the storage infrastructure to meet these inundating fluctuating I/O demands. It is time to look at another storage architecture type of infrastructure technology – Composable Infrastructure Architecture.

Infrastructure is changing. The previous staid infrastructure architecture parts of compute, network and storage have long been thrown of the window, precipitated by the rise of x86 server virtualization almost 20 years now. It triggered a tsunami of virtualizing everything, including storage virtualization, which eventually found a more current nomenclature – Software Defined Storage. Both storage virtualization and software defined storage (SDS) are similar and yet different and should be revered through different contexts and similar goals. This Tech Target article laid out both nicely.

As virtualization raged on, converged infrastructure (CI) which evolved into hyperconverged infrastructure (HCI) went fever pitch for a while. Companies like Maxta, Pivot3, Atlantis, are pretty much gone, with HPE® Simplivity and Cisco® Hyperflex occasionally blipped in my radar. In a market that matured very fast, HCI is now dominated by Nutanix™ and VMware®, with smaller Microsoft®, Dell EMC® following them.

From HCI, the attention of virtualization has shifted something more granular, more scalable in containerization. Despite a degree of complexity, containerization is taking agility and scalability to the next level. Kubernetes, Dockers are now mainstay nomenclature of infrastructure engineers and DevOps. So what is driving composable infrastructure? Have we reached the end of virtualization? Not really.

Evolution of infrastructure. Source: IDC

It is just that one part of the infrastructure landscape is changing. This new generation of AI/ML workloads are flipping the coin to the other side of virtualization. As we see the diagram above, IDC brought this mindset change to get us to Think Composability, the next phase of Infrastructure.

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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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Stating the case for a Storage Appliance approach

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I was in Indonesia last week to meet with iXsystems™‘ partner PT Maha Data Solusi. I had the wonderful opportunity to meet with many people there and one interesting and often-replayed question arose. Why aren’t iX doing software-defined-storage (SDS)? It was a very obvious and deliberate question.

After all, iX is already providing the free use of the open source TrueNAS® CORE software that runs on many x86 systems as an SDS solution and yet commercially, iX sell the TrueNAS® storage appliances.

This argument between a storage appliance model and a storage storage only model has been debated for more than a decade, and it does come into my conversations on and off. I finally want to address this here, with my own views and opinions. And I want to inform that I am open to both models, because as a storage consultant, both have their pros and cons, advantages and disadvantages. Up front I gravitate to the storage appliance model, and here’s why.

My story of the storage appliance begins …

Back in the 90s, most of my work was on Fibre Channel and NFS. iSCSI has not existed yet (iSCSI was ratified in 2003). It was almost exclusively on the Sun Microsystems® enterprise storage with Sun’s software resell of the Veritas® software suite that included the Sun Volume Manager (VxVM), Veritas® Filesystem (VxFS), Veritas® Replication (VxVR) and Veritas® Cluster Server (VCS). I didn’t do much Veritas® NetBackup (NBU) although I was trained at Veritas® in Boston in July 1997 (I remembered that 2 weeks’ trip fondly). It was just over 2 months after Veritas® acquired OpenVision. Backup Plus was the NetBackup.

Between 1998-1999, I spent a lot of time working Sun NFS servers. The prevalent networking speed at that time was 100Mbits/sec. And I remember having this argument with a Sun partner engineer by the name of Wong Teck Seng. Teck Seng was an inquisitive fella (still is) and he was raving about this purpose-built NFS server he knew about and he shared his experience with me. I detracted him, brushing aside his always-on tech orgasm, and did not find great things about a NAS storage appliance. Auspex™ was big then, and I knew of them.

I joined NetApp® as Malaysia’s employee #2. It was an odd few months working with a storage appliance but after a couple of months, I started to understand and appreciate the philosophy. The storage Appliance Model made sense to me, even through these days.

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I built a 6-node Gluster cluster with TrueNAS SCALE

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I haven’t had hands-on with Gluster for over a decade. My last blog about Gluster was in 2011, right after I did a proof-of-concept for the now defunct, Jaring, Malaysia’s first ISP (Internet Service Provider). But I followed Gluster’s development on and off, until I found out that Gluster was a feature in then upcoming TrueNAS® SCALE. That was almost 2 years ago, just before I accepted to offer to join iXsystems™, my present employer.

The eagerness to test drive Gluster (again) on TrueNAS® SCALE has always been there but I waited for SCALE to become GA. GA finally came on February 22, 2022. My plans for the test rig was laid out, and in the past few weeks, I have been diligently re-learning and putting up the scope to built a 6-node Gluster clustered storage with TrueNAS® SCALE VMs on Virtualbox®.

Gluster on OpenZFS with TrueNAS SCALE

Before we continue, I must warn that this is not pretty. I have limited computing resources in my homelab, but Gluster worked beautifully once I ironed out the inefficiencies. Secondly, this is not a performance test as well, for obvious reasons. So, this is the annals along with the trials and tribulations of my 6-node Gluster cluster test rig on TrueNAS® SCALE.

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