Category Archives: Big Data

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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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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Nurturing Data Governance for Cybersecurity and AI

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Towards the middle of the 2000s, I started getting my exposure in Data Governance. This began as I was studying and practising to be certified as an Oracle Certified Professional (OCP) circa 2002-2003. My understanding of the value of data and databases in the storage world, now better known as data infrastructure, grew and expanded quickly. I never gotten my OCP certification because I ran out of money investing in the 5 required classes that included PL/SQL, DBA Admin I and II, and Performance Tuning. My son, Jeffrey was born in 2002, and money was tight.

The sentiment of data governance of most organizations I have engaged with at that time, and over the next course of almost 18 years or so, pre-Covid, the practice of data governance was to comply to some regulatory requirements. 

All that is changing. Early 2024, NIST released the second version of their Cybersecurity Framework (CSF). CSF 2.0 placed Data Governance in the center of the previous 5 pillars of CSF 1.1. The diagram below shows the difference between the versions.

High level change of Cybersecurity Framework 1.1 to 2.0.

Ripples like this in my data management radar are significant, noticeable and important to me. I blogged about it in my April 2024 blog “NIST CSF 2.0 brings Data Governance into the Light“.

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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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Preliminary Data Taxonomy at ingestion. An opportunity for Computational Storage

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Data governance has been on my mind a lot lately. With all the incessant talks and hype about Artificial Intelligence, the true value of AI comes from good data. Therefore, it is vital for any organization embarking on their AI journey to have good quality data. And the journey of the lifecycle of data in an organization starts at the point of ingestion, the data source of how data is either created, acquired to be presented up into the processing workflow and data pipelines for AI training and onwards to AI applications.

In biology, taxonomy is the scientific study and practice of naming, defining and classifying biological organisms based on shared characteristics.

And so, begins my argument of meshing these 3 topics together – data ingestion, data taxonomy and with Computational Storage. Here goes my storage punditry.

Data Taxonomy in post-injection 

I see that data, any data, has to arrive at a repository first before they are given meaning, context, specifications. These requirements are different from file permissions, ownerships, ctime and atime timestamps, the content of the ingested data stream are made to fit into the mould of the repository the data is written to. Metadata about the content of the data gives the data meaning, context and most importantly, value as it is used within the data lifecycle. However, the metadata tagging, and preparing the data in the ETL (extract load transform) or the ELT (extract load transform) process are only applied post-ingestion. This data preparation phase, in which data is enriched with content metadata, tagging, taxonomy and classification, is expensive, in term of resources, time and currency.

Elements of a modern event-driven architecture including data ingestion (Credit: Qlik)

Even in the burgeoning times of open table formats (Apache Iceberg, HUDI, Deltalake, et al), open big data file formats (Avro, Parquet) and open data formats (CSV, XML, JSON et.al), the format specifications with added context and meanings are added in and augmented post-injection.

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Data Trust and Data Responsibility. Where we should be at before responsible AI.

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Last week, there was a press release by Qlik™, informing of a sponsored TechTarget®‘s Enterprise Strategy Group (ESG) about the state of responsible AI practices across industries. The study highlighted critical gaps in the approach to responsible AI, ethical AI practices and AI regulatory compliances. From the study, Qlik™ emphasizes on having a solid data foundation. To get to that bedrock foundation, we must trust the data and we must be responsible for the kinds of data that built that foundation. Hence, Data Trust and Data Responsibility.

There is an AI boom right now. Last year alone, the AI machine and its hype added in USD$2.4 trillion market cap to US tech companies. 5 months into 2024, AI is still supernova hot. And many are very much fixated to the infallible fables and tales of AI’s pompous splendour. It is this blind faith that I see many users and vendors alike sidestepping the realities of AI in the present state as it is.

AI is not always responsible. Then it begs the question, “Are we really working with a responsible set of AI applications and ecosystems“?

Responsible AI. Are we there yet?

AI still hallucinates, unfortunately. The lack of transparency of AI applications coming to a conclusion and a recommended decision is not always known. What if you had a conversation with ChatGPT and it says that you are dead. Well, that was exactly what happened when Tom’s Guide writer, Tony Polanco, found out from ChatGPT that he passed away in September 2021.

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NIST CSF 2.0 brings Data Governance into the light

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In the past weekend, I watched a CNA Insider video delving into Data Theft in Malaysia. It is titled “Data Theft in Malaysia: How your personal information may be exploited | Cyber Scammed”.

You can watch the 45-minute video below.

Such dire news is nothing new. We Malaysians are numbed to those telemarketers calling and messaging to offer their credit card services, loans, health spa services. You name it; there is something to sell. Of course, these “services” are mostly innocuous, but in recent years, the forms of scams are risen up several notches and severity levels. The levels of sophistication, the impacts, and the damages (counting financial and human casualties) have rocketed exponentially. Along with the news, mainstream and others, the levels of awareness and interests in data, especially PII (personal identifiable information) in Malaysians, are at its highest yet.

Yet the data theft continues unabated. Cybersecurity Malaysia (CSM), just last week, reported a 1,192% jump of data theft cases in Malaysia in 2023. In an older news last year, cybersecurity firm Surf Shark ranked Malaysia as the 8th most breached country in Q3 of 2023.
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And great AI starts with good Data Management

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Processing data has become more expensive.

Somewhere, there is a misconception that data processing is cheap. That stems from the well-known pricings of the capacities of public cloud storage that are a fraction of cents per month. But data in storage has to be worked upon, and has to be built up and protected to increase its value. Data has to be processed, moved, shared, and used by applications. Data induce workloads. Nobody keeps data stored forever and never be used again. Nobody buys storage just for capacity alone.

We have a great saying in the industry. No matter, where the data moves, it will land in a storage. So, it is clear that data does not exist in ether. And yet, I often see how little attention and prudence and care, when it comes to data infrastructure and data management technologies, the very components that are foundational to great data.

Great data management for Great AI

AI is driving up costs in data processing

A few recent articles drew my focus into the cost of data processing.

Here is one posted by a friend on Facebook. It is titled “The world is running out of data to feed AI, experts warn.”

My first reaction was, “How can we run out of data“? We have so much data in the world today that the 175 zettabytes predicted by IDC when we reach 2025 might be grossly inaccurate. According to Exploding Topics, it is estimated that we create 328.77TB of data per day, 120 zettabytes per year. While I cannot vouch for the accuracy of the numbers, the numbers are humongous.

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