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

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

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

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

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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Deploying a MinIO SNMD Object Storage Server in TrueNAS SCALE

[ 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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Making Immutability the key factor in a Resilient Data Protection strategy

We often hear “Cyber Resilience” word thrown around these days. Every backup vendor has a cybersecurity play nowadays. Many have morphed into cyber resilience warrior vendors, and there is a great amount of validation in terms of Cyber Resilience in a data protection world. Don’t believe me?

Check out this Tech Field Day podcast video from a month ago, where my friends, Tom Hollingsworth and Max Mortillaro discussed the topic meticulously with Krista Macomber, who has just become the Research Director for Cybersecurity at The Futurum Group (Congrats, Krista!).

Cyber Resilience, as well articulated in the video, is not old wine in a new bottle. The data protection landscape has changed significantly since the emergence of cyber threats and ransomware that it warrants the coining of the Cyber Resilience terminology.

But I want to talk about one very important cog in the data protection strategy, of which cyber resilience is part of. That is Immutability, because it is super important to always consider immutable backups as part of that strategy.

It is no longer 3-2-1 anymore, Toto. 

When it comes to backup, I always start with 3-2-1 backup rule. 3 copies of the data; 2 different media; 1 offsite. This rule has been ingrained in me since the day I entered the industry over 3 decades ago. It is still the most important opening line for a data protection specialist or a solution architect. 3-2-1 is the table stakes.

Yet, over the years, the cybersecurity threat landscape has moved closer and closer to the data protection, backup and recovery realm. This is now a merged super-segment pangea called cyber resilience. With it, the conversation from the 3-2-1 backup rule in these last few years is now evolving into something like 3-2-1-1-0 backup rule, a modern take of the 3-2-1 backup rule. Let’s take a look at the 3-2-1-1-0 rule (simplified by me).

The 3-2-1-1-0 Backup rule (Credit: https://www.dataprise.com/services/disaster-recovery/baas/)

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

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

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

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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