Tag Archives: ROI

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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IT Data practices and policies still wanting

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There is an apt and honest editorial cartoon about Change.

From https://commons.wikimedia.org/wiki/File:Who-Wants-Change-Crowd-Change-Management-Yellow.png

I was a guest of Channel Asia Executive Roundtable last week. I joined several luminaries in South East Asia to discuss about the topic of “How Partners can bring value to the businesses to manage their remote workforce“.

Covid-19 decimated what we knew as work in general. The world had to pivot and now, 2+ years later, a hybrid workforce has emerged. The mixture of remote work, work-from-home (WFH), physical office and everywhere else has brought up a new mindset and new attitudes with both the employers and their staff alike. Without a doubt, the remote way of working is here to stay.

People won but did the process lose?

The knee jerk reactions when the lockdowns of Covid hit were to switch work to remote access to applications on premises or in the clouds. Many companies have already moved to the software-as-a-service (SaaS) way of working but not all have made the jump, just like not all the companies’ applications were SaaS based. Of course, the first thing these stranded companies do was to look for the technologies to solve this unforeseen disorder.

People Process Technology.
Picture from https://iconstruct.com/blog/people-process-technology/

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Komprise is a Winner

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[Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies to be presented at this event. The content of this blog is of my own opinions and views]

I, for one perhaps have seen far too many “file lifecycle and data management” software solutions that involved tiering, hierarchical storage management, ILM or whatever you call them these days. If I do a count, I would have managed or implemented at least 5 to 6 products, including a home grown one.

The whole thing is a very crowded market and I have seen many which have come and gone, and so when the opportunity to have a session with Komprise came at Storage Field Day 19, I did not carry a lot of enthusiasm.

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