Thinking small to solve Big

[This article was posted in my LinkedIn at https://www.linkedin.com/pulse/thinking-small-solve-big-chin-fah-heoh/ on Sep 9th 2019]

The world’s economy has certainly turned. And organizations, especially the SMEs, are demanding more. There were times that many technology vendors and their tier 1 systems integrators could get away with plenty of high level hobnobbing, and showering the prospect with their marketing wow-factor. But those fancy, smancy days are drying up and SMEs now do a lot of research and demand a more elaborate and a more comprehensive technology solution to their requirements.

The SMEs have the same problems faced by the larger organizations. They want more data stored, protected and recoverable, and maximize the value of data. However, their risk factors are much higher than the larger enterprises, because a disruption or a simple breakdown could affect their business and operations far greater than larger organizations. In most situations, they have no safety net.

So, the past 3 odd years, I have learned that as a technology solution provider, as a systems integrator to SMEs, I have to be on-the-ball with their pains all the time. And I have to always remember that they do not have the deep pockets, especially when the economy in Malaysia has been soft for years.

That is why I have gravitated to technology solutions that matter to the SMEs and gentle to their pockets as well. Take for instance a small company called Itxotic I discovered earlier this year. Itxotic is a 100% Malaysian home-grown technology startup, focusing on customized industry intelligence, notably computer vision AI. Their prominent technology include defect detection in a manufacturing production line.

 

At the Enterprise level, it is easy for large technology providers like Hitachi or GE or Siemens to peddle similar high-tech solutions to SMEs requirements. But this would come with a price tag of hundreds of thousands of ringgit. SMEs will balk at such a large investment because the price tag is definitely something not comprehensible to the SME factories. That is why I gravitated to the small thinking of Itxotic, where their small, yet powerful technology solves big problems in the SMEs.

And this came about when more Industry 4.0 opportunities started to come into my radar. Similarly, I was also approached to look into a edge-network data analytics technology to be integrated into PLCs (programmable logic controllers). At present, the industry consultants who invited me, are peddling a foreign technology solution, and the technology costs RM13,000 per CPU core. In a typical 4-core processor IPC (industrial PC), that is a whopping RM52,000, minus the hardware and integration services. This can easily drive up the selling price of over RM100K, again, a price tag that will trigger a mini heart attack with the SMEs.

I am tasked by the industry consultants to design a more cost-friendly, aka cheaper solution and today, we are already building an alternative with Apache Kafka, its connectors and Grafana for visual reporting. And I think the cost to build this alternative technology will be probably 70-80% cheaper than the one they are reselling now. The “think small, solve Big” mantra is beginning to take hold, and I am excited about it.

In the “small” mantra, I mean to be intimate and humble with the end users. One lesson I have learned over the past years is, the SMEs count on their technology partners to be with them. They have no room for failure because a costly failure is likely to be devastating to their operations and business. Know the technology you are pitching well, so that the SMEs are confident that you can deliver, not some over-the-top high-level technology pitch. Look deep into the technology integration with their existing technology and operations, and carefully and meticulously craft and curate a well mapped plan for them. Commit to their journey to ensure their success.

I have often seen technology vendors and resellers leaving SMEs high and dry when it comes to something outside their scope, and this has been painful. That is why this isn’t a downgrade for me when I started working with the SMEs more often in the past 3 years, even though I have served the enterprise for more than 25 years. This invaluable lesson is an upgrade for me to serve my SME customers better.

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Storage Performance Considerations for AI Data Paths

The hype of Deep Learning (DL), Machine Learning (ML) and Artificial Intelligence (AI) has reached an unprecedented frenzy. Every infrastructure vendor from servers, to networking, to storage has a word to say or play about DL/ML/AI. This prompted me to explore this hyped ecosystem from a storage perspective, notably from a storage performance requirement point-of-view.

One question on my mind

There are plenty of questions on my mind. One stood out and that is related to storage performance requirements.

Reading and learning from one storage technology vendor to another, the context of everyone’s play against their competitors seems to be  “They are archaic, they are legacy. Our architecture is built from ground up, modern, NVMe-enabled“. And there are more juxtaposing, but you get the picture – “We are better, no doubt“.

Are the data patterns and behaviours of AI different? How do they affect the storage design as the data moves through the workflow, the data paths and the lifecycle of the AI ecosystem?

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Scaling new HPC with Composable Architecture

[Disclosure: I was invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. Tech Field Day Extra was an included activity as part of the Dell Technologies World. My expenses, travel, accommodation and conference fees were covered by Dell Technologies, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Deep Learning, Neural Networks, Machine Learning and subsequently Artificial Intelligence (AI) are the new generation of applications and workloads to the commercial HPC systems. Different from the traditional, more scientific and engineering HPC workloads, I have written about the new dawn of supercomputing and the attractive posture of commercial HPC.

Don’t be idle

From the business perspective, the investment of HPC systems is high most of the time, and justifying it to the executives and the investors is not easy. Therefore, it is critical to keep feeding the HPC systems and significantly minimize the idle times for compute, GPUs, network and storage.

However, almost all HPC systems today are inflexible. Once assigned to a project, the resources pretty much stay with the project, even when the workload processing of the project is idle and waiting. Of course, we have to bear in mind that not all resources are fully abstracted, virtualized and software-defined whereby you can carve out pieces of the hardware and deliver a percentage of that resource. Case in point is the CPU, where you cannot assign certain clock cycles of CPU to one project and another half to the other. The technology isn’t there yet. Certain resources like GPU is going down the path of Virtual GPU, and into the realm of resource disaggregation. Eventually, all resources of the HPC systems – CPU, memory, FPGA, GPU, PCIe channels, NVMe paths, IOPS, bandwidth, burst buffers etc – should be disaggregated and pooled for disparate applications and workloads based on demands of usage, time and performance.

Hence we are beginning to see the disaggregated HPC systems resources composed and built up the meet the diverse mix and needs of HPC applications and workloads. This is even more acute when a AI project might grow cold, but the training of AL/ML/DL workloads continues to stay hot

Liqid the early leader in Composable Architecture

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Connecting ideas and people with Dell Influencers

[Disclosure: I was invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. My expenses, travel, accommodation and conference fees were covered by Dell Technologies, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

I just got home from Vegas yesterday after attending my 2nd Dell Technologies World as one of the Dell Luminaries. The conference was definitely a bigger one than the one last year, with more than 15,000 attendees. And there was a frenzy of announcements, from Dell Technologies Cloud to new infrastructure solutions, and more. The big one for me, obviously was Azure VMware Solutions officiated by Microsoft CEO Satya Nadella and VMware CEO Pat Gelsinger, with Michael Dell bringing together the union. I blogged about Dell jumping into the cloud in a big way.

AI Tweetup

In the razzmatazz, the most memorable moments were one of the Tweetups organized by Dr. Konstanze Alex (Konnie) and her team, and Tech Field Day Extra.

Tweetup was alien to me. I didn’t know how the concept work and I did google tweetup before that. There were a few tweetups on the topics of data protection and 5G, but the one that stood out for me was the AI tweetup.

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Lift and Shift Begone!

I am excited. New technologies are bringing the data (and storage) closer to processing and compute than ever before. I believe the “Lift and Shift” way would be a thing of the past … soon.

Data is heavy

Moving data across the network is painful. Moving data across distributed networks is even more painful. To compile the recent first image of a black hole, an amount of 5PB or more had to shipped for central processing. If this was moved over a 10 Gigabit network, it would have taken weeks.

Furthermore, data has dependencies. Snapshots, clones, and other data relationships with applications and processes render data inert, weighing it down like an anchor of a ship.

When I first started in the industry more than 25 years ago, Direct Attached Storage (DAS) was the dominating storage platform. I had a bulky Sun MultiDisk Pack connected via Fast SCSI to my SPARCstation 2 (diagram below):

Then I was assigned as the implementation engineer for Hock Hua Bank (now defunct) retail banking project in their Sibu HQ in East Malaysia. It was the first Sun SPARCstorage 1000 (photo below), running a direct attached Fibre Channel 0.25 Gbps FCAL (Fibre Channel Arbitrated Loop). It was the cusp of the birth of SAN (Storage Area Network).

Photo from https://www.cca.org/dave/tech/sys5/

The proliferation of SAN over the next 2 decades pushed DAS into obscurity, until SAS (Serial Attached SCSI) came about. Added to the mix was the prominence of Cloud Storage. But on-premises storage and Cloud Storage didn’t always come together. There was always a valley between the 2, until the public clouds gained a stronger foothold in the minds of IT and businesses. Today, both on-premises storage and cloud storage are slowly cosying as one Data Singularity, thanks to vision and conceptualization of data fabrics. NetApp was an early proponent of the Data Fabric concept 4 years ago. Continue reading

The full force of Western Digital

[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

3 weeks after Storage Field Day 18, I was still trying to wrap my head around the 3-hour session we had with Western Digital. I was like a kid in a candy store for a while, because there were too much to chew and I couldn’t munch them all.

From “Silicon to System”

Not many storage companies in the world can claim that mantra – “From Silicon to Systems“. Western Digital is probably one of 3 companies (the other 2 being Intel and nVidia) I know of at present, which develops vertical innovation and integration, end to end, from components, to platforms and to systems.

For a long time, we have always known Western Digital to be a hard disk company. It owns HGST, SanDisk, providing the drives, the Flash and the Compact Flash for both the consumer and the enterprise markets. However, in recent years, through 2 eyebrow raising acquisitions, Western Digital was moving itself up the infrastructure stack. In 2015, it acquired Amplidata. 2 years later, it acquired Tegile Systems. At that time, I was wondering why a hard disk manufacturer was buying storage technology companies that were not its usual bread and butter business.

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VAST Data must be something special

[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Vast Data coming out bash!

The delegates of Storage Field Days were always the lucky bunch. We have witnessed several storage technology companies coming out of stealth at these Tech Field Days. The recent ones in memory for me were Excelero and Hammerspace. But to have one where the venerable storage doyen, Mr. Howard Marks, Vast Data new tech evangelist, to introduce the deep dive of Vast Data technology was something special.

For those who knew Howard, he is fiercely independent, very storage technology smart, opinionated and not easily impressed. As a storage technology connoisseur myself, I believe Howard must have seen something special in Vast Data. They must be doing something extremely unique and impressive that someone like Howard could not resist, and made him jump to the vendor side. This sets the tone of my blog.

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From the past to the future

2019 beckons. The year 2018 is coming to a close and I look upon what I blogged in the past years to reflect what is the future.

The evolution of the Data Services Platform

Late 2017, I blogged about the Data Services Platform. Storage is no longer the storage infrastructure we know but has evolved to a platform where a plethora of data services are served. The changing face of storage is continually evolving as the IT industry changes. I take this opportunity to reflect what I wrote since I started blogging years ago, and look at the articles that are shaping up the landscape today and also some duds.

Some good ones …

One of the most memorable ones is about memory cloud. I wrote the article when Dell acquired a small company by the name of RNA Networks. I vividly recalled what was going through my mind when I wrote the blog. With the SAN, NAS and DAS, and even FAN (File Area Network) happening during that period, the first thing was the System Area Network, the original objective Infiniband and RDMA. I believed the final pool of where storage will be is the memory, hence I called it the “The Last Bastion – Memory“. RNA’s technology became part of Dell Fluid Architecture.

True enough, the present technology of Storage Class Memory and SNIA’s NVDIMM are along the memory cloud I espoused years ago.

What about Fibre Channel over Ethernet (FCoE)? It wasn’t a compelling enough technology for me when it came into the game. Reduced port and cable counts, and reduced power consumption were what the FCoE folks were pitching, but the cost of putting in the FC switches, the HBAs were just too great as an investment. In the end, we could see the cracks of the FCoE story, and I wrote the pre-mature eulogy of FCoE in my 2012 blog. I got some unsavoury comments writing that blog back then, but fast forward to the present, FCoE isn’t a force anymore.

Weeks ago, Amazon Web Services (AWS) just became a hybrid cloud service provider/vendor with the Outposts announcement. It didn’t surprise me but it may have shook the traditional systems integrators. I took the stance 2 years ago when AWS partnered with VMware and juxtaposed it to the philosophical quote in the 1993 Jurassic Park movie – “Life will not be contained, … Life finds a way“.

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Disaggregation or hyperconvergence?

[Preamble: I have been invited by  GestaltIT as a delegate to their TechFieldDay from Oct 17-19, 2018 in the Silicon Valley USA. My expenses, travel and accommodation are covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

There is an argument about NetApp‘s HCI (hyperconverged infrastructure). It is not really a hyperconverged product at all, according to one school of thought. Maybe NetApp is just riding on the hyperconvergence marketing coat tails, and just wanted to be associated to the HCI hot streak. In the same spectrum of argument, Datrium decided to call their technology open convergence, clearly trying not to be related to hyperconvergence.

Hyperconvergence has been enjoying a period of renaissance for a few years now. Leaders like Nutanix, VMware vSAN, Cisco Hyperflex and HPE Simplivity have been dominating the scene, and touting great IT benefits and eliminating IT efficiencies. But in these technologies, performance and capacity are tightly intertwined. That means that in each of the individual hyperconverged nodes, typically starting with a trio of nodes, the processing power and the storage capacity comes together. You have to accept both resources as a node. If you want more processing power, you get the additional storage capacity that comes with that node. If you want more storage capacity, you get more processing power whether you like it or not. This means, you get underutilized resources over time, and definitely not rightsized for the job.

And here in Malaysia, we have seen vendors throw in hyperconverged infrastructure solutions for every single requirement. That was why I wrote a piece about some zealots of hyperconverged solutions 3+ years ago. When you think you have a magical hammer, every problem is a nail. 😉

In my radar, NetApp and Datrium are the only 2 vendors that offer separate nodes for compute processing and storage capacity and still fall within the hyperconverged space. This approach obviously benefits the IT planners and the IT architects, and the customers too because they get what they want for their business. However, the disaggregation of compute processing and storage leads to the argument of whether these 2 companies belong to the hyperconverged infrastructure category.

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