The leapfrog game in Asia with HPC

Brunei, a country rich in oil and gas, is facing a crisis. Their oil & gas reserves are rapidly running dry and expected to be depleted within 2 decades. Their deep dependency on oil and gas, once the boon of their economy, is now the bane of their future.

Since 2000, I have been in and out of Brunei and got involved in several engagements there. It is a wonderful and peaceful country with friendly people, always welcoming visitors with open hearts. The country has prospered for decades, with its vast oil riches but in the past few years, the oil prices have been curbed. The profits of oil and gas no longer justify the costs of exploration and production.

2 years ago, I started pitching a new economy generator for the IT partners in Brunei. One that I believe will give a country like Brunei the ability to leapfrog their neighbours in South East Asia, which is to start build a High Performance Computing (HPC)-as-a-Service (HPC-as-a-Service) type of business.

Why HPC? Why do I think HPC will give a developing country like Brunei super powers in the digital economy?

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Cohesity SpanFS – a foundational shift

[Preamble: I was a delegate of Storage Field Day 15 from Mar 7-9, 2018. My expenses, travel and accommodation were paid for by GestaltIT, the organizer and I was not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

Cohesity SpanFS impressed me. Their filesystem was designed from ground up to meet the demands of the voluminous cloud-scale data, and yes, the sheer magnitude of data everywhere needs to be managed.

We all know that primary data is always the more important piece of data landscape but there is a growing need to address the secondary data segment as well.

Like a floating iceberg, the piece that is sticking out is the more important primary data but the larger piece beneath the surface of the water, which is the secondary data, is becoming more valuable. Applications such as file shares, archiving, backup, test and development, and analytics and insights are maturing as the foundational data management frameworks and fast becoming the bedrock of businesses.

The ability of businesses to bounce back after a disaster; the relentless testing of large data sets to develop new competitive advantage for businesses; the affirmations and the insights of analyzing data to reduce risks in decision making; all these are the powerful back engine applicability that thrust businesses forward. Even the ability to search for the right information in a sea of data for regulatory and compliance reasons is part of the organization’s data management application.

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Storage dinosaurs evolving too

[Preamble: I am a delegate of Storage Field Day 15 from Mar 7-9, 2018. My expenses, travel and accommodation are paid for by GestaltIT, the organizer and I am not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

I have been called a dinosaur. We storage networking professionals and storage technologists have been called dinosaurs. It wasn’t offensive or anything like that and I knew it was coming because the writing was on the wall, … or is it?

The cloud and the breakneck pace of all the technologies that came along have made us, the storage networking professionals, look like relics. The storage guys have been pigeonholed into a sunset segment of the IT industry. SAN and NAS, according to the non-practitioners, were no longer relevant. And cloud has clout (pun intended) us out of the park.

I don’t see us that way. I see that the Storage Dinosaurs are evolving as well, and our storage foundational knowledge and experience are more relevant that ever. And the greatest assets that we, the storage networking professionals, have is our deep understanding of data.

A little over a year ago, I changed the term Storage in my universe to Data Services Platform, and here was the blog I wrote. I blogged again just before the year 2018 began.

 

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My dilemma of stateful storage marriage

I should be a love match maker.

I have been spending much hours in the past few months, thinking of stateful data in stateful storage containers and how they would consummate with distributed applications containers and functions-as-a-service (aka serverless, aka Lambda). It still hasn’t made much sense, and I have not solved this problem yet. Although there were bits and pieces that coming together and the jigsaw looked well enough to give a cackled reply, what I have now is still not good enough for me. I am still searching for answers, better than the ones I have now.

The CAP theorem is in center of my mind. Distributed data, distributed states of data are on my mind. And by the looks of things, the computing world is heading towards containers and serverless computing too. Both distributed applications containers and serverless computing make a lot of sense. If we were to engage a whole new world of fog computing, edge computing, IoT, autonomous systems, AI, and other real-time computing, I would say that the future belongs to decentralization. Cloud Computing and having edge systems and devices getting back to the cloud for data is too slow. The latency of micro- or even nano-seconds is just not good enough. If we rely on the present methods to access the most relevant data, we are too late.

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Of Object Storage, Filesystems and Multi-Cloud

Data storage silos everywhere. The early clarion call was to eliminate IT data storage silos by moving to the cloud. Fast forward to the present. Data storage silos are still everywhere, but this time, they are in the clouds. I blogged about this.

Object Storage was all the rage when it first started. AWS, with its S3 (Simple Storage Service) offering, started the cloud storage frenzy. Highly available, globally distributed, simple to access, and fitted superbly into the entire AWS ecosystem. Quickly, a smorgasbord of S3-compatible, S3-like object-based storage emerged. OpenStack Swift, HDS HCP, EMC Atmos, Cleversafe (which became IBM SpectrumScale), Inktank Ceph (which became RedHat Ceph), Bycast (acquired by NetApp to be StorageGrid), Quantum Lattus, Amplidata, and many more. For a period of a few years prior, it looked to me that the popularity of object storage with an S3 compatible front has overtaken distributed file systems.

What’s not to like? Object storage are distributed, they are metadata rich (at a certain structural level), they are immutable (hence secure from a certain point of view), and some even claim self-healing (depending on data protection policies). But one thing that object storage rarely touted dominance was high performance I/O. There were some cases, but they were either fronted by a file system (eg. NFSv4.1 with pNFS extensions), or using some host-based, SAN-client agent (eg. StorNext or Intel Lustre). Object-based storage, in its native form, has not been positioned as high performance I/O storage.

A few weeks ago, I read an article from Storage Soup, Dave Raffo. When I read it, it felt oxymoronic. SwiftStack was just nominated as a visionary in the Gartner Magic Quadrant for Distributed File Systems and Object Storage. But according to Dave’s article, Swiftstack did not want to be “associated” with object storage that much, even though Swiftstack’s technology underpinning was all object storage. Strange.

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The rise of RDMA

I have known of RDMA (Remote Direct Memory Access) for quite some time, but never in depth. But since my contract work ended last week, and I have some time off to do some personal development, I decided to look deeper into RDMA. Why RDMA?

In the past 1 year or so, RDMA has been appearing in my radar very frequently, and rightly so. The speedy development and adoption of NVMe (Non-Volatile Memory Express) have pushed All Flash Arrays into the next level. This pushes the I/O and the throughput performance bottlenecks away from the NVMe storage medium into the legacy world of SCSI.

Most network storage interfaces and protocols like SAS, SATA, iSCSI, Fibre Channel today still carry SCSI loads and would have to translate between NVMe and SCSI. NVMe-to-SCSI bridges have to be present to facilitate the translation.

In the slide below, shared at the Flash Memory Summit, there were numerous red boxes which laid out the SCSI connections and interfaces where SCSI-to-NVMe translation (and vice versa) would be required.

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The changing face of storage

No, we are not a storage company anymore. We are a data management company now.

I was reading a Forbes article interviewing NetApp’s CIO, Bill Miller. It was titled:

NetApp’s CIO Helps Drive Company’s Shift From Data Storage To Data Management

I was fairly surprised about the time it took for that mindset shift messaging from storage to data management. I am sure that NetApp has been doing that for years internally.

To me, the writing has been in the wall for years. But weak perception of storage, at least in this part of Asia, still lingers as that clunky, behind the glassed walls and crufty closets, noisy box of full of hard disk drives lodged with snakes and snakes of orange, turquoise or white cables. 😉

The article may come as a revelation to some, but the world of storage has changed indefinitely. The blurring of the lines began when software defined storage, or even earlier in the form of storage virtualization, took form. I even came up with my definition a couple of years ago about the changing face of storage framework. Instead of calling it data management, I called the new storage framework,  the Data Services Platform.

So, this is my version of the storage technology platform of today. This is the Data Services Platform I have been touting to many for the last couple of years. It is not just storage technology anymore; it is much more than that.

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Let’s smoke the storage peace pipe

NVMe (Non-Volatile Memory Express) is upon us. And in the next 2-3 years, we will see a slew of new storage solutions and technology based on NVMe.

Just a few days ago, The Register released an article “Seventeen hopefuls fight for the NVMe Fabric array crown“, and it was timely. I, for one, cannot be more excited about the development and advancement of NVMe and the upcoming NVMeF (NVMe over Fabrics).

This is it. This is the one that will end the wars of DAS, NAS and SAN and unite the warring factions between server-based SAN (the sexy name differentiating old DAS and new DAS) and the networked storage of SAN and NAS. There will be PEACE.

Remember this?

nutanix-nosan-buntingNutanix popularized the “No SAN” movement which later led to VMware VSAN and other server-based SAN solutions, hyperconverged techs such as PernixData (acquired by Nutanix), DataCore, EMC ScaleIO and also operated in hyperscalers – the likes of Facebook and Google. The hyperconverged solutions and the server-based SAN lines blurred of storage but still, they are not the usual networked storage architectures of SAN and NAS. I blogged about this, mentioning about how the pendulum has swung back to favour DAS, or to put it more appropriately, server-based SAN. There was always a “Great Divide” between the 2 modes of storage architectures. Continue reading

Considerations of Hadoop in the Enterprise

I am guilty. I have not been tendering this blog for quite a while now, but it feels good to be back. What have I been doing? Since leaving NetApp 2 months or so ago, I have been active in the scenes again. This time I am more aligned towards data analytics and its burgeoning impact on the storage networking segment.

I was intrigued by an article posted by a friend of mine in Facebook. The article (circa 2013) was titled “Never, ever do this to Hadoop”. It described the author’s gripe with the SAN bigots. I have encountered storage professionals who throw in the SAN solution every time, because that was all they know. NAS, to them, was like that old relative smelled of camphor oil and they avoid NAS like a plague. Similar DAS was frowned upon but how things have changed. The pendulum has swung back to DAS and new market segments such as VSANs and Hyper Converged platforms have been dominating the scene in the past 2 years. I highlighted this in my blog, “Praying to the Hypervisor God” almost 2 years ago.

I agree with the author, Andrew C. Oliver. The “locality” of resources is central to Hadoop’s performance.

Consider these 2 models:

moving-compute-storage

In the model on your left (Moving Data to Compute), the delivery process from Storage to Compute is HEAVY. That is because data has dependencies; data has gravity. However, if you consider the model on your right (Moving Compute to Data), delivering data processing to the storage layer is much lighter. Compute or data processing is transient, and the data in the compute layer is volatile. Once compute’s power is turned off, everything starts again from a clean slate, hence the volatile stage.

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