A Paean to NFS

It is certainly encouraging to see both NAS protocols, NFS and SMB, featured well in the latest VMware® vSAN 7 Update 1 release. The NFS v3 and v4.1 support was already in vSAN 7.0 when it was earlier announced as part of its Native File Services for vSAN. But some years ago, NFS was not always the primary storage protocol of choice. SAN protocols, Fibre Channel and iSCSI, were almost always designated to serve enterprise applications. At the client side, Windows became prominent, and the SMB/CIFS protocol dominated the landscape of the desktop. This further pushed NFS into the back closet.

NFS or Network File System has its naysayers. The venerable, but often maligned distributed network file protocol is 36 years today. In storage vendors such as NetApp®, VAST Data, Pure Storage FlashBlade, and Dell EMC Isilon, NFS is still positioned as the primary file protocol for manufacturing testers on the shop floor, EDA/eCAD applications, seismic and subsurface applications in Oil & Gas and many more. In another development, just like its presence in the vSAN Native Services,, NFS has also quietly embedded itself into many storage platforms to serve the data platform services within the respective framework itself.

And I have experienced NFS from the client side to the enterprise applications and more, and I take this opportunity to pay tribute.

NFS (Network File System) client server network

NFS (Network File System) client server network

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The prudence needed for storage technology companies

Blitzscaling has been on my mind a lot. Ever since I discovered that word a while back, it has returned time and time again to fill my thoughts. In the wake of COVID-19, and in the mire of this devastating pandemic, is blitzscaling still the right strategy for this generation of storage technology, hyperconverged, data management and cloud storage startups?

What the heck is Blitzscaling? 

For the uninformed, here’s a video of Reid Hoffman, co-founder of Linked and a member of the Paypal mafia, explaining Blitzscaling.

Blitzscaling is about hyper growing, scaling ultra fast and rocketing to escape velocity, at the expense of things like management efficiency, financial prudence, profits and others. While this blog focuses on storage companies, blitzscaling is probably most recognizable in the massive expansion of Uber (and contraction) a few years ago. In the US, the ride hailing war is between Uber and Lyft, but over here in South East Asia, just a few years back, it was between Uber and Grab. In China it was Uber and Didi.

From the storage angle, 2 segments exemplified the blitzscaling culture between 2015 and 2020.

  • All Flash Startups
  • Hyper Converged Infrastructure Startups

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Dell EMC Isilon is an Emmy winner!

[ 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 presented at this event. The content of this blog is of my own opinions and views ]

And the Emmy® goes to …

Yes, the Emmy® goes to Dell EMC Isilon! It was indeed a well deserved accolade and an honour!

Dell EMC Isilon had just won the Technology & Engineering Emmy® Awards a week before Storage Field Day 19, for their outstanding pioneering work on the NAS platform tiering technology of media and broadcasting content according to business value.

A lasting true clustered NAS

This is not a blog to praise Isilon but one that instill respect to a real true clustered, scale-out file system. I have known of OneFS for a long time, but never really took the opportunity to really put my hands on it since 2006 (there is a story). So here is a look at history …

Back in early to mid-2000, there was a lot of talks about large scale NAS. There were several players in the nascent scaling NAS market. NetApp was the filer king, with several competitors such as Polyserve, Ibrix, Spinnaker, Panasas and the young upstart Isilon. There were also Procom, BlueArc and NetApp’s predecessor Auspex. By the second half of the 2000 decade, the market consolidated and most of these NAS players were acquired.

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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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Whither HPC, HPE?

HPE is acquiring Cray Inc. Almost 3 years ago, HPE acquired SGI. Back in 2017, HPE partnered WekaIO, and invested big in the latest Series C funding of WekaIO just weeks ago.

Cray, SGI and WekaIO are all strong HPC technology companies. Given the strong uptick in the HPC market, especially commercial HPC, we cannot deny HPE’s ambition to become the top SuperComputing and HPC vendor in the industry. Continue reading

WekaIO controls their performance destiny

[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]

I was first introduced to WekaIO back in Storage Field Day 15. I did not blog about them back then, but I have followed their progress quite attentively throughout 2018. 2 Storage Field Days and a year later, they were back for Storage Field Day 18 with a new CTO, Andy Watson, and several performance benchmark records.

Blowout year

2018 was a blowout year for WekaIO. They have experienced over 400% growth, placed #1 in the Virtual Institute IO-500 10-node performance challenge, and also became #1 in the SPEC SFS 2014 performance and latency benchmark. (Note: This record was broken by NetApp a few days later but at a higher cost per client)

The Virtual Institute for I/O IO-500 10-node performance challenge was particularly interesting, because it pitted WekaIO against Oak Ridge National Lab (ORNL) Summit supercomputer, and WekaIO won. Details of the challenge were listed in Blocks and Files and WekaIO Matrix Filesystem became the fastest parallel file system in the world to date.

Control, control and control

I studied WekaIO’s architecture prior to this Field Day. And I spent quite a bit of time digesting and understanding their data paths, I/O paths and control paths, in particular, the diagram below:

Starting from the top right corner of the diagram, applications on the Linux client (running Weka Client software) and it presents to the Linux client as a POSIX-compliant file system. Through the network, the Linux client interacts with the WekaIO kernel-based VFS (virtual file system) driver which coordinates the Front End (grey box in upper right corner) to the Linux client. Other client-based protocols such as NFS, SMB, S3 and HDFS are also supported. The Front End then interacts with the NIC (which can be 10/100G Ethernet, Infiniband, and NVMeoF) through SR-IOV (single root IO virtualization), bypassing the Linux kernel for maximum throughput. This is with WekaIO’s own networking stack in user space. Continue reading

Sexy HPC storage is all the rage

HPC is sexy

There is no denying it. HPC is sexy. HPC Storage is just as sexy.

Looking at the latest buzz from Super Computing Conference 2018 which happened in Dallas 2 weeks ago, the number of storage related vendors participating was staggering. Panasas, Weka.io, Excelero, BeeGFS, are the ones that I know because I got friends posting their highlights. Then there are the perennial vendors like IBM, Dell, HPE, NetApp, Huawei, Supermicro, and so many more. A quick check on the SC18 website showed that there were 391 exhibitors on the floor.

And this is driven by the unrelentless demand for higher and higher performance of computing, and along with it, the demands for faster and faster storage performance. Commercialization of Artificial Intelligence (AI), Deep Learning (DL) and newer applications and workloads together with the traditional HPC workloads are driving these ever increasing requirements. However, most enterprise storage platforms were not designed to meet the demands of these new generation of applications and workloads, as many have been led to believe. Why so?

I had a couple of conversations with a few well known vendors around the topic of HPC Storage. And several responses thrown back were to put Flash and NVMe to solve the high demands of HPC storage performance. In my mind, these responses were too trivial, too irresponsible. So I wanted to write this blog to share my views on HPC storage, and not just about its performance.

The HPC lines are blurring

I picked up this video (below) a few days ago. It was insideHPC Rich Brueckner interview with Dr. Goh Eng Lim, HPE CTO and renowned HPC expert about the convergence of both traditional and commercial HPC applications and workloads.

I liked the conversation in the video because it addressed the 2 different approaches. And I welcomed Dr. Goh’s invitation to the Commercial HPC community to work with the Traditional HPC vendors to help push the envelope towards Exascale SuperComputing.

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Is Pure Play Storage good?

I post storage and cloud related articles to my unofficial SNIA Malaysia Facebook community (you are welcomed to join) every day. It is a community I started over 9 years ago, and there are active live banters of the posts of the day. Casual, personal were the original reasons why I started the community on Facebook rather than on LinkedIn, and I have been curating it religiously for the longest time.

The Big 5 of Storage (it was Big 6 before this)

Looking back 8-9 years ago, the storage vendor landscape of today has not changed much. The Big 5 hegemony is still there, still dominating the Gartner Magic Quadrant for Enterprise and Mid-end Arrays, and is still there in the All-Flash quadrant as well, albeit the presence of Pure Storage in that market.

The Big 5 of today – Dell EMC, NetApp, HPE, IBM and Hitachi Vantara – were the Big 6 of 2009-2010, consisting of EMC, NetApp, Dell, HP, IBM and Hitachi Data Systems. The All-Flash, or Gartner calls it Solid State Arrays (SSA) market was still an afterthought, and Pure Storage was just founded. Pure Storage did not appear in my radar until 2 years later when I blogged about Pure Storage’s presence in the market.

Here’s a look at the Gartner Magic Quadrant for 2010:

We see Pure Play Storage vendors in the likes of EMC, NetApp, Hitachi Data Systems (before they adopted the UCP into their foray), 3PAR, Compellent, Pillar Data Systems, BlueArc, Xiotech, Nexsan, DDN and Infortrend. And when we compare that to the 2017 Magic Quadrant (I have not seen the 2018 one yet) below:

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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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