Category Archives: Deep Learning

Object Storage becoming storage lingua franca of Edge-Core-Cloud

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Data Fabric was a big buzzword going back several years. I wrote a piece talking about Data Fabric, mostly NetApp®’s,  almost 7 years ago, which I titled “The Transcendence of Data Fabric“. Regardless of storage brands and technology platforms, and each has its own version and interpretations, one thing holds true. There must be a one layer of Data Singularity. But this is easier said than done.

Fast forward to present. The latest buzzword is Edge-to-Core-Cloud or Cloud-to-Core-Edge. The proliferation of Cloud Computing services, has spawned beyond to multiclouds, superclouds and of course, to Edge Computing. Data is reaching to so many premises everywhere, and like water, data has found its way.

Edge-to-Core-to-Cloud (Gratitude thanks to https://www.techtalkthai.com/dell-technologies-opens-iot-solutions-division-and-introduces-distributed-core-architecture/)

The question on my mind is can we have a single storage platform to serve the Edge-to-Core-to-Cloud paradigm? Is there a storage technology which can be the seamless singularity of data? 7+ years onwards since my Data Fabric blog, The answer is obvious. Object Storage.

The ubiquitous object storage and the S3 access protocol

For a storage technology that was initially labeled “cheap and deep”, object storage has become immensely popular with developers, cloud storage providers and is fast becoming storage repositories for data connectors. I wrote a piece called “All the Sources and Sinks going to Object Storage” over a month back, which aptly articulate how far this technology has come.

But unknown to many (Google NASD and little is found), object storage started its presence in SNIA (it was developed in Carnegie-Mellon University prior to that) in the early 90s, then known as NASD (network attached secure disk). As it is made its way into the ANSI T10 INCITS standards development, it became known as Object-based Storage Device or OSD.

The introduction of object storage services 16+ years ago by Amazon Web Services (AWS) via their Simple Storage Services (S3) further strengthened the march of object storage, solidified its status as a top tier storage platform. It was to AWS’ genius to put the REST API over HTTP/HTTPS with its game changing approach to use CRUD (create, retrieve, update, delete) operations to work with object storage. Hence the S3 protocol, which has become the de facto access protocol to object storage.

Yes, I wrote those 2 blogs 11 and 9 years ago respectively because I saw that object storage technology was a natural fit to the burgeoning new world of storage computing. It has since come true many times over.

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All the Sources and Sinks going to Object Storage

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The vocabulary of sources and sinks are beginning to appear in the world of data storage as we witness the new addition of data processing frameworks and the applications in this space. I wrote about this in my blog “Rethinking data. processing frameworks systems in real time” a few months ago, introducing my take on this budding new set of I/O characteristics and data ecosystem. I also started learning about the Kappa Architecture (and Lambda as well), a framework designed to craft and develop a set of amalgamated technologies to handle stream processing of a series of data in relation to time.

In Computer Science, sources and sinks are considered external entities that often serve as connectors of input and output of disparate systems. They are often not in the purview of data storage architects. Also often, these sources and sinks are viewed as black boxes, and their inner workings are hidden from the views of the data storage architects.

Diagram from https://developer.here.com/documentation/get-started/dev_guide/shared_content/topics/olp/concepts/pipelines.html

The changing facade of data stream processing presents the constant motion of data, the continuous data being altered as it passes through the many integrated sources and sinks. We are also see much of the data processed in-memory as much as possible. Thus, the data services from a traditional storage model of SAN and NAS may straggle with the requirements demanded by this new generation of data stream processing.

As the world of traditional data storage processing is expanding into data streams processing and vice versa, and the chatter of sources and sinks can no longer be ignored.

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Computational Storage embodies Data Velocity and Locality

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I have been earnestly observing the growth of Computational Storage for a number of years now.  It was known by several previous names, with the name “in-situ data processing” stuck with me the most. The Computational Storage nomenclature became more cohesive when SNIA® put together the CMSI (Compute Memory Storage Initiative) some time back. This initiative is where several standards bodies, the major technology players and several SIGs (special interest groups) in SNIA® collaborated to advance Computational Storage segment in the storage technology industry we know of today.

The use cases for Computational Storage are burgeoning, and the functional implementations of Computational Storage are becoming vital to tackle the explosive data tsunami. In 2018 IDC, in its Worldwide Global Datasphere Forecast 2021-2025 report, predicted that the world will have 175 ZB (zettabytes) of data. That number, according to hearsay, has been revised to a heady figure of 250ZB, given the superlative rate data is being originated, spawned and more.

Computational Storage driving factors

If we take the Computer Science definition of in-situ processing, Computational Storage can be distilled as processing data where it resides. In a nutshell, “Bring Compute closer to Storage“. This means that there is a processing unit within the storage subsystem which does not require the host CPU to perform processing. In a very simplistic manner, a RAID card in a storage array can be considered a Computational Storage device because it performs the RAID functions instead of the host CPU. But this new generation of Computational Storage has much more prowess than just the RAID function in a RAID card.

There are many factors in Computational Storage that make a lot sense. Here are a few:

  1. Voluminous data inundate the centralized architecture of the cloud platforms and the enterprise systems today. Much of the data come from end point devices – mobile devices, sensors, IoT, point-of-sales, video cameras, et.al. Pre-processing the data at the origin data points can help filter the data, reduce the size to be processed centrally, and secure the data before they are ingested into the central data processing systems
  2. Real-time processing of the data at the moment the data is received gives the opportunity to create the Velocity of Data Analytics. Much of the data do not need to move to a central data processing system for analysis. Often in use cases like autonomous vehicles, fraud detection, recommendation systems, disaster alerts etc require near instantaneous responses. Performing early data analytics at the data origin point has tremendous advantages.
  3. Moore’s Law is waning. The CPU (central processing unit) is no longer the center of the universe. We are beginning to see CPU offloading technologies to augment the CPU’s duties such as compression, encryption, transcoding and more. SmartNICs, DPUs (data processing units), VPUs (visual processing units), GPUs (graphics processing units), etc have come forth to formulate a new computing paradigm.
  4. Freeing up central resources with Computational Storage also accelerates the overall distributed data processing in the whole data architecture. The CPU and the adjoining memory subsystem are less required to perform context switching caused by I/O interrupts as in most of the compute/storage architecture today. The total effect relieves the CPU and giving back more CPU cycles to perform higher processing tasks, resulting in faster performance overall.
  5. The rise of memory interconnects is enabling a more distributed computing fabric of data processing subsystems. The rising CXL (Compute Express Link™) interconnect protocol, especially after the Gen-Z annex, has emerged a force to be reckoned with. This rise of memory interconnects will likely strengthen the testimony of Computational Storage in the fast approaching future.

Computational Storage Deployment Models

SNIA Computational Storage Universe in 2019

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Time to Conflate Storage with Data Services

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Around the year 2016, I started to put together a better structure to explain storage infrastructure. I started using the word Data Services Platform before what it is today. And I formed a pictorial scaffold to depict what I wanted to share. This was what I made at that time.

Data Services Platform (circa 2016)- Copyright Heoh Chin Fah

One of the reasons I am bringing this up again is many of the end users and resellers still look at storage from the perspective of capacity, performance and price. And as if two plus two equals five, many storage pre-sales and architects reciprocate with the same type of responses that led to the deteriorated views of the storage technology infrastructure industry as a whole. This situation irks me. A lot.

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Celebrating MinIO

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“Essentially MinIO is a web server …“

I vaguely recalled Anand Babu Periasamy (AB as he is known), the CEO of MinIO saying that when I first met him in 2017. I was fresh “playing around” with MinIO and instantly I fell in love with software technology. Wait a minute. Object storage wasn’t supposed to be so easy. It was not supposed to be that simple to set up and use, but MinIO burst into my storage universe like the birth of the Infinity Stones. There was a eureka moment. And I was attending one of the Storage Field Days in the US shortly after my MinIO discovery in late 2017. What an opportunity!

I could not recall how I made the appointment to meeting MinIO, but I recalled myself taking an Uber to their cosy office on University Avenue in Palo Alto to meet. Through Andy Watson (one of the CTOs then), I was introduced to AB, Garima Kapoor, MinIO’s COO and his wife, Frank Wessels, Zamin (one of the business people who is no longer there) and Ugur Tigli (East Coast CTO) who was on the Polycom. I was awe struck.

Last week, MinIO scored a major Series B round funding of USD103 million. It was delayed by the pandemic because I recalled Garima telling me that the funding was happening in 2020. But I think the delay made it better, because the world now is even more ready for MinIO than ever before.

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The Starbucks model for Storage-as-a-Service

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Starbucks™ is not a coffee shop. It purveys beyond coffee and tea, and food and puts together the yuppie beverages experience. The intention is to get the customers to stay as long as they can, and keep purchasing the Starbucks’ smorgasbord of high margin provisions in volume. Wifi, ambience, status, coffee or tea with your name on it (plenty of jokes and meme there), energetic baristas and servers, fancy coffee roasts and beans et. al. All part of the Starbucks™-as-a-Service pleasurable affair that intends to lock the customer in and have them keep coming back.

The Starbucks experience

Data is heavy and they know it

Unlike compute and network infrastructures, storage infrastructures holds data persistently and permanently. Data has to land on a piece of storage medium. Coupled that with the fact that data is heavy, forever growing and data has gravity, you have a perfect recipe for lock-in. All storage purveyors, whether they are on-premises data center enterprise storage or public cloud storage, and in between, there are many, many methods to keep the data chained to a storage technology or a storage service for a long time. The storage-as-a-service is like tying the cow to the stake and keeps on milking it. This business model is very sticky. This stickiness is also a lock-in mechanism.

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What the heck is Storage Modernization?

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We often hear the word “modernization” thrown around these days. The push is to get the end user to refresh their infrastructure, and the storage infrastructure market is rife with modernization word. Is your storage ripe for “modernization“?

Many possibilities to modernize storage

To modernize, it has to be relative to legacy storage hardware, and the operating environment that came with it. But if the so-called “legacy” still does the job, should you modernize?

Big Data is right

When the word “Big Data” came into prominence a while back, it stirred the IT industry into a frenzy. At one point, Apache Hadoop became the poster elephant (pun intended) for this exciting new segment. So many Vs came out, but I settled with 4 Vs as the framework of my IT conversations. The 4Vs we often hear are:

  • Volume
  • Velocity
  • Variety
  • Veracity

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The future of Fibre Channel in the Cloud Era

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The world has pretty much settled that hybrid cloud is the way to go for IT infrastructure services today. Straddled between the enterprise data center and the infrastructure-as-a-service in public cloud offerings, hybrid clouds define the storage ecosystems and architecture of choice.

A recent Blocks & Files article, “Broadcom server-storage connectivity sales down but recovery coming” caught my attention. One segment mentioned that the server-storage connectivity sales was down 9% leading me to think “Is this a blip or is it a signal that Fibre Channel, the venerable SAN (storage area network) protocol is on the wane?”

Fibre Channel Sign

Thus, I am pondering the position of Fibre Channel SANs in the cloud era. Where does it stand now and in the near future? Continue reading →

Where are your files living now?

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[ This is Part One of a longer conversation ]

EMC2 (before the Dell® acquisition) in the 2000s had a tagline called “Where Information Lives™“**. This was before the time of cloud storage. The tagline was an adage of enterprise data storage, proper and contemporaneous to the persistent narrative at the time – Data Consolidation. Within the data consolidation stories, thousands of files and folders moved about the networks of the organizations, from servers to clients, clients to servers. NAS (Network Attached Storage) was, and still is the work horse of many, many organizations.

[ **Side story ] There was an internal anti-EMC joke within NetApp® called “Information has a new address”.

EMC tagline “Where Information Lives”

This was a time where there were almost no concerns about Shadow IT; ransomware were less known; and most importantly, almost everyone knew where their files and folders were, more or less (except in Oil & Gas upstream – to be told in later in this blog). That was because there were concerted attempts to consolidate data, and inadvertently files and folders, in the organization.

Even when these organizations were spread across the world, there were distributed file technologies at the time that could deliver files and folders in an acceptable manner. Definitely not as good as what we have today in a cloudy world, but acceptable. I personally worked a project setting up Andrew File Systems for Intel® in Penang in the mid-90s, almost joined Tacit Networks in the mid-2000s, dabbled on Microsoft® Distributed File System with NetApp® and Windows File Servers while fixing the mountains of issues in deploying the worldwide GUSto (Global Unified Storage) Project in Shell 2006. Somewhere in my chronological listings, Acopia Networks (acquired by F5) and of course, EMC2 Rainfinity and NetApp® NuView OEM, Virtual File Manager.

The point I am trying to make here is most IT organizations had a good grip of where the files and folders were. I do not think this is very true anymore. Do you know where your files and folders are living today? 

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Storage IO straight to GPU

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The parallel processing power of the GPU (Graphics Processing Unit) cannot be denied. One year ago, nVidia® overtook Intel® in market capitalization. And today, they have doubled their market cap lead over Intel®,  [as of July 2, 2021] USD$510.53 billion vs USD$229.19 billion.

Thus it is not surprising that storage architectures are changing from the CPU-centric paradigm to take advantage of the burgeoning prowess of the GPU. And 2 announcements in the storage news in recent weeks have caught my attention – Windows 11 DirectStorage API and nVidia® Magnum IO GPUDirect® Storage.

nVidia GPU

Exciting the gamers

The Windows DirectStorage API feature is only available in Windows 11. It was announced as part of the Xbox® Velocity Architecture last year to take advantage of the high I/O capability of modern day NVMe SSDs. DirectStorage-enabled applications and games have several technologies such as D3D Direct3D decompression/compression algorithm designed for the GPU, and SFS Sampler Feedback Streaming that uses the previous rendered frame results to decide which higher resolution texture frames to be loaded into memory of the GPU and rendered for the real-time gaming experience.

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