Storage does not mean Capacity only

I was listening to several storage luminaries in the GestaltIT’s podcastNo one understands Storage anymore” a few of weeks ago. Around the minute of 11.09 in the podcast, Dr. J. Metz, SNIA® Chair, brought up this is powerful quote “Storage does not mean Capacity“. It struck me, not in a funny way. It is what it is, and it something I wanted to say to many who do not understand the storage solutions they are purchasing. It exemplifies what is wrong in the many organizations today in their understanding of investing in a storage infrastructure project.

This is my pet peeve. The first words uttered in most, if not all storage requirements in my line of work are, “I want this many Terabytes of storage“. There are no other details and context of what the other requirement factors are, such as availability, performance, future growth, etc. Or even the goals to achieve when purchasing a storage system and operating it. What is the improvement they are looking for? What are the problems to solve?

Where is the OKR?

It pains me to say this. For the folks who have in the IT industry for years, both end users and IT purveyors alike, most are absolutely clueless about OKR (Objectives and Key Results) for their storage infrastructure project. Many cannot frame the data challenges they are facing, and they have no idea where to go next. There is no alignment. There is no strategy. Even worse, there is no concept of how their storage infrastructure investments will improve their business and operations.

Just the other day, one company director from a renown IT integrator here in Malaysia came calling. He has been in the IT industry since 1989 (I checked his Linkedin profile), asking to for a 100TB storage quote. I asked a few questions about availability, performance, scalability; the usual questions a regular IT guy would ask. He has no idea, and instead of telling me he didn’t know, he gave me a runaround of this and that. Plenty of yada, yada nonsense.

In the end, I told him to buy a consumer grade storage appliance from Taiwan. I will just let him make a fool of himself in front of his customer since he didn’t want to take accountability of ensuring his customer get a proper enterprise storage solution in good faith. His customer is probably in the same mould as well.

Defensive Strategies as Data Foundations

A strong storage infrastructure foundation is vital for good Data Credibility. If you do the right things for your data, there is Data Value, and it will serve your business well. Both Data Credibility and Data Value create confidence. And Confidence equates Trust.

In order to create the defensive strategies let’s look at storage Availability, Protection, Accessibility, Management Security and Compliance. These are 6 of the 8 data points of the A.P.P.A.R.M.S.C. framework.

Offensive Strategies as Competitive Advantage

Once we have achieved stability of the storage infrastructure foundation, then we can turn over and drive towards storage Performance, Recovery, plus things like Scalability and Agility.

With a strong data infrastructure foundation, the organization can embark on the offensive, and begin their business transformation journey, knowing that their data is well run, protection, and performs.

Alignment with Data and Business Goals

Why are the defensive and offensive strategies requiring alignment to business goals?

The fact is simple. It is about improving the business and operations, and setting OKRs is key to measure the ROI (return of investment) of getting the storage systems and the solutions in place. It is about switching the cost-fearing (negative) mindset to a profit-conviction (positive) mindset.

For example, maybe the availability of the data to the business is poor. Maybe there is the need to have access to the data 24×7, because the business is going online. The simple measurable fact is we can move availability from 95% uptime to 99.99% uptime with an HA storage system.

Perhaps there are concerns about recoverability in the deluge of ransomware threats. Setting new RPO goals from 24 hours to 4 hours is a measurable objective to enhance data resiliency.

Or getting the storage systems to deliver higher performance from 350 IOPS to 5000 IOPS for the database.

What I am saying here is these data points are measurable, and they can serve as checkpoints for business and operational improvements. From a management perspective, these can be used as KPI (key performance index) to define continuous improvement of Data Confidence.

Furthermore, it is easy when a OKR dashboard is used to map the improvement markers when organizations use storage to move from point A to point B, where B equates to a new success milestone. The alignment sets the paths to the business targets.

Storage does not mean only Capacity.

The sad part is what the OKRs and the measured goals alignments are glaringly missing in the minds of many organizations purchasing a storage infrastructure and data management solution. The people tasked to source a storage technology solution are not placing a set of goals and objectives. Capacity appears to be the only thing on their mind.

I am about to meet a procurement officer of a customer soon. She asked me this question “Why is your storage so expensive?” over email. I want to change her mindset, just like the many officers and C-levels who hold the purse strings.

Let’s frame the use storage infrastructure in the real world. Nobody buys a storage system just to keep data in there much like a puddle keeps stagnant water. Sooner or later the value of the data in the storage evaporates or the value becomes dull if the data is not used well in any ways, shape or form.

Storage systems and the interconnected pathways from on premises, to the next premises, to the edge and to the clouds serve the greater good for Data. Data is used, shared, shaped, improved, enhanced, protected, moved, and more to deliver Value to the Business.

Storage capacity is just one of the few factors to consider when investing in a storage infrastructure solution. In fact, capacity is probably the least important piece when considering a storage solution to achieve the company’s OKRs. If we think about it deeper, setting the foundation for Data in the defensive manner will help elevate value of the data to be promoted with the offensive strategies to gain the competitive advantage.

Storage infrastructure and storage solutions along with data management platforms may appear to be a cost to the annual budgets. If you know set the OKRs, define A to get to B, alignment the goals, storage infrastructure and the data management platforms and practices are investments that are worth their weight in gold. That is my guarantee.

On the flip side, ignoring and avoiding OKRs, and set the strategies without prudence will yield its comeuppance. Technical debts will prevail.

Rant over.

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

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

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

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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Celebrating MinIO

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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How well do you know your data and the storage platform that processes the data

Last week was consumed by many conversations on this topic. I was quite jaded, really. Unfortunately many still take a very simplistic view of all the storage technology, or should I say over-marketing of the storage technology. So much so that the end users make incredible assumptions of the benefits of a storage array or software defined storage platform or even cloud storage. And too often caveats of turning on a feature and tuning a configuration to the max are discarded or neglected. Regards for good storage and data management best practices? What’s that?

I share some of my thoughts handling conversations like these and try to set the right expectations rather than overhype a feature or a function in the data storage services.

Complex data networks and the storage services that serve it

I/O Characteristics

Applications and workloads (A&W) read and write from the data storage services platforms. These could be local DAS (direct access storage), network storage arrays in SAN and NAS, and now objects, or from cloud storage services. Regardless of structured or unstructured data, different A&Ws have different behavioural I/O patterns in accessing data from storage. Therefore storage has to be configured at best to match these patterns, so that it can perform optimally for these A&Ws. Without going into deep details, here are a few to think about:

  • Random and Sequential patterns
  • Block sizes of these A&Ws ranging from typically 4K to 1024K.
  • Causal effects of synchronous and asynchronous I/Os to and from the storage

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At the mercy of the cloud deity

Amazon Web Services (AWS) went down in the middle of last week. News of the outage were mentioned:

AWS Management Console unavailable error

Piling the misery

The AWS outage headlines attract the naysayers, the fickle armchair pundits, and the opportunists. Here are a few news articles that bring these folks to chastise the cloud giant.

Of course, I am one of these critics. I don’t deny that I am not. But I read this situation from a multicloud hyperbole of which I am not a fan. Too much multicloud whitewashing by vendors trying to pitch multicloud as a disaster recovery solution without understanding that this is easier said than done.

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Storage Elephant Compute Birds

Data movement is expensive. Not just costs, but also latency and resources as well. Thus there were many narratives to move compute closer to where the data is stored because moving compute is definitely more economical than moving data. I borrowed the analogy of the 2 animals from some old NetApp® slides which depicted storage as the elephant, and compute as birds. It was the perfect analogy, because the storage is heavy and compute is light.

“Close up of a white Great Egret perching on top of an African Elephant aa Amboseli national park, Kenya”

Before the animals representation came about I used to use the term “Data locality, Data Mobility“, because of past work on storage technology in the Oil & Gas subsurface data management pipeline.

Take stock of your data movement

I had recent conversations with an end user who has been paying a lot of dollars keeping their “backup” and “archive” in AWS Glacier. The S3 storage is cheap enough to hold several petabytes of data for years, because the IT folks said that the data in AWS Glacier are for “backup” and “archive”. I put both words in quotes because they were termed as “backup” and “archive” because of their enterprise practice. However, the face of their business is changing. They are in manufacturing, oil and gas downstream, and the definitions of “backup” and “archive” data has changed.

For one, there is a strong demand for reusing the past data for various reasons and these datasets have to be recalled from their cloud storage. Secondly, their data movement activities still mimicked what they did in the past during their enterprise storage days. It was a classic lift-and-shift when they moved to the cloud, and not taking stock of  their data movements and the operations they ran on these datasets. Still ongoing, their monthly AWS cost a bomb.

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What happened to NDMP?

The acronym NDMP shows up once in a while in NAS (Network Attached Storage) upgrade tenders. And for the less informed, NDMP (Network Data Management Protocol) was one of the early NAS data management (more like data mover specifications) initiatives to backup NAS devices, especially the NAS appliances that run proprietary operating systems code.

NDMP Logo

Backup software vendors often have agents developed specifically for an operating system or an operating environment. But back in the mid-1990s, 2000s, the internal file structures of these proprietary vendors were less exposed, making it harder for backup vendors to develop agents for them. Furthermore, there was a need to simplify the data movements of NAS files between backup servers and the NAS as a client, to the media servers and eventually to the tape or disk targets. The dominant network at the time ran at 100Mbits/sec.

To overcome this, Network Appliance® and PDC Solutions/Legato® developed the NDMP protocol, allowing proprietary NAS devices to run a standardized client-server architecture with the NDMP server daemon in the NAS and the backup service running as an NDMP client. Here is a simplified look at the NDMP architecture.

NDMP Client-Server Architecture

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