The young report card on Decentralized Storage

I kept this blog in my queue for over 4 months. I was reluctant to publish it because I thought the outrageous frenzies of NFTs (non-fungible tokens), metaverses and web3 were convoluting the discussions on the decentralized storage topic. 3 weeks back, a Google Trends search for these 3 opaque terms over 90 days showed that the worldwide fads were waning. Here was the Google Trends output on April 2, 2022:

Google Trends on NFT, metaverse and web3

Decentralized storage intrigues me. I like to believe in its potential and I often try to talk to people to strengthen the narratives, and support its adoption where it fits. But often, the real objectives of decentralized storage are obfuscated by the polarized conversations about cryptocurrencies that are pegged to their offerings, NFTs (non-fungible tokens), DAOs (decentralized autonomous organizations) and plenty of hyperboles with bewildering facts as well.

But I continue to seek sustainable conversations about decentralized storage without the sway of the NFTs or the cryptos. After dipping in my toes and experiencing with HODLers, and looking at the return to sanity, I believe we can discuss decentralized storage with better clarity now. The context is to position decentralized storage to the mainstream, specifically to business organizations already immersed in centralized storage. Here is my fledgling report card on decentralized storage.

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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, 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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HODLing Decentralized Storage is not zero sum

I have been dipping my toes into decentralized storage. I wrote about “Crossing the Chasm” last month where most early technologies have to experience to move into the mainstream adoption. I believe the same undertaking is going on for decentralized storage and the undercurrents are beginning to feel like a tidal wave. However, the clarion calls and the narratives around decentralized storage are beginning to sound the same after several months on researching the subject.

Salient points of decentralized storage

I have summarized a bunch of these arguments for decentralized storage. They are:

  • Democratization of cloud storage services separate from the hyperscaling behemoths of Web2
  • Inherent data security with default encryption, immutability and blockchain-ed. (most decentralized storage are blockchain-based. A few are not)
  • Data privacy with the security key for data decryption and authentication with the data owner(s)
  • No centralized control of data storage services, prices, market transparency and sovereignty
  • Green with more efficient energy consumption compared to Bitcoin
  • Data durability with data sharding creating no single point of failure and maintaining continuous data access services with geo content dispersal

Rocket fuel – The cryptos

Most early adoptions of a new technology require some sort of bliztscaling momentum to break free from the gravity of the old one. The cryptocurrencies pegged to many decentralized storage platforms are the rocket fuel to power the conversations and the narratives of the decentralized storage today. I probably counted over a hundred of these types of cryptocurrencies, with more jumping into the bandwagon as the gravy train moves ahead.

The table below is part of a TechTarget Search Storage article “7 Decentralized Storage Networks compared“. I found this article most enlightening.

7 Decentralized Storage Compared

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The Currency to grow Decentralized Storage

Unless you have been living under a rock in the past months, the fervent and loud, but vague debates of web3.0 have been causing quite a scene on the Internet. Those tiny murmurs a few months ago have turned into an avalanche of blares and booms, with both believers and detractors crying out their facts and hyperboles.

Within the web3.0, decentralized storage technologies have been rising to a crescendo. So many new names have come forth into the decentralized storage space, most backed by blockchain and incentivized by cryptocurrencies and is putting the 19th century California Gold Rush to shame.

At present, the decentralized storage market segment is fluid, very vibrant and very volatile. Being the perennial storage guy that I am, I would very much like the decentralized storage to be sustainably successful but first, it has to make sense. Logic must prevail before confidence follows.

Classic “Crossing the Chasm”

To understand this decentralization storage chaos, we must understand where it is now, and where it is going. History never forgets to teach us of the past to be intelligible in the fast approaching future.

I look to this situation as a classic crossing the chasm case. This Crossing the Chasm concept was depicted in Geoffrey Moore’s 1991 book of the same name. In his book, he spoke well about the Technology Adoption Cycle that classifies and demonstrates the different demographics and psychological progression (and regression) of how a technology is taken to mainstream.

Geoffrey Moore’s Crossing the Chasm Technology (Disruption) Adoption Cycle

As a new technology enters the market, the adoption is often fueled by the innovators, the testers, the crazy ones. It progresses and the early adopters set in. Here we get the believers, the fanatics, the cults that push the envelope a bit further, going against the institutions and the conventions. This, which is obvious, describes the early adopter stage of the decentralized storage today.

Like all technologies, it has to go mainstream to be profitable and to get there, its value to the masses must be well defined to be accepted. This is the market segment that decentralized storage must move to, to the early majority stage. But there is a gap, rightly pointed out and well defined by Geoffrey Moore. The “Chasm“. [ Note: To read about why the chasm, read this article ].

So how will decentralized storage cross the chasm to the majority of the market?

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