Cloud computing

Cloud computing has helped start, grow and transform many organizations across many business segments contributing significantly to global economic growth. One of the important areas that cloud computing expansion and adoption is large data. No blockchain currently aims to accommodate big data and this is where DXChain plans to create an impact. Substantial data has helped derive insights from enormous data (with volume, variety and speed ) sources that are both structured and unstructured, and more data resources are being added at a much faster rate every day during the rapid adoption of IoT. Now that the cloud has helped us figure out how to capture data, machine learning algorithms are turning them into a learning ground to construct intelligence that will transform the world like never before. With the recent advancements we've made in machine learning, it's clear now more than ever before that AI is going to play a vital role in the evolution of humanity. Now is about the time that we should begin yielding to machine intelligence by building and positioning our compute models that can recognize the requirements of machines as a separate and essential element. This means we should expect to see both centralized and decentralized models (and some other future models) to live and coexist to serve dedicated purposes seamlessly. I feel that the cloud model works well for human-centric use and the decentralized model for machines and distributed data applications. Mainframe and mini computing utilize the centralized model where the distributed dumb terminals offer I/O, but computing tools and applications are centralized.

In the 80s, and including the later variants, Unix introduced the idea of decentralized computing where
software was set up on several computers/workstations on a LAN/WAN network. By virtue of this, both standalone and networked architectures in the kind of client/server and peer-to-peer applications were embraced. Then came the Web technologies of the'90s, which made it feasible to deploy the server software in one spot and Web clients were deployed as needed in remote, distributed places. This brought back the centralized model again that is still continued today via the cloud. Adding the decentralized model as a separate offering to tackle machine and distributed data needs won't only be costly but also be technically very challenging for cloud sellers. It will require embeddable or separate border resources (software and hardware) distributed with seamless interconnection with one another.

There are billions of connections, and all of these are connected to cloud backbone for larger offline data processing. And I think today's cloud models will continue to evolve with more innovation to build on the centralized cloud models for human-centric applications, but it will be interesting to see how things will play out later on. To answer this question, we need to understand from a historic perspective of how the different computing models were adopted as new technologies emerged. There have been primarily two models that have been playing out alternatively since the mainframe was introduced to the business world. By creating differentiated cloud models for human-centric vs. machine-centric through the use of centralized and decentralized computing, respectively, cloud vendors can take the market positions and value they have already established and evolve to be futuristic. My view on the debate as to whether machines will take over humans is that its absurd.

If we built AI properly, then the machines will know that coexistence and peace is the ideal way and will figure out ways to accomplish this by guiding humans and not try to take over the world. There are other emerging regions popping up everywhere like cryptocurrencies, the machine market (i.e., a combination of IoT and cryptocurrencies), distributed applications such as Ethereum DApps and a whole slew of peer-to-peer applications. However, the majority of these emerging areas require a computing model that appears to be incompatible with the current cloud compute model. Can cloud providers do something about this incompatibility and change the cloud to provide comprehensive services in the future, or will they fall prey to disruption?

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