Whenever you save a note to Google Keep or tap a server-hosting service (e.g., AWS), you are using the Cloud — an umbrella term for a range of networking, storage, and computation available on-demand and offered by organizations around the world. The concept relies on pooling shared resources of computers, to achieve economies of scale and empower much of the services, applications, and infrastructure of the modern Internet.
Cloud infrastructure and services have exploded in recent years, with Amazon Web Services (AWS), Microsoft Azure, and Google Cloud leading the charge into the software-as-as-services (SaaS) paradigm that is the fastest growing sector of a $3.76 trillion industry in global IT spending.
The rapid expansion of cloud computing has come at a decisive cost, though. Latency, high prices, reduced privacy, incompatibility with emerging technologies, and increasingly centralized repositories of sensitive user data are collateral effects of a market dominated by tech giants in an age of surveillance capitalism.
“Centralized solutions are not ideal for building decentralized, P2P and IoT applications which have the requirement for computation resources running close to the edge devices,” says DeepCloud AI CEO, Max Rye. “Processing the growing volume of data generated at the edge needs cost-effective solutions for payment flow and for machine-to-machine micropayments executed automatically as devices interact with each other and automate tasks.”
DeepCloud AI is a rising project at the convergence of cloud computing, blockchain-based decentralization, and artificial intelligence (AI). Their vision?
Democratize cloud computing and level the playing field for users, developers, resource providers, and small businesses.
Identifying and Addressing a Problem
Considering the sheer size, ubiquity, and power of cloud computing today, at first glance, it would seem odd to pinpoint areas of friction where the current landscape is either insufficient or creating adverse collateral effects. However, there is a certain truth to the fallacy of techno-optimism, and many times, our ability to identify downstream problem areas is wanting.
“The sharing economy has resulted in the dominance of specific markets by aggregation firms like Uber and Airbnb,” says Rye. “Just like many other technologies, their novelty often supersedes any perspective of their eventual progression to the massive, centralized behemoths that they have become today.”
Rye is accurate on that front. Uber accounts for 69 percent of the ride-hailing market share, and Airbnb had 80 million stays in 2016. Such dominance breeds the ability to charge high fees while concurrently fostering fears of data privacy in the hands of centralized walled gardens. While not terrifying problems, they present opportunities for improvement, particularly as decentralization and privacy rise to a premium among mainstream users tired of data impropriety by large tech firms.
“One of the emerging technologies that is disruptive to the current aggregative and cloud centralization models is blockchain,” details Rye. “Blockchains empower us to usher in a new era of the ‘Internet of Value,’ where data, marketplaces, and access to resources are democratized in line with the original vision of the Internet.”
That’s precisely where DeepCloud AI comes into the fold. By flattening the cloud computing landscape with an infrastructure built on a blockchain and resource allocation circumscribed by AI, DeepCloud AI is pushing for an ecosystem where the barrier to entry in developing and deploying decentralized applications is vastly reduced.
In particular, one of the areas where DeepCloud AI is preparing for an immediate impact is in the field of IoT “edge” devices, where a new generation of cost-effective, yet powerful applications can materialize.
“DeepCloud AI offers a revolutionary cloud infrastructure for decentralized applications that can meet enterprise performance demands,” says Rye.
Integrating DeepCloud AI With Practical IoT Applications
At its core, DeepCloud AI is a modular platform built on a blockchain that extends to three primary target areas: infrastructure-as-a-service (IaaS), application marketplaces, and a developer community. The model is subsequently split into a dual-sided architecture, with network resource providers on one end and decentralized application developers on the other.
“Network resource providers furnish computational and storage power to the application developers, who in turn, populate the application marketplaces in a mutually beneficial relationship,” says Rye.
One of the critical aspects of building a decentralized cloud infrastructure, that can reduce costs without sacrificing performance, is in the efficiency of resource allocation — handled by AI in DeepCloud. At a high level, the AI is a matching algorithm for network resources that maps efficient pathways to demands for those resources. The result is optimized and consistent resource allocation to edge (e.g., IoT) devices — breaching the powerful potential of more interconnected devices and their secure, private data.
“We enable resource providers close to the edge, such as retail shopping malls or apartment complex residents, to share the excess capacity of their computer resources on the decentralized cloud, close to the city traffic lights, making it possible to do these local computations close to the source and enable such use cases,” details the DeepCloud AI whitepaper. “With our AI matching engine, we match the right resources to the right applications based on real-time analytics of the data across the network.”
Tapping into the cryptography and promising SGX Enclave Computing technology, DeepCloud AI can also provide such resources to edge devices securely and without exposing sensitive user data. This is a momentous step for edge devices, which have a history littered with security and privacy mishaps.
DeepCloud AI is already getting the real-world IoT applications of their network off the ground too, and not just any run-of-the-mill use case either, it’s a project with the Mexican Federal Government.
“We recently announced the development of MexiCar on DeepCloud AI,” says Rye. “MexiCar is an IoT platform built on our infrastructure that stores, tracks, and secures ownership and vehicle registration information using our tamper-proof blockchain and integrated with Mexico’s Federal District Government Transport and Highway Department.”
Vehicle owners can use MexiCar for digital documentation to secure vehicle registration, which can be validated by third-parties, including the government and insurance companies, to streamline crash reporting and mitigate fraud or corruption. Due to its existence on DeepCloud AI, MexiCar can even overcome latency and connectivity problems in rural areas while simultaneously ensuring a censorship-resistant registry of user data.
The prototype is expected to launch in the state of Coahuila in July 2019 and encompasses a partnership with the firm Xilinx as well — a leading FPGA Accelerator Card producer. Xilinx’s FPGA cards help DeepCloud AI to process heavy computational loads at the edge, fostering an autonomous vehicle-friendly design that is forward-thinking.
In the end, DeepCloud AI strives to expand its decentralized cloud computing infrastructure beyond IoT devices and into other sectors such as supply chain, smart cities, and TV service providers.
“The potential for disruption is there,” says Rye. “Now it’s just about executing as we progress towards the Web 3.0 and a new iteration of the Internet landscape.”