There are many discussions around edge computing and its adoption scenarios. One of the key ingredients that could accelerate edge transformation is the ability to use an existing software stack for continuous deployment to the edge. We believe that a new type of hardware that can organically coexist with the cloud ecosystem could be a solution to address the challenge. For example, Turing Pi designed to be an edge computer with cloud-native hardware architecture. 

The idea of processing data closer to the source isn’t new. Though, the opportunities that Kubernetes as well as machine learning open to it sound more promising than ever. 

It is doubtful that edge computing is going to replace the cloud. More likely, the edge is going to be an extension. That’s why the synergy of these two paradigms is empirical. 

Cloud computing has spawned many cloud-native solutions. Technologies like containers, serverless, Kubernetes changed the way developers build, deliver, and scale software. What all these technologies have in common is that they designed for distributed computing. The idea behind Turing Pi is a cluster of computers interconnected with Ethernet but all in one compact device. This architecture allows deploying and scaling software to the edge in the same way as in the cloud. 

The concept of a cluster board is similar to a PC motherboard but with an Ethernet network instead of a PCI bus. Rather than using just one processor, the cluster board can combine multiple processors and multiple types of processors. As an example, general-purpose compute modules can work in combination with machine learning modules. This heterogeneous approach could open a wider adaptation of machine learning applications at the edge. 

In other words, Turing Pi is a compact, energy-efficient, out of the box RACK system. These characteristics allow placing clusters in various locations, hard to reach places, and build things like mobile data centers. A striking example of cluster computing adoption is the Chick-Fil-A restaurant chain. The company placed local Kubernetes clusters in more than two thousand restaurants. That helped Chick-Fil-A to build a resilient, highly available, internet-independent system. As a result, the company automated critical business processes and reduced public cloud load by processing data locally. The possibility of using cloud-native technologies at the edge helped Chick-Fil-A to accelerate innovations and get the code running in production as fast as possible. 

Turing Pi V1 model is mostly aimed at developers, Devops and researchers. It is an amazing platform for learning and experimentation. The cluster board currently supports Raspberry Pi compute modules, which are intended for industrial applications. Raspberry Pi, by far, is the most popular single-board computer, and its colossal software ecosystem should help to lower entry points for new developers. Bare metal cluster using Raspberry Pis and running on Linux is not only affordable compared to what it can do, but can also be a great tool when dealing with resource-intensive tasks like compiling, continuous data parsing, or learning how to manage a cluster using real hardware instead of virtual machines. Also, it is very useful to have a personal Kubernetes cluster to explore concepts of distributed systems, create proof of concept prototypes, and deploy self-hosted services on-premises or at the edge with almost zero recurring costs.

Yes, we believe that compact clusters and edge computing is a great fit. Compact clusters could be great building blocks for edge infrastructure. It’s easy to scale within one mini cluster and within the whole fleet of clusters. Different cluster nodes intended for different type of applications, which is a more reliable and efficient way to run services. At the same time container orchestration tools like Kubernetes makes it easy to monitor, maintain and deliver software.