Edge computing in hybrid cloud: 3 approaches

Hybrid computing in general has a great deal in common with hybrid computing that leverages edge computing. But you need different partitioning approaches for data and processing when you’ve decided to keep the processing of data as close to the source of your data as possible.

In the case of edge computing, you typically keep the computing platform or device near an IoT device that produces data. In the case of hybrid cloud, you want to keep the data on private or public clouds as close to the source (e.g., application) as possible. In both cases, you gain the advantage of lower latency and better logical partitioning of the data.

Hybrid systems have the objective to partition the data, and the processing, between your public and private cloud instances. (In “How to take on data management in hybrid clouds,” I discussed the tools and approaches to use for data management in the hybrid cloud.)

But with the new concept of edge computing—even edge computing paired with IoT-based systems or with public clouds—the use of a hybrid cloud architecture takes on new dynamics. Here’s what your team needs to understand about edge computing in hybrid environments, and how to best approach it.

These are the three basic approaches, or architectures, for edge computing that work for hybrid cloud:

There are three architectures and solutions patterns to understand. Some are obvious, and some are not-so-obvious approaches. These include the following:

Edge computing is all about the data, with some processing done on the edge as well. The idea is to place the data closest to the source, then provide rudimentary processing on that data and return decisions made on that data to the data source. This source is typically co-located with the edge computing device.

For instance, take an application that gathers data from a motorcycle, a motorcycle rider, and the environment (road conditions, weather, traffic, etc.). Here are a few decisions you must make:

With this last approach, you can do some processing on the edge data as well. Also, keep in mind that you’re still dependent upon your private or public cloud for processing the data in more sophisticated ways, such as when using machine learning.

The idea is that the system can work for long periods of time without a network connection, gather data the whole time, and locally process that data. Also keep in mind that the public or private cloud is the best place to do the processing, considering access to cheap CPU and data management platforms.

The last approach is often the best for leveraging data on the edge and manipulating the edge within a hybrid architecture. Let’s build on the motorcycle example.

The idea is to provide two tiers of information (cloud and edge computing). The edge tier is a small and inexpensive device that mounts on the motorcycle, which uses direct Bluetooth communication to connect with a dozen sensors on the bike, as well as a smartwatch that the rider wears to monitor biotelemetry. Finally, a Lidar-based scanner tracks other moving vehicles near the bike, including ones that are likely to be a threat.

The data the edge device gathers is also responsible for real-time alerting for things such as speed, behavior, and direction of other close vehicles that are likely to put the rider at risk.