This source is typically co-located with the edge computing device.įor 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: 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. Pull data to the edgeĮdge computing is all about the data, with some processing done on the edge as well. There are three architectures and solutions patterns to understand. Some are obvious, and some are not-so-obvious approaches. This approach has the potential to become a bit too complicated. The edge device is paired with a hybrid cloud, meaning a paired public and private cloud. This is typically the edge device just communicating with the "mother ship," where processing and data lives.The edge device is the master to the public cloud, meaning that the public cloud is subordinate to the edge device. While this just turns the tables a bit, the best use of this approach is when your applications need to run on the edge as a primary, with the public cloud in a supporting role. Many airlines are using this architecture. Considering the processing that occurs onboard a plane, the edge is more important than storing the data within a public cloud.The edge device and the public cloud provider become a new hybrid. This means that no private cloud exists and that the edge device serves as an analog to a private cloud.These are the three basic approaches, or architectures, for edge computing that work for hybrid cloud: (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.)īut 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. ![]() Hybrid systems have the objective to partition the data, and the processing, between your public and private cloud instances. In both cases, you gain the advantage of lower latency and better logical partitioning of the 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 the case of edge computing, you typically keep the computing platform or device near an IoT device that produces data. 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.
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