Bridging the Gap Between Cloud and Edge
Michael Alba posted on June 13, 2017 |
AWS Greengrass promises a flexible Edge and Cloud IoT solution. (Image courtesy of AWS).
AWS Greengrass promises a flexible Edge and Cloud IoT solution. (Image courtesy of AWS).

Amazon Web Services (AWS) has recently announced a new service, AWS Greengrass, that aims to give Internet of Things (IoT) engineers more flexibility on the Edge.

In a blog post, AWS CTO Werner Vogels says that “[u]nlocking the value of data is a primary goal that AWS helps our customers to pursue.” He goes on to describe how local data processing (aka Edge analytics) is often necessary for this goal: “…it can be valuable to process some data right at the source where it is generated. Some applications – medical equipment, industrial machinery, and building automation are just a few – can't rely exclusively on the cloud for control.”

According to Vogels, AWS Greengrass will allow IoT developers to take advantage of both Edge and Cloud processing as needed. That’s because AWS Greengrass “extends AWS onto your devices, so they can act locally on the data they generate while still taking advantage of the cloud.”

Here’s how it works: via the cloud, users define sets of IoT devices called Greengrass groups. Each group must include at least one Linux-running device configured as a Greengrass Core, which acts as the hub between the Greengrass group and the AWS Cloud. The Core is also able to locally execute AWS Lambda code (code that otherwise exists in the Cloud) that’s triggered in response to IoT events (such as certain types of sensor data).

Overview of a Greengrass group. (Image courtesy of AWS).
Overview of a Greengrass group. (Image courtesy of AWS).

In this way, even when a connection to the Cloud is unavailable, the devices in the Greengrass group can still communicate and process data over a local network. Then, when the connection returns, the data can be uploaded and synced to the Cloud.

For many IoT applications, there’s a major benefit to this kind of leeway between Edge and Cloud processing.

“Before AWS Greengrass, device builders often had to choose between the low latency of local execution, and the flexibility, scale, and ease of the cloud,” says Vogels. “AWS Greengrass removes that trade-off—manufacturers and OEMs can now build solutions that use the cloud for management, analytics, and durable storage, while keeping critical functionality on-device or nearby.”

To learn more about AWS Greengrass, visit the AWS website. For more information on Edge analytics, read Edge Processing Allows for Faster Decision Making on the IoT.

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