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3 IoT Edge Storage Considerations

Added to IoTplaybook or last updated on: 05/31/2019

IoT edge devices now do more than forward traffic from smaller devices. Often, they store information before forwarding it or actively process information on the edge device itself.

Every IoT installation is different, but IoT devices can spawn large amounts of data. Local storage and bandwidth for uploading that data to remote storage are often limited. Edge storage systems enable integrators to perform tasks like combining data from multiple devices and doing some analytics locally. Here are some of the features integrators should look for when local storage and processing are needed.

1. Local compute power

3 IoT Edge Storage Considerations
Dr. Tom Bradicich, HPE

“The whole notion of the edge is that it provides a location near where the data is generated, which can be used to process data and compute some sort of response without latency challenges,” says Lazarus Vekiarides, CTO and co-founder of storage vendor ClearSky Data. “Any time you send data to the cloud for analysis and then bring it back to act on locally, you’re going to experience high latency.” In other words, local processing is faster when speed is critical.

2. A compact form factor

“Small size is important to edge devices because it allows for higher density,” explains Dr. Tom Bradicich, vice president of IoT and converged systems at Hewlett Packard Enterprise.

3. Local analytics capability

Solutions with local analytics capabilities allow real-time analysis to be performed on data for issues that are time critical. The uploaded data can then be incorporated into a centralized data warehouse for use with data from other systems, like ERP and customer service applications.

“It’s essential to manage and analyze IoT data in a timely fashion, so it’s useful in real time,” explains Michele Null, global marketing leader in the IoT division at analytics software vendor SAS Institute Inc.

3 IoT Edge Storage Considerations
HPE Edgeline EL 1000

Edge-based real-time analytical models determine whether a particular event is relevant and generate instant alerts when urgent action is needed, like when a connected vehicle is traveling on an icy roadway and the driver receives an alert to slow down. Memory usage in such a system can be limited because it decides what data to discard and what data to keep long term.

As IoT installations grow and diversify across industries, managing the data they generate is increasingly important. Edge storage systems offer configuration options, including analytics and processing capabilities, that make managing that data and choosing where it is eventually stored easier for integrators and their IoT customers.

 

SCOTT KOEGLER is a technology journalist with 20 years’ experience writing about business, computing, and technology topics. He was CIO for three midsize companies for a total of 15 years. His work with developers, marketing, business processes, and C-level executives has allowed him to focus on the intersection of business and technology.