Star InactiveStar InactiveStar InactiveStar InactiveStar Inactive
 

Cloud vendors want companies to use their platforms for the full scope of their IoT deployments, but that might not be their best choice. As edge computing emerges as part of IoT deployments, users must decide not only how often to send data to the cloud, but whether to send it there in the first...

The post Edge devices’ compute demands complicate cloud IoT choices appeared first on The Troposphere.


Cloud vendors want companies to use their platforms for the full scope of their IoT deployments, but that might not be their best choice.

As edge computing emerges as part of IoT deployments, users must decide not only how often to send data to the cloud, but whether to send it there in the first place. These decisions are particularly imperative for industrial customers and other settings where connected devices require more compute power nearby.

For starters, some edge devices have limited or no connectivity, so a steady stream of data transmission back to a cloud platform isn’t feasible. Furthermore, massive cloud data centers are typically located far from the source of IoT data, which can impact latency for data that requires quick analysis and decisions, such as to make an autonomous car change lanes. And some devices must process lots of data quickly or closer to the source of that information for compliance reasons, which in turn necessitates for more compute power at the edge.

AWS and Microsoft have begun to fill those gaps in their services with IoT services that extend from the cloud to the edge. AWS’ addition of Greengrass, its stripped-down software for edge devices, was particularly striking — for the first time in more than a decade of operations, AWS made its compute capabilities available outside its own data centers. That shift in philosophy illustrates just how much potential AWS sees in this market, and also some of the limitations.

With Greengrass and Azure IoT Edge, users now can streamline their IoT operations under one umbrella, and companies dabbling in IoT may find that attractive. Others may be drawn to the emerging collection of IoT vendors that process data as close to the source as possible.

Major cloud providers take a “cloud down” approach that uses existing big data technologies, but that emphasis doesn’t help if the business value of IoT requires decisions in a short timeframe, said Ramya Ravichandar, director of product management at FogHorn Systems. The startup company provides industrial customers machine learning at the edge, in partnerships and competition with those cloud providers.

Ravichandar cited the example of a review of system of assembly lines, where data is sent back to the cloud to run large-scale machine learning models to improve those systems, potentially across global regions.

“[The cloud is] where you want to leverage heavy duty training on large data stores, because building that model is always going to require bigger [compute power] than what is at the edge,” she said.

Users must decide if there’s value to send edge device data to the cloud determine where to store and process the data, weigh latency requirements and risks, and from all that determine costs and how to spread them between the edge and the cloud, said Alfonso Velosa, a Gartner analyst.

“We’re still figuring out how that architecture is going to roll out,” Velosa said. “Many companies are investing in it but we don’t know the final shape of it.”

The post Edge devices’ compute demands complicate cloud IoT choices appeared first on The Troposphere.


Read full article on Cloud Computing from IT knowledge exchange


Powered by Preisvergleich


My Tweets

Last Articles

How to open and manage tabs in macOS Finder

The macOS finder is more flexible than you think. Jack Wallen shows you how.

How to add falling snow to a PowerPoint slide

Spread a little holiday cheer by incorporating a falling snow effect in your next presentation.

How to turn features on and off in Microsoft Windows 10 from the Control Panel

Microsoft decided to conceal the traditional Control Panel, but you can still access it if you...

Using Microsoft Flow to connect Office 365 to Google's GSuite

Microsoft's low-code serverless tools can simplify application integration, and bring Windows and...

How to get started with no-code apps: 9 tips

No-code platforms can help businesses save time and money, according to Quick Base. Here's how to...

How the Eclipse Foundation is saving enterprise Java

The Eclipse Foundation is quiet and doesn't brag about its work on enterprise Java, but this...

5 goal-setting apps for iOS and Android users

It's easy to set a goal for yourself but achieving it can be tough. One of these five apps could...

5 productivity apps to help make work less overwhelming

If you're looking forward to a more productive new year, one of these apps may be just what you...

How to install Linux software from source

If you're curious about installing Linux apps from source, Jack Wallen questions if that's the...

How Python made it big at Microsoft

If your company is slow to embrace open source, take heart from Microsoft's Python experience.

  • How to open and manage tabs in macOS Finder

    Tuesday, 18 December 2018 19:30
  • How to add falling snow to a PowerPoint slide

    Tuesday, 18 December 2018 19:30
  • How to turn features on and off in Microsoft Windows 10 from the Control Panel

    Tuesday, 18 December 2018 18:30
  • Using Microsoft Flow to connect Office 365 to Google's GSuite

    Tuesday, 18 December 2018 16:30
  • How to get started with no-code apps: 9 tips

    Tuesday, 18 December 2018 15:30
  • How the Eclipse Foundation is saving enterprise Java

    Tuesday, 18 December 2018 00:30
  • 5 goal-setting apps for iOS and Android users

    Monday, 17 December 2018 23:30
  • 5 productivity apps to help make work less overwhelming

    Monday, 17 December 2018 22:30
  • How to install Linux software from source

    Monday, 17 December 2018 20:30
  • How Python made it big at Microsoft

    Saturday, 15 December 2018 17:30