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Cloud vs. Edge Computing

6 min read
Cloud vs. Edge Computing

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There is an endless number of buzzwords within the technology industry at any given moment. We’ve all heard quite enough about the Cloud, the sting, and therefore the Internet of Things (IoT) for the past several years. However, there still seems to be some confusion within the Smart Manufacturing industry about how these three terms work together.

The Internet of Things refers to the billions of physical devices around the world that are now connected to the web, collecting and sharing data. they’ll, however, be sharing that data to the Cloud (IoT Cloud Computing) or keeping it local, at the sting (IoT Edge Computing). What I’m seeing within the marketplace is that manufacturing companies don’t realize or skills to effectively do both. they’re either sending all of their data to the Cloud, or none of it. the aim of this text is to seem at the usefulness of Cloud vs. Edge computing as a part of an IoT solution.

The Limitations of Cloud Computing

Let’s start with Cloud Computing. As I discussed, there seems to be an all or nothing approach. Many companies believe all real-time computing should be pushed to the Cloud. they need to exchange all local databases and make them available on the Cloud to enable various dashboards, make the info readily available ten years down the road, and do not need to worry about long-term storage and security.

However, I’m here to inform you the Cloud isn’t the end-all-be-all for real-time computing, and here’s why.

  • Limited network bandwidth. Networks are still not at the extent we would like them to be, latency still exists. If someone wants something quickly, then collecting the info and sending it to the Cloud to be analyzed takes overtime . to not mention the very fact that a lot of factories aren’t Internet-connected in the least yet or are located in remote areas with poor network availability.
  • Not ideal for short-term analysis. The Cloud is sweet for long-term analysis. it’s enormous power for long-term tasks, watching data over time, storing it, etc. However, if you would like data a few specific assets and you would like it now, the Cloud isn’t the perfect solution.
  • Real-time deciding. Managing several sorts of data formats during a central location is often chaotic. there’s wisdom in doing something specific to an asset right next to the asset without counting on the other external systems which will or might not have connection issues so as to require action on the info in near real-time.

These limitations aren’t to mention that Cloud Computing isn’t useful or doesn’t have its place – but it can’t do everything. What I see most frequently is companies collecting vast amounts of knowledge and sending it to the Cloud, on the other hand only employing a small fraction of that data. they’re spending time and money on data they’ll never need or check out.

Or you have the opposite side of the coin – not collecting enough data within the Cloud. within the manufacturing industry especially, most factories aren’t pushing all of their data to the Cloud, they’re only pushing the variables they know they’re going to need like uptime, downtime, production run rate, and more. But these variables aren’t asset dependent. Suppose they need to understand how a specific asset is performing. Suppose they need to maneuver towards machine learning or other use cases that believe asset-specific data. IoT Cloud computing can’t do this job alone.

Edge Computing Fills within the Gaps

Enter Edge computing. Edge computing is distributed intelligence at the asset-level.

There are some processing functions and data analysis you would like to stay at the sting. there’s some data that’s useful at the sting instead of during a central location. the great thing about Edge computing is you’re still collecting vast amounts of knowledge and you’ll turn it whenever you would like it. However, you aren’t sending it all up to the Cloud, and crossing your fingers it’ll become useful someday – which saves time and money.

The Edge layer is meant to research the info next to the asset, where the sting is connected. An IoT gateway with a foothold computing platform will normalize the info and pre-process it so you’re not sending everything to the Cloud. Then, the supervisor must determine which parameters are useful within the Cloud and run those applications there.

I already mentioned that Cloud computing isn’t the end-all-be-all, but let’s check out some specific reasons why Edge computing is often a key a part of any good IoT solution for real-time computing.

Security. Anytime you send data to the Cloud you risk security. Keeping it secure at the sting is easier.
Storing some data at the sting is just cheaper than storing all of it within the Cloud.
Speed of knowledge analysis. Analyzing smaller amounts of knowledge locally, at the asset, saves tons of your time over sending everything to the Cloud for batch analysis.
doesn’t require connectivity. Many factory floors that aren’t yet connected to the web but still got to be ready to analyze data, which may be done locally with Edge computing.

Cloud vs. Edge Computing

So, when planning an IoT project, how does a customer skills much of their data should be processed at the sting vs. what proportion within the Cloud?

Any high volume IoT system should have both layers – Edge and Cloud (and everything in between for that matter but that’s another topic for an additional day). What I mean by high volume is vast amounts of knowledge. Many assets, in many locations, with many various parameters all of which require to be analyzed for various business purposes starting from analysis to predictive maintenance to factory performance.

What sorts of functions should be left at the Edge? Anything that affects the local assets. as an example, if the utilization case is to gather several parameters from a series of assets to work out how well they’re functioning, then flag them red or green for functionality, that data is often processed at the sting. Statistical analysis is often performed there and therefore the alerts are often cleared locally if a drag exists. That way if there’s a drag on an area asset it is often drilled down easily.

What sorts of functions should be performed within the Cloud? A long-term analysis is that the primary function for the Cloud. Businesses want to ascertain how their factories are performing over time and people numbers are still best analyzed within the Cloud. Also, if you’re looking to try to complex machine learning, then you would like more compute power and enormous amounts of knowledge over time so as to make accurate machine learning models. However, once you’ve got created those models, it’s better to run them at the sting for the explanations we’ve mentioned above.

Consider a producing plant, as an example, with a robotic system. The system performs tasks measured in milliseconds. Data output is extremely high, and data is refreshed every 10 milliseconds at the sting. Edge computing is important for this technique so as to stay the production line running smoothly. If this data was pushed to the Cloud it’s going to take an admin several hours to ascertain a drag. it might require an excellent deal of bandwidth and security to send all of that data to the Cloud. At the sting, a drag is discovered and addressed far more quickly.

As manufacturing companies move towards IoT systems they need to try to so as efficiently and cost-effectively as possible. the mixture of IoT Edge computing and Cloud computing allows them to realize intelligence at the sting without changing an excessive amount of on the factory floor. they will start by collecting data from legacy systems right next to them. Then, over time, they will send the info that creates sense to the Cloud for those long-term applications and development of machine learning models. And then, once those Machine Learning models are created, run them at the sting to require action on the info being a collection for real-time deciding.

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