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What Is Edge Computing (& Why Should You Care)?

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Edge computing has been gaining popularity in enterprise tech—especially in the manufacturing, utility, and shipping industries—and you may be wondering if it’s an overhyped buzzword or something more substantial. The short answer is that edge computing is very much worth paying attention to, but to understand its implications we first need to discuss how it works, why it was developed, and what it can do for your operation.

Simply put, edge computing refers to a distributed computing framework that aims to process and manage data as close to the source of data generation as possible rather than sending said data to the cloud. This arrangement represents the most recent phase in computing technology’s never-ending oscillation between centralization and distribution.

What do we mean when we talk about the edge?

By “edge” we mean the border between the cloud and a local computing environment. In general networking terms, an edge device is any device that provides an entry point into that local network environment, such as a router.

With the growth of the Internet of Things, though, the term “edge” has expanded beyond networking devices, and now includes systems that are designed to process data on prem rather than having to send everything to the cloud. 

For manufacturers, edge computing could mean handling the processing and analysis of sensor data on premise instead of on the cloud. For shipping, it might mean using current weather data to optimize a vessel’s course using a machine learning model, without needing an internet connection.

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Benefits (and a few challenges) of edge computing

Modern edge computing architecture allows data from IoT devices to be processed at the edge of the network, either in lieu of being sent to a data center or cloud, or before being set. This arrangement presents several clear benefits: 

  • Speed: Because it effectively eliminates lag times that come with distance, edge computing can facilitate real-time data processing without latency. This is critical for devices and applications that depend on short and predictable response times, like autonomous vehicle operating systems (as milliseconds can really matter on a busy roadway) and digital-enabled factories (where smart devices perpetually monitor the manufacturing process).
  • Connection: In addition to being able to improve response times, edge computing can offer complex processing (like predictions from a model) in situations where cloud connectivity is unavailable or has limited capacity, like ships at sea or rural utility substations.
  • Security: Cloud computing architecture is inherently centralized, which means a cloud-reliant network is especially vulnerable to a single attack or power outage. Edge computing effectively disperses this kind of risk, as data is largely processed on local devices and storage can be distributed between local servers and data centers as needed.
  • Cost: Edge computing helps you categorize and manage data, which can save you a lot of money associated with bandwidth and storage costs. A simple example of this is a security camera in a warehouse. Most of the time such a camera is going to be recording empty space or common activity. Sending that footage to the cloud would be a waste of both bandwidth and the cost of storage those hours of footage. If you had an AI-powered security camera, though, it could analyze imagery, detect anything out of the ordinary, and only prioritize relevant footage for cloud storage. That’s effectively how edge computing works, but on a larger scale.

As with any technological development, there are also associated challenges, namely:

  • Security: Yes, security is listed here as both a benefit and a challenge. That’s because while the cloud presents a centralized vulnerability, as described above, edge devices represent a distributed vulnerability, in that each device can be compromised or otherwise operate incorrectly. In the case of cloud servers, you might well be using something like AWS that has the most advanced security available, but if you’re running your own systems, you’ll have to set up security on your own.
  • Storage requirements: Because less data is being sent to the cloud, edge computing requires higher storage requirements on the edge devices themselves. And even though storage is less expensive than ever before, it still requires power costs, which need to be factored into any investment. As mentioned above, though, doing processing on-site can help optimize storage needs so you’re only saving essential data instead of everything.
  • Maintenance requirements: Maintenance issues are similar to security ones, as the distributed architecture means there are more network combinations and discrete computing nodes that have to be maintained.

The issues raised above are indeed challenges, but they’re problems that can easily addressed. Every network architecture will have its shortcomings, but when you integrate edge computing into your network you’re providing yourself with increased flexibility to customize as necessary.

This is especially true in circumstances where remote servers can not be immediately accessed, such as on ships at sea. Right now, for instance, the U.S. Navy is preparing to install edge computing infrastructure aboard its fleet of aircraft carriers so that these distant ships can run critical (and data-rich) applications independently of a central network.

This same problem impacts the industrial transportation industry as well, which is why cargo ships, freight trains, and even semi-trailers are implementing edge architectures to overcome communication limitations, process sensor data, analyze fuel consumption, and even assist with autonomous navigation.

The future of edge computing

For the moment, edge computing is not a replacement for the cloud, but rather a complementary piece of network technology. Cloud-based data centers are still uniquely positioned to handle large flows of data (including massive files) and run applications that are not critically time-sensitive.

But the volume of data generated from the Internet of Things will continue to grow, and edge devices and servers are going to be necessary to process it at the source. That’s why research firm Gartner has predicted that by 2025, a staggering 75 percent of enterprise data will be generated and processed at “the edge.”

Edge computing is already being leveraged in industrial and manufacturing sectors, and the use cases described ahead will spur further applications in the near future:

  • Increased data collection: Edge computing allows sensors to gather critical data in remote locations where cloud connections are not stable or cost-effective, such as on cargo ships (as discussed above) or at oil fields or underground mining sites.
  • Increased interoperability: IoT service providers offer different (and proprietary) devices, APIs, and data formats, which has led to interoperability issues. Edge computing architecture, though, provides a standard platform for IoT applications and converts communication protocols for older “legacy” machines.
  • Increased cost savings: Edge computing immediately reduces costs associated with bandwidth and data storage. And by facilitating real-time processing of sensor data, it can reduce energy consumption by automatically adjusting lighting, cooling, and other environmental controls and ensure machines are operating consistently and productively.

As these examples make clear, it’s not strictly edge computing itself that is so valuable, but rather how it provides a cost-effective way to scale up IoT adoption. Increased use of IoT-enabled sensors and machinery, in turn, provides greater opportunities for machine learning and AI solutions.

Wrapping up

With ever-increasing amounts of data been generated every day, we’re sure to see the demand for—and the impact of—edge computing solutions grow in the coming years.

If you think an edge computing solution would be helpful for your organization, schedule a free AI assessment and we’ll show you how running AI and ML models at the edge can optimize and streamline your business.

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Chris Doty

Chris Doty

Chris has a PhD in linguistics, and has previously worked on ML projects for Amazon's Alexa. As RapidMiner's Content Marketing Manager, he works to evangelize for the power of AI and ML to upskill and empower people in a changing world. When he isn't working, he enjoys learning languages and drawing.