Let’s unpack that.
Have you ever been excited to see a certain store opening a location nearby? Finally, right? Now you can get those [insert favorite product] without the 45-minute drive.
That’s really what edge computing is all about. Right now your cloud-based data has to travel a long way from the central location where all the servers live. It may be rocketing at the speed of light, but it’s sure to hit traffic along the way, not to mention increase the likelihood of outages by having that much road to travel. And, while “cloud computing” may seem like a limitless resource, it’s filling up faster every day.
Edge computing involves processing at or near the source, using edge servers on the fringe of the network.
How does edge computing work?
Having an edge gateway right in your home, office, or even in the palm of your hand, allows control of how much data needs to be sent to the centralized cloud vs. how much can be processed locally. Sending less to the cloud means reduced network congestion, faster speeds, and real-time processing. As one example, this is important for artificial intelligence that relies on instantaneously “learning” in order to provide its unique service as effectively as possible. If you bog down this process in a thick muck of less essential data transmission, you slow down all the corollary functions and benefits.
If you’re doing research on edge computing, you’re going to bump into the topic of “IoT devices” pretty quickly. IoT stands for Internet of Things, and refers to devices that are equipped to operate through the internet. Think of your favorite smart technologies that make life easier and more convenient, from Alexa to security cameras.
Why is edge computing important?
Increased security with edge technology
The big cloud players like Amazon, Apple, and Microsoft have gone to great lengths to secure the data they manage. After all, they’re trust-based service providers, and they know how critical it is to protect the information you store on their servers as part of the user experience.
It’s no secret though that local networks (with the right provisions in place) offer the best security. That’s why many industries that deal with lots of personal information use closed networks and on-site servers; their data is too sensitive to be trusted on the open roads of cloud-based transmission.
Edge computing systems work much like a closed network, keeping your information closer to you. When you purchase the IoT device, you’re also purchasing the security infrastructure, like Apple’s biometric features, for example. By decentralizing the security protocols and software you reduce reliance on the cloud.
Bandwidth is the amount of data you can send within a network inside a specific period of time.
Or, sticking with our highway analogy, bandwidth refers to how much traffic the road is designed to handle.
It stands to reason then that by lessening the amount of information being sent from your network to the cloud, the less chance you have of running into bandwidth issues that slow you down. Using the edge computing model lessens traffic by keeping it local, ensuring that the main arteries stay wide open for essential data transmission.
Few things are more aggravating than network latency, or lag. Especially in our modern world that relies on smooth, consistent communication across our networks, latency can be annoying at best and detrimental at worst.
It’s caused by increased time being required for the packets of information we send to be received, decoded, processed, and implemented. And the greatest cause of latency? You guessed it: distance.
Once again, when we rely less on traditional data centers and more on local edge devices and networking, latency is cut way down.
What are the challenges of edge computing?
But what about larger-scale edge computing for your business or organization? There are several key challenges, but it’s important to weigh them against the benefits. It also is helpful to talk with an experienced IT solutions provider that can help you navigate your specific needs and options.
The cloud isn’t an ethereal expanse of limitless space. It’s a collection of servers in a centralized location. So, if you aren’t relying on that cloud, you need to leverage your own data center. This could be on-premise, or a shared edge data center at a different (but at least somewhat close) location. The closer the center, the faster your transmission speeds.
If you choose an on-premise network, these increased speeds come at an increased cost. Plus, you need to make sure you have a team in place to maintain the hardware and software.
As your network needs grow, so will the need for a larger team, more equipment, more storage, network architecture, etc. There are always substantial costs to factor in when you’re considering new network technology, and edge computing solutions are no different.
We listed security as an edge computing benefit above, but it also can be a liability if it’s not managed properly. On an enterprise scale, it can become much more difficult to identify and pinpoint security vulnerabilities and breaches.
The differences between edge, cloud, and fog computing
Data management and storage is performed off-site. There are huge benefits, but also drawbacks when it comes to bandwidth limitations and latency.
You know the drill on this one. Edge computing allows your data to be processed closer to home, increasing speed and real-time insights while decreasing bandwidth and latency.
Fog computing is all about quickly processing vast amounts of information, especially in scenarios where instantaneous data collection and processing is key. Think about autonomous, self-driving vehicles, for example. Sensors and cameras collect huge data loads, and it’s critical that the data is instantly processed and converted to specific commands. It sees someone slowing down, and the brakes are applied.
This is made possible by sifting through that data and making a smart decision about what needs to be sent to the cloud, and what doesn’t. By streamlining and minimizing the amount of information sent, the speed increases and latency decreases proportionately.
Regardless of what you’re monitoring at an enterprise scale, fog computing’s network architecture allows you to efficiently analyze and “sort” that data, only sending the essentials through to the larger cloud network.
Edge computing use cases
- You leverage AI, and need to reduce latency for the most effective “ machine learning” and actuation
- You routinely run into latency and bandwidth limitations, especially due to large amounts of data processing and storage
- Your IoT devices are held back by insufficient network connectivity
Have more questions about edge computing and your network options? Contact us at Teltek! We specialize in IT services and solutions, helping business and organizations implement the best systems for their specific needs.