Cloud edge computing, now more commonly known as edge computing, is an IT architecture in which client data is processed as close to the source as possible. This often involves moving machine learning tools from cloud data centers to embedded systems on devices in the field, at the periphery of the network—the edge.
Performing computations at or near the source of data is faster, more efficient, and safer than even the best cloud-only computing solutions. The majority of today’s computing still takes place in the cloud, with the processing work being done in distributed data centers. As businesses today are flooded with data collected from remote employees, sensors, and IoT devices, data centers can become clogged and expensive to maintain.
Distributed computing can significantly cut the volume of data being transferred to on-premises or co-location data centers and the cloud. This almost instantly solves the beastly problem of latency, which is the time lost in data transfer, thus improving the speed of operations throughout your network. It also cuts storage costs while enhancing security.
So, in addition to reducing data-transfer delays that prevent immediate analysis, edge computing addresses several of cloud computing’s most challenging issues, including data security and privacy. By the way, because their value is widely recognized, the hardware and software required by the edge-computing revolution is available and cost effective.
Some Real-World Edge Computing Solutions
Collecting, analyzing, and processing data at the edge allows for real-time troubleshooting and response. Edge computing is what enables self-driving cars to respond instantly to an event taking place at 80 miles an hour.
It’s easy to see why onboard processing makes sense in that instance, rather than sending the information captured by an autonomous vehicle’s sensors to the cloud for analysis, but the hyperfast response delivered by edge computing technology delivers powerful benefits in a surprising number of applications.
Edge computing is in widespread use in the manufacturing industry, where factory floors and supply-chain processes are being automated. This improves production, optimizes logistics, and allows the manufacturers to build an infrastructure to manage the endless stream of data being sent and received by endpoint devices.
By gathering and analyzing data on the factory floor, and acting on the information in real time, manufacturers are reaping profound benefits. Downtime is slashed. Maintenance can be accurately predicted and implemented. Yields go up while waste goes down — as do overall costs.
Edge computing is also enabling remarkable innovations in healthcare and medicine. One of my favorite examples is an automated insulin delivery system that uses artificial pancreas sensors under the skin to detect when insulin is needed, and subcutaneous pumps to deliver it.
Four Big Benefits of Distributed Edge Computing
1. Moving Computation to the Network’s Periphery Cuts Latency
Because so much data is being structured, sorted, cleaned, and analyzed on cloud-based platforms, the massive amounts of data streaming to and from the cloud is creating a literal traffic jam. Moving computational work to the edge takes pressure off the cloud platform, automatically reducing latency.
2. Moving Data and Analysis from the Cloud to the Edge Cuts Bandwidth Costs
This may seem obvious, but because the quantity of data being produced is so vast, the scope of this benefit is actually surprising: When more data is stored, processed, and analyzed on local devices rather than in the cloud environment, your use of bandwidth plummets resulting in significant cost savings.
3. Edge Computing Can Definitely Improve Security—but not Automatically
Data is most vulnerable to being shanghaied by cybercriminals when it is in transit. Reducing the millions upon millions of data transfers between your devices and your core cloud network makes that network immediately safer. By the same token, distributing computational power to the countless devices at the edge of your network is definitely not without risk. This is a situation where you probably want to bring in an edge compute and data center services provider for help.
4. Edge-Enabled Devices Simplify Scalability
Since each device in a cloud edge computing network is more autonomous, scaling — both up and down — is much simpler. Scaling either way can be achieved by merely adding or removing devices and nodes. As edge devices become more common, and OEMs make edge capability native, scaling is becoming even easier. The flexibility provided by this benefit creates significant value.
One Big Reason to Consider Edge Now
Sometimes the market is wise, and by studying industry movements, you can make predictions and make bold, smart moves. And right now, is a moment when it’s easy to see where it is heading.
The International Data Corporation (IDC) estimates that global spending on edge hardware, software, and services will exceed $176 billion in 2022 — a 14.8% increase over last year. It is predicted to reach almost $275 billion by 2025.
Those billions of dollars will be spent, in part, to build technologies that will make edge computing an even more attractive option.
The Future Compute conference held each year by MIT Technology Review is often a good place to get a look at the most important tech trends. One of the first lines in 2022’s Future Compute agenda reads as follows: “AI, Cloud, Edge, and Security are the pillars of information technology.”
So, if you haven’t begun considering how edge computing might help your organization, now might be a great time to do so.
I hope you found this information helpful. As always, contact us anytime about your technology needs.
Until next time,