Edge computing seems to be the talk of the town. Unlike the rest of the world, everyone is eager to talk about this concept as its Global revenue is estimated to reach $378 billion by 2028. However, the path to reach this milestone is not as glamorous as your favorite social media influencer might tell you.
Table of Contents
Security: Your Biggest Headache
Security is the most common concern for 23% of enterprises considering Edge Computing deployment. Securing the network is not as simple as having one tank of a firewall device at the edge, and each edge device intake assumes each device functions as a separate door to your house.
The distributed nature modern security paradigms feel primitive. A myriad of security risks such as unauthorized device access, real-time data breach threats while being transferred to a protected environment, and the struggle of efficient mass device patching exist.
Infrastructure Reality Check
The real kicker comes here. Patchy 5G coverage is especially stunting growth in certain regions. As is the case for most edge devices, they are deployed in areas where data network coverage is as reliable as the accuracy of a guess.
The Cloud Google blog mentions the term intermittent connectivity, which refers to the need to build systems able to perform autonomously in offline mode, requiring no dependency on other systems to perform tasks. The diverse geography of Europe poses specific challenges for widely placed edge nodes.
Resource Constraints That Bite
Compared to cloud infrastructures, edge devices have much less processing power and capabilities. It becomes much like walking a tightrope when balancing performance with power, especially when combined with growing workloads and devices.
The data management issue at hand translates to operating at edge locations with volumetric data and limited storage. Frontier. Optimization first solves with lightweight algorithms, selective data filtering, and hybrid algorithm systems that decide when to communicate with cloud storage.
The Talent Crunch
A lack of qualified professionals strained one third of all organizations. This crowd is not your “average” IT professionals. Data engineers who work with edge-specific use cases are much harder to find. The company ends up spending far more training and retaining talent, inevitably making it a high cost.
What’s Coming Next
Some of the more pressing ones are the deployment of AI models on devices with less processing power, and managing data compliance across multiple regions. These are some of the pressing concerns. In addition, energy and carbon footprints are on the rise, making the focus on sustainability much harder.
The Bottom Line
Deployment of edge infrastructure is not a matter of simply buying a new device. The focus is on tackling the multitude of issues within secutity, infrastructure, limited resources, and availailabilty of skilled personnel. The tools are on the edge of harder to compay at, but respond much better, offer gleaming gains on latency, and bandwidth.
What you can do is think small, invest in the correct skill set, and alongside, try going hybrid. After all, in this business, last generation tech is of no use if you don’t implement it properly, and if there’s no risk of you losing your mind or data in the process.
I’m a technology writer with a passion for AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and curious. and encourage participation I continue to research innovation and technology. Let’s connect and talk technology! LinkedIn for more insights and collaboration opportunities: