- Edge computing shifts computing and storage resources from central data centres to locations closer to where data is generated, reducing latency and bandwidth usage.
- It enables real-time data processing and analysis at the source, real-time analytics, and enhanced IoT capabilities in various sectors including retail, utilities, and autonomous vehicles.
Edge computing revolutionises how data is processed by moving computing resources closer to where data is generated, minimising latency and enhancing real-time processing capabilities. This approach empowers industries to leverage immediate insights and responsiveness, from predictive maintenance in manufacturing to real-time analytics in autonomous vehicles.
Also read: Why edge computing is essential in today’s digital landscape?
Also read: Edge computing vs. cloud computing: Essential contrasts
What is edge computing
Edge computing is an IT architecture where client data undergoes processing at the network periphery, near its origin. This approach relocates storage and computing resources from central data centres to closer proximity with the data source. Rather than sending raw data to a central facility for processing and analysis, computations are performed at the data generation point. Only the outcomes of these computations, like real-time business insights or equipment maintenance forecasts, are transmitted back to the central data centre for review and human interaction.
How does it work
IT architects are shifting their focus from central data centres to the logical edge of the infrastructure—taking storage and computing resources from the data centre and moving those resources to the point where data is generated.
The principle is simple: if you can’t get the data close to the data centre, get the data centre close to the data. The concept of edge computing is not new, and is rooted in the decades-old idea that remote computing—such as remote offices and branch offices—is more reliable and efficient when it comes to placing computing resources where they’re needed than relying on a single central location.
Edge computing places storage and servers where the data is, often requiring only a portion of the rack equipment to run on a remote LAN to collect and process data locally. In many cases, computing equipment is deployed in shielded or hardened enclosures to protect the equipment from extreme temperatures, humidity, and other environmental conditions. Processing typically involves normalising and analysing data streams to look for business intelligence, and only the results of the analysis are sent back to the main data centre.
In some retail environments, video surveillance of showrooms might be combined with actual sales data to determine the most ideal product configuration or consumer demand. Other cases involve predictive analytics that can guide equipment maintenance and repairs before actual defects or failures occur. Still others are often integrated with utilities, such as water treatment or power generation, to ensure equipment is functioning properly and output quality is maintained.
Why it’s important
Take the rise of self-driving cars. They will rely on intelligent traffic control signals. Cars and traffic control will need to generate, analyse, and exchange data in real time. Multiply this requirement by a large number of self-driving cars, and the scope of the potential problems becomes clearer. This requires fast and responsive networks. Edge computing becomes a viable and important architecture that enables distributed computing, deploying computing and storage resources closer to the data source (ideally in the same physical location).
