What is edge computing and how it is reshaping the future
The growth of various sectors such as automotive, healthcare, agriculture, and even military applications is significantly driven by advancements in edge computing infrastructure. But what exactly is edge computing? At its core, edge computing involves processing data closer to its source—at the edge of the network—rather than transmitting everything to a central cloud service.
Over the past decade, the demand for rapid responses in real-time data processing has surged. From autonomous vehicles to security applications and AI deployments, modern edge computing promises instantaneous feedback by minimizing round-trip processing times to as little as a few milliseconds.
This distributed computing approach prioritizes speed, bandwidth efficiency, and reduced dependence on cloud infrastructure. Despite facing challenges related to computational power, there has been substantial progress in developing both the hardware and software components, ranging from specialized hardware to mature software solutions and edge orchestration platforms designed to manage thousands of deployed edge devices.
The cost of moving data
In conventional data processing approaches like cloud computing, the data is transferred to the cloud via the internet or, in some cases, via a wide area network. Generally speaking, this approach works well for many applications but has a cost in terms of bandwidth needs.
In some cases, the cost comes with moving data out of the cloud (known as egress charges); in other cases, the cost is in terms of the time that it takes to move data into the cloud. Processing data at the edge can offer an efficient system that results in lower latency while avoiding costs for bandwidth and data storage.
For many types of AI deployments, for example, businesses have realized the limitations of centralized cloud infrastructure, convincing them to shift to edge processing that lets them worry less about bandwidth and latency constraints. Networking technologies such as 5G, in tandem with edge computing, could enable many AI deployments in remote locations.
One of the key benefits of edge computing for AI is lower latency. AI-based applications rely on high-accuracy models, and a quicker data feedback loop can be used to improve the accuracy of AI models. After using data, it can be discarded rather than stored, resulting in another cost-saving benefit.
This highlights another benefit of edge computing: data sovereignty. As data collected at the source is locally processed, this allows organizations to keep the data well protected within a defined geographic location as well as a specific, localized ecosystem.
