Edge computing has emerged as a viable and vital architecture that enables distributed computing by deploying compute and storage resources closer to the data source, ideally in the same physical location.
In general, distributed computing models are not novel, and concepts such as remote offices, branch offices, data center colocation, and cloud computing have a long and illustrious history.
Computing tasks necessitate appropriate architectures, and an architecture that suits one type of computing task may not be compatible with all types of computing tasks.
Ultimately, edge computing is the area where all the storage resources and deployment of computing are being processed.
It thus preferably locates compute and storage at the same network edge location as the data source.
A small enclosure with several servers and some storage, for example, could be installed atop a wind turbine to collect and process data generated by sensors within the turbine itself.
As another example of this technology, a railway station might house a small amount of computing and storage to collect and process a plethora of track and rail traffic sensor data.
The outcomes of such processing can then be sent to another data center for human review, archiving, and merging with other data results for broader analytics.
Edge computing places storage and servers where the data is, often requiring only a partial rack of equipment to operate on the remote LAN to collect and process the data locally.
In many cases, computing equipment is housed in shielded or hardened enclosures to protect it from extremes of temperature, moisture, and other environmental conditions.
Processing frequently entails normalizing and analyzing the data stream in search of business intelligence, and only the results of the analysis are returned to the primary data center. But how does edge computing work?
Edge computing is a game of location. All systems of edge computing depend upon the location.
In classic enterprise computing, data is generated at a client endpoint, such as a user’s computer. Such data is transferred across a WAN, such as the internet, and into the corporate LAN, where it is stored and processed by an enterprise application.
The results of that work are then communicated back to the client endpoint. For most common business applications, this is still a tried-and-true method of client-server computing.
The growth of traditional data center infrastructure to accommodate it is increasing day by day.
Due to the large number of devices connected to the internet server and the amount of data generated by these devices and used by organizations,
According to resources in the year 2024-2025, the percentage is increased by 75 of data generated by enterprises will be generated outside of centralized data centers.
The possibility of moving so much data in situations that are often time- or disruption-sensitive places an enormous strain on the global internet, which is frequently clogged up and interrupted.
Information systems developers have changed their focus from the central data center to the logical edge of the infrastructure, relocating storage and computing resources from the data center to where the data is generated.
The principle is simple: if you can’t move the data closer to the data center, move the data center closer to the data.
Edge computing is not a new idea; it is based on decades-old ideas of remote computing, such as remote offices and branch offices, to make the working environment more efficient with computing resources in the desired location rather than dependent upon a single location.
It is critical to carefully consider hardware and software options. Adlink Technology, Cisco, Amazon, Dell EMC, and HPE are among the many vendors in the edge computing space. Cost, performance, features, interoperability, and support must all be considered for each product offering.
Tools should provide comprehensive visibility and control over the remote edge environment from a software standpoint.