Edge computing is a distributed information technology (IT) architecture in which client data is processed at the periphery of the network, as close to the originating source as possible.
In simplest terms, edge computing moves some portion of storage and compute resources out of the central data center and closer to the source of the data itself. Rather than transmitting raw data to a central data center for processing and analysis, that work is instead performed where the data is actually generated — whether that’s a retail store, a factory floor, a sprawling utility or across a smart city. Only the result of that computing work at the edge, such as real-time business insights, equipment maintenance predictions or other actionable answers, is sent back to the main data center for review and other human interactions.
Thus, edge computing is reshaping IT and business computing. Take a comprehensive look at what edge computing is, how it works, the influence of the cloud, edge use cases, trade offs and implementation considerations.
How does Edge Computing Works?
The edge computing concept is not entirely new; it dates back to decades associated with remote computing. For example, branch offices and remote workplaces placed computing resources at a location where they can reap maximum benefits instead of relying on a central location.
In traditional computing, where data was produced at the client-side (like a user’s PC), it moved across the internet to corporate LAN to store data and process it using an enterprise app. Next, the output is sent back, traveling through the internet, to reach the client’s device.
Now, modern IT architects have moved from the concept of centralized data centers and embraced the edge infrastructure. Here, the computing and storage resources are moved from a data center to the location where the user generates the data (or the data source).
The edge computing process involves data normalization and analysis to find business intelligence, sending only the relevant data after analysis to the main data center. Furthermore, business intelligence here can mean:
- Video surveillance in retail shops
- Sales data
- Predictive analytics for equipment repair and maintenance
- Power generation,
- Maintaining product quality,
- Ensure proper device functioning and more.
Edge Computing Vs Cloud Computing Vs Fog Computing
Edge. Edge computing is the deployment of computing and storage resources at the location where data is produced. This ideally puts compute and storage at the same point as the data source at the network edge. For example, a small enclosure with several servers and some storage might be installed atop a wind turbine to collect and process data produced by sensors within the turbine itself. As another example, a railway station might place a modest amount of compute and storage within the station to collect and process myriad track and rail traffic sensor data. The results of any such processing can then be sent back to another data center for human review, archiving and to be merged with other data results for broader analytics.
Cloud. Cloud computing is a huge, highly scalable deployment of compute and storage resources at one of several distributed global locations (regions). Cloud providers also incorporate an assortment of pre-packaged services for IoT operations, making the cloud a preferred centralized platform for IoT deployments. But even though cloud computing offers far more than enough resources and services to tackle complex analytics, the closest regional cloud facility can still be hundreds of miles from the point where data is collected, and connections rely on the same temperamental internet connectivity that supports traditional data centers. In practice, cloud computing is an alternative — or sometimes a complement — to traditional data centers. The cloud can get centralized computing much closer to a data source, but not at the network edge.
Fog. But the choice of computing and storage deployment isn’t limited to the cloud or the edge. A cloud data center might be too far away, but the edge deployment might simply be too resource-limited, or physically scattered or distributed, to make strict edge computing practical. In this case, the notion of fog computing can help. Fog computing typically takes a step back and puts compute and storage resources “within” the data, but not necessarily “at” the data.
Fog computing environments can produce bewildering amounts of sensor or IoT data generated across expansive physical areas that are just too large to define an edge.
Edge Computing with IOT and 5G possibilities
Edge computing, IoT and 5G possibilities
Edge computing continues to evolve, using new technologies and practices to enhance its capabilities and performance. Perhaps the most noteworthy trend is edge availability, and edge services are expected to become available worldwide by 2028. Where edge computing is often situation-specific today, the technology is expected to become more ubiquitous and shift the way that the internet is used, bringing more abstraction and potential use cases for edge technology.
This can be seen in the proliferation of computing, storage, and network appliance products specifically designed for edge computing. More multivendor partnerships will enable better product interoperability and flexibility at the edge. An example includes a partnership between AWS and Verizon to bring better connectivity to the edge. Wireless communication technologies, such as 5G and Wi-Fi 6, will also affect edge deployments and utilization in the coming years, enabling virtualization and automation capabilities that have yet to be explored, such as better vehicle autonomy and workload migrations to the edge, while making wireless networks more flexible and cost-effective.