An era characterized by the proliferation of data has led to surging issues in handling latency and insights for some applications.
Enterprises are seeking the next version of the cloud to address these concerns. Edge computing has subsequently emerged with the potential to address these issues—especially as we approach the IoT-ridden world which demands low latency.
Edge computing is a concept where computation is closer to the source of data. It marks a paradigm shift – addressing the challenges of privacy, data control, and latency that enterprises have faced while running centralized workloads on the cloud. It brings applications, high-bandwidth content, and control functions closer to the end-user while aiming to reduce latency for compute-intensive real-time applications.
‘Edge’ can be defined as a block of compute and network resources in the path between data source and cloud data centers. Each of the edge units has their own group of resources in the form of compute, network, and storage among others. They may be called edge gateways. Alternately, the device could act as the edge itself, feeding into the central or core. The primary job of the edge devices in edge computing architecture (but not limited to) is to handle network switching, load balancing, security, routing, and audit trail as is, but doubling up for processing real-time data as and when required. Besides, a cluster of devices would be the local digestion point for the data originating from a variety of sources. The frequently used data will be analyzed, processed, and stored immediately by the edge layer and the less frequently used data will be sent to the cloud for further storage or processing.
At the edge, the source can not only request service and content from the cloud but also perform the computing tasks on the cloud. Edge can perform compute offloading, data storage, caching, and processing, as well as distribute requests and delivery service from the cloud to the user. In a nutshell, it allows computation to be carried out at the edge of the network, on upstream data on behalf of applications such as IoT and downstream data, on behalf of the cloud.
Furthermore, edge computing significantly decreases the network traffic, with a significant reduction in data volume. This, in turn, improves the QoS and latency issues, thereby reducing network costs in some cases. This distributed computing is a step ahead in limiting the points of failure, with enhanced security at each node through which the data passes. Hardware limitations are expected to be erased in a centralized processing system, with less power consumption as a bonus.
Cloud offloading is possible if the information is processed at the source itself, making edge uniquely well suited for the IoT applications. Edge has extensive application when data needs to be processed at the source—for instance, in the case of smart cities, smart homes, intelligent transport, and ATMs to name a few. It is expected to change the way information is stored and processed in the near future.
Types of edge devices include localized devices, local and regional data centers which provide the deployment speed and capacity in-line with application demands. As applications such as IoT demand only a minuscule amount of latency, edge cloud computing is expected to gain traction as it aims to solve these problems effectively.