Organizations are shifting IoT deployments from cloud computing to edge computing, but a newer option — the edge cloud — has emerged to bring the cloud to the edge.
Cloud computing and edge computing are well known for their distinct advantages in IoT based on use case, data processing and storage needs. However, the combination of the two computing infrastructures offers greater flexibility to developers and lower latency to consumers while also maintaining data privacy standards.
Enter the concept of cloud at the edge, a term gaining traction among behemoth cloud service providers, network operators and IoT developers.
What does edge cloud mean?
To understand cloud at the edge, otherwise known as edge cloud, tech experts must also define the two terms it combines and the differences between them.
Cloud computing refers to the storage and processing of data in a community, private, public or hybrid cloud data center in a centralized location. For IoT applications, processing all data in the cloud introduces greater latency to complete an action.
Edge computing refers to the process of real-time data storage and computation on the device or data source, rather than sending it to a distant data center. For IoT devices, this dramatically decreases lag and saves bandwidth. The centralized cloud still serves as the main storage facility for large amounts of data and additional processing. The IoT device where edge processing occurs acts as a node.
Edge cloud refers to the decentralization of the traditional, massive cloud data centers. The IoT edge cloud moves cloud storage and compute closer to the edge source while also scaling down its size. Edge sites may connect to each other or to a core cloud for additional data inputs and processing or storage capabilities, or isolated in instances of a data breach or service compromise.
Edge cloud requires additional remotely administered data centers — also referred to as edge sites — close to the end users. It also calls for a large number of edge sites at specific locations where increased compute and processing is needed beyond what can be completed at the edge in conjunction with low-latency, time-sensitive IoT tasks.