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.
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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.
Opportunities, considerations of edge cloud computing
Even though the tools and architectures needed to build out the cloud at the edge are still nascent, opportunities abound when considering future edge cloud computational capabilities as applied to distributed node edge sites. Organizations can use IoT edge cloud computing for machinery analytics processing, execution in industrial IoT and ultrafast telematics processing in metro areas with a high number of autonomous vehicles.
Developers and IT professionals should plan now for both opportunities presented and potential constraints. The IoT edge cloud offers consistent OS deployment and application integration among edge sites to increase regularity of service delivery. Organizations can also use edge clouds to scale globally and remotely with an increased distribution of cloud nodes where clusters of users will benefit most. IT experts must be aware of edge cloud network limitations in connectivity and reliability to consistently meet low-latency IoT application requirements, such as those in augmented or virtual reality. The IoT edge cloud also requires hardware consistency and can be cost-prohibitive if not fulfilled.
Additional areas where decreased latency, high data processing and increased cost-efficiency of edge cloud data center distribution can have sizable effects include:
- clusters of users with high streaming of video on mobile devices;
- within cities or near highways to administer public safety applications at scale;
- manufacturers or logistics centers that utilize robotics, automation and data analytics; and
- hospitals, clinics and other healthcare facilities that require immediacy in IoT service delivery.
In these situations, edge sites may even predict data processing demands based on historical data analysis and predictive analytics capabilities, connecting to other nodes on the edge cloud to respond to variable processing needs.
IoT edge cloud success depends on laying out the groundwork
For the IoT edge cloud to reach its full potential, organizations must have technologies in place that solve not only intelligent edge processing needs, but also network infrastructure limitations.
AI chips within IoT devices process more on the edge directly. That, combined with a shorter distance to an edge site, equals faster replies or actions while also saving on bandwidth and server costs. For example, AI chips in devices provide smarter and quicker answers to Alexa queries or AI-driven photo editing.
Progressive web applications offer save-as-you-go changes stored locally, even if offline, that sync with the cloud when reconnected. Edge sites can use the applications to balance the workload between processors with more frequent and flexible synchronization.
5G network connectivity is critical to cloud edge sites operating and communicating at the speeds necessary to maintain low latency as compared to edge computing alone. For example, AWS Wavelength and Verizon 5G have unveiled products that developers can use to build apps directly at the edge with access to the density and capacity of a 5G network.