The (IoT) is an exciting topic as businesses across all industries make plans to incorporate smart devices and sensors into their business models. Because of this, each year the amount of data generated globally continues to grow.
The problem with this scenario is that total worldwide data generation is growing faster than total worldwide network capacity. We can’t move all the data to the cloud—we must push intelligence down toward the source of the data. In the coming years, we will need to move data before it hits the cloud. Fog and Edge Computing offer a solution to the swelling data dilemma by bringing processors to where the data is located, not moving data to where the processors are.
Cloud computing, like Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), and Platform-as-a-Service (PaaS), provides standard interfaces and methodology to deploying solutions in the cloud. Data is stored, managed, and processed in the cloud using a network of remote servers hosted on the Internet.
Cloud computing is extraordinary; however, there can be disadvantages. One is the cost to transfer data from the cloud, such as downloading large amounts of data; another is storage costs. Moving data takes time and money and sometimes the capacity just isn’t available.
Computing at the edge means pushing data processing out to the edge of the network, where data is generated. Any edge device like a router, sensor, or smart device can do Edge Computing. Each device has its own role in processing information. Edge Computing may have no affiliation with a cloud or a server and can exist as a standalone machine. Typically, Edge Computing devices are used in Machine-to-Machine (M2M) systems that are closed. Typical functions of Edge Computing include: data aggregation, denaturing, filtering, data scrubbing, and anomaly detection. The intent is to reduce cost and latency and control network bandwidth.
Fog colocates computing to where the data is and pushes intelligence and processing capabilities closer to where the data originates. Fog differs from Edge Computing in that fog has an association with cloud services. Data is processed and stored at a fog node and pertinent data is transmitted back. There could be multiple fog nodes between the actual sensor device and the cloud data center itself.
Fog devices perform all the actions of an Edge Computing device, but are flexible in partitioning workloads between the fog nodes and cloud data centers. Fog Computing also offers the benefits of well-defined software frameworks, making the fog and cloud transparent to the user and developer.
Both Fog and Edge Computing reduce the amount of data being sent back-and-forth between sensors and the cloud, saving time and power, and conserving network bandwidth.
Ultimately, both Fog and Edge Computing will be valuable computing solutions as IoT expands.
Tools to Get There
There are valuable tools and resources available for exploring Fog Computing, such as OpenFog Consortium, a global coalition of technology industry leaders and academic institutions working to standardize and promote Fog Computing. The partnership encourages the deployment of Fog technologies through the development of an open architecture.
Another resource is Edge X Foundry, a standard model anyone can use for Fog Computing. It’s a common open platform that helps to tie together IoT and Edge Computing, offering tools to quickly create IoT edge solutions that are flexible to changing business needs.
It seems everyone wants to implement IoT, but very few understand how IoT may affect their business. Resources such as these are a valuable way to learn more about IoT, Fog and Edge Computing, and how to use them in the most effective and efficient manner.