Why both cloud and edge computing are essential to IoT

What is a distributed computing framework?

A distributed computing framework is a data processing approach that forgoes the practice of processing all of a business’ data in one place — e.g., all in the cloud or all at the edge — in favor of distributing the load across multiple locations. Here, it’s important to dispel the commonly held view that edge computing is singular — meaning a business only has one edge. In reality, companies can and often do have more than one. The edge is simply the point of data generation, so anywhere that happens is effectively an edge.

The simplest distributed computing framework involves three layers: the cloud, the site and the individual equipment in that site, but can be subdivided into even more layers depending on the environment. These separate layers allow industrial businesses to process and manage their data wherever it makes the most sense for their operation and objectives, whether that’s in the cloud or at one of the edges.

A distributed computing framework in a factory

A large deployment is a great environment to examine this framework in action, such as a factory with hundreds of pieces of equipment. Each piece of equipment in the factory is considered an edge endpoint because they each generate data. The factory itself constitutes an edge aggregation point, as it consolidates data from all enclosed equipment. The factory then would have processing ability in the cloud, which would be saved for instances when the business has something specific to report. Cloud computing also becomes especially helpful when a business has multiple factory sites.

In this example, the business could first compile data generated from the equipment on the factory floor before sending it to the cloud. Adding this step helps prevent a cluttered data repository, which often results from sending information from hundreds of pieces of equipment straight to the cloud. Incorporating edge and cloud computing into a factory can offer several benefits, but what would happen if the factory were to use only edge or cloud computing?

• Cloud computing only: In traditional IoT architectures, all collected data is transported, combined and processed in a central data store. This has worked well in instances where only data collection is necessary, but for businesses that need to analyze information from each individual piece of equipment, this approach is no longer viable. Relying solely on cloud computing for some of these larger deployments — such as the factory example — would make it very difficult to react to the data generated on the equipment quickly enough to have a positive business impact. In fact, these kinds of delays can make a huge difference in scenarios that involve safety and quality. Including edge computing in a distributed framework allows businesses to move faster than they would if the data had to travel to the cloud and back, which opens the door for real-time analysis right on the equipment itself.
• Edge computing only: Alternatively, edge devices only process data that is locally collected and on a short-term basis, meaning a factory relying exclusively on edge computing would lack the ability to get a full view of their operation and easily store data for identifying trends over time. Locally collected data provides a great picture of what is happening at the site and with the equipment, but not as they relate to each other. To get this higher level of analytics without the cloud, the business would have to manually combine all factory data, which would be inefficient and time-consuming.

Why both the cloud and the edge are essential for IoT

An industrial business that uses both cloud and edge computing for its IoT deployments will not only be able to take advantage of the low-latency and device responsiveness that comes with edge computing, but it will also benefit from the scalability, cost effectiveness and low maintenance of cloud computing. A multi-tiered approach fuses the strengths of both types of computing instead of picking one over the other. For example, a manufacturer of heavy-duty trucks might use edge computing to predict when individual trucks need maintenance, but can turn to cloud computing to make decisions about the fleet as a whole. Data about the types of repairs implemented and exact time spent on repairs can be stored in the cloud to help mechanics eliminate unnecessary diagnostics or steps for future repair situations.

The IoT landscape continues to change, introducing more devices every day, and with them, more data that businesses need to process and manage. No matter where an organization falls on the edge-to-cloud scale, it’s important that they choose a computing approach that best fits their business needs in order to gain a competitive advantage. Businesses that perform analytics both at the edge and in the cloud can use real-time data to make faster, more accurate decisions that create real operational value, such as minimizing costs and maximizing performance.

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