Source | 51CTO
What is edge computing? How does it differ from traditional cloud computing services? When might edge computing be the right choice for businesses?
Edge computing is a form of cloud computing, but unlike traditional cloud computing architectures that centralize computing and storage in a single data center, edge computing pushes computing or data processing capabilities to edge devices for processing. Therefore, only the results of data processing need to be transmitted over the network. In some cases, this provides precise results while consuming less network bandwidth.
The Internet of Things (IoT) is the most common use case for edge computing. IoT uses edge sensors to collect data from geographically dispersed areas, which are connected via data networks, often utilizing WAN (Wide Area Network) technologies. In traditional IoT architectures, all collected sensor data is transmitted to a central repository for integrated processing. This works well only when data needs to be accumulated for collection and analysis. But what if there is no need to combine the data to achieve the desired results? What if each IoT sensor only needs to process the collected data and send results when specific requirements are met?
People are beginning to see the benefits of edge computing. If there is no real need to collect all data in a centralized cloud computing repository, it makes no sense to waste expensive bandwidth transmitting it. In fact, a fully effective IoT design might be one where sensors only connect to the cloud when they need to report important information. This design reduces the networking costs of IoT by utilizing technologies such as cellular-based systems that use a pay-per-thousand model, rather than the more expensive always-on connection.
One point to consider with edge computing is that since data is not stored long-term and will eventually be deleted, it is not conducive to big data analysis. Remember, edge devices only provide results from processing locally collected data. In most cases, the collected data can only be discarded. Therefore, if a business’s IoT project needs to store all collected data for cumulative analytical decision-making, then edge computing is not suitable.
That said, edge computing is very suitable for IoT deployments that adopt localized and batch processing. An example in this regard might be tallying sales and inventory at remote retail stores and then sending the computed results back to corporate headquarters on a scheduled daily basis. Businesses may not need real-time data for every transaction. Instead, this data can be processed locally and a simple report generated at closing time.
This approach also helps significantly reduce the storage overflow issues many businesses face with traditional cloud computing architectures. Real-time data flowing into cloud storage can accumulate rapidly. Often, this data ultimately becomes useless. However, organizations are often concerned that once data enters the cloud, it will be deleted, and they can easily waste thousands of dollars storing data that is almost certainly not going to be used. Edge computing can address this issue by only sending useful post-processed information to the cloud. This way, businesses only need to store what they actually need.
As the concept of business digitization deepens, the edge computing model will become a key component of many IoT plans. Both network and storage costs account for a significant portion of distributed IoT projects, so reducing data transmission and storage requirements is attractive in many use cases. If deployed correctly, edge computing will achieve certain IoT projects at a much lower cost than traditional cloud computing methods.
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