Moving computing closer to the data source and away from the cloud is more than just a buzzword in today's technology—many enterprises are looking for ways to reduce latency, improve efficiency, and get better results by implementing edge computing.
Edge computing is moving data processing closer to the source that creates the data and away from the cloud in an attempt to optimize systems. We're still firmly in the midst of the cloud computing era, with our devices accessing centralized services like Office 365, Dropbox, and Slack offered by cloud providers including Amazon, Google, IBM, and Microsoft. However, there are several advantages to moving some data processing operations closer to where they are created, without having to send them to the cloud.
It is important that edge computing is discussed not as a replacement for the cloud, but as a decentralized extension or addition to it. And edge computing is growing: by 2025, it is predicted that 75% of enterprise data will be created and processed outside the cloud.
Internet of Things (IoT) devices generate an extraordinary amount of data, but not all of it is critical. However, when data is needed for real-time decision making, it becomes essential that the data can be processed without latency (the time it takes to send the data and receive a response). One of the benefits of edge computing is reduced latency. The huge volumes of data generated by the IoT require a different approach to their processing. While you may be able to wait a few seconds for your digital assistant to go to the cloud to check the prediction before sending a response, you're likely to fail if your autonomous agent needs to wait for a response from the cloud before accepting action decisions. The closer the processing takes place, as in the case of edge computing, the smaller the delay in response time.
Another benefit of edge computing that many cite is increased security, since data does not have to travel over the network. On the other hand, the data is distributed over a single center. If the data center is compromised, local data may still be safe. In addition, with the help of the latest calculations, the possibility of advanced security management using hardware and software appears.
Microsoft is developing Azure Sphere, which combines a cloud service, a managed Linux OS, and a certified microcontroller as a microcontroller security solution. With the return to on-premise data processing, security should become a top priority in new product development.
There are also significant bandwidth savings due to edge computing. As you scale, it becomes more and more difficult to move all the information to the cloud - one device versus 100 streaming data in the cloud have different bandwidth values. If you do not have an Internet connection, access to the cloud is not available. Edge computing solutions allow you to store some functions and data locally and send only some of them to the cloud.
Examples of limit calculations
Autonomous vehicles work with edge computing in many ways to reduce latency and avoid the bandwidth issues that would occur if these vehicles relied on the cloud for all functions.
IoT sensors are critical for safety monitoring, such as in oil and gas production. In cases of equipment failure or maintenance needs that require immediate attention, there is an advantage in using modern computing that allows data analysis and reporting in real time.
Many modern sensors and IoT devices are used in today's cities to help with traffic management. The data may be processed on local equipment, which may also output non-essential information. By sending some information to the cloud, operating and storage costs are reduced.
Power usage and management is also a great place to deploy modern computing. With smart meters that track usage and identify opportunities to fine-tune energy consumption, edge computing enables real-time action.
Edge computing can empower retailers to deliver the best in-store customer experience.