In today’s data-driven world, where businesses rely on real-time information to make critical decisions, edge computing has become the technology of choice. By moving some portion of compute, and storage resources from a central data center and closer to the data source, latency issues, bandwidth limitations, and network disruptions are greatly minimized.
With edge computing, data produced in a factory floor or retail store are processed and analyzed at the network’s edge and within the premises. Since data doesn’t travel across networks, speed is one obvious advantage. This translates to instant analysis of data, faster response by site personnel, and real-time decision-making.
How Edge Computing Works
Edge computing brings computing power closer to the data source, where sensors and other data capturing instruments are located. The entire edge computing process takes place inside intelligent devices that speed up the processing of the various data collected before the devices connect to the IoT.
The goal of edge computing is to boost efficiency. Instead of sending all the data collected by sensors to the enterprise applications for processing, edge devices do the computing and only send important data for further analysis or storage. This is possible thanks to edge AI, i.e., artificial intelligence at the edge.
After the edge devices do the computation of the data with the help of edge AI, these devices group the data collected or results obtained into different categories. The three basic categories are:
- Data that doesn’t need further action and shouldn’t be stored or transmitted to enterprise applications.
- Data that should be retained for further analysis or record keeping.
- Data that requires an immediate response.
The work of edge computing is to discriminate between these data sets and identify the level of response and the action required, then act on it accordingly.
Depending on the compute power of the edge device and the complexity of the data collected, the device may work on the outlier data and provide a real-time response. Or send it to the enterprise application for further analysis in real-time with immediate retrieval of the results. Since only the important and urgent data sets are sent over the network, there’s reduced bandwidth requirement. This results in substantial cost savings, especially with wireless cellular networks.
Why Edge Computing?
There are several reasons why edge computing is winning the popularity battle in the enterprise computing world. Digital transformation initiatives, from robotics & advanced automation to AI and data analytics, all have one thing in common – they are largely data-dependent. Most industries that leverage these technologies are also time-sensitive, meaning the data they produce becomes irrelevant in a matter of minutes, if not seconds.
The large amounts of data currently produced by IoT devices strain a shared computing 8model due to system congestion and network disruption. This results in huge financial losses, injuries, and costly damages for time and disruption-sensitive applications. The attractiveness of edge computing often narrows down to the three network challenges it seeks to solve. These are:
Latency – a lag in the communication between devices and network delays decision-making in time-sensitive applications. Edge computing solves this problem using a more distributed network, which ensures there’s no disconnect in real-time information transfer and processing. This gives a more reliable and consistent network.
Bandwidth – Every network has a limited bandwidth, especially wireless communications. Edge computing solves bandwidth limitations by processing immense volumes of data near the network’s edge then only sending the most relevant information through the network. This minimizes the volume of data that requires a cellular connection.
Data Compliance and Governance – organizations that handle sensitive data, are subject to data regulations of various countries. By processing this set of data near the source, these companies can keep the sensitive customer/employee data within their borders, hence ensuring compliance.
Edge Computing Use Cases
Over the years, edge data centers have found several use cases across industries, thanks to rapid tech adoption and the benefits of processing data at the network edge. Ideally, any application that requires moving large amounts of data to a centralized data center before retrieving the result and insights could benefit a lot from edge computing. Below are the different ways several industries use edge computing in their day-to-day operations:
Transportation – autonomous vehicles produce around 5 to 20 terabytes of data daily from information about speed, location, traffic conditions, road conditions, etc. This data must be organized, processed, and analyzed in real-time, and insights fed into the system while the vehicle is on the road. This time-sensitive application requires accurate, reliable, and consistent onboard computing.
Manufacturing – several manufacturers now deploy edge computing to monitor manufacturing processes and enable real-time analytics. By coupling this with machine learning and AI, edge computing can help streamline manufacturing processes with real-time insights, predictive analytics, and more.
Farming – indoor farming relies on different sensors that collect a wide range of data that must be processed and analyzed to gain insights into the crops’ health, weather conditions, nutrient density, etc. Edge computing makes this data processing and insights generation faster, hence faster response and decision making.
The other areas where edge computing has been adopted include healthcare facilities to help patients avoid health issues in real-time and retail to optimize vendor ordering and predict sales.
Edge Computing Challenges
Edge computing isn’t without its challenges, and some of the common ones revolve around security and data lifecycles. Applications that rely on IoT devices are vulnerable to data breaches, which could comprise security at the edge. As far as data lifecycles are concerned, the challenge comes in with the large amount of data stored at the network’s edge. A ton of useless data may take up critical space; hence businesses should keenly choose the data to keep and discard.
Edge computing also relies on some level of connectivity, and the typical network limitations are another cause for concern. It’s, therefore, necessary to plan for connectivity problems and design an edge computing deployment that can accommodate common networking issues.
Implementing Edge Computing
Regardless of the industry you are in, edge computing comes with several benefits, but only if it’s designed well and deployed to solve the challenges common with centralized data centers. To get the most from your investment, you want to work with a reputed edge computing company or an expert IT consultant to guide you on the best way forward.
Article Source: What Is Edge Computing?