Digital Themes

Edge Computing

Edge computing informs many activities in daily life, from personal devices such as Smartphones, to computers monitoring traffic flow and the utility grid. What makes these devices “Smart” is their ability to process data in real time, share insights and act on them. Instead of relying on a central data center for computation and data storage, Edge computing brings these activities “close to the edge.” It involves the actual devices or locations where information is being produced and consumed, such as a user’s computer, IoT device or other edge device. Cloud computing should not be confused with edge computing, as data is processed in a traditional data center or public cloud, rather than operating “autonomously” in a device outside of the cloud. Fog computing serves as the communication point between edge computing and the cloud as a distribution network that connects edge and cloud environments.

Content delivery network (CDN) edge servers are computers at the edge of a network that provide an entry point into that network. These networks are made up of other edge devices that include routers and routing switches placed inside internet exchange points (IxPs), allowing different networks to connect and share information. The devices run within a predefined network pattern—to connect with devices outside of this established pattern, networks use edge servers (hardware devices) as a bridge to allow traffic to flow between these and other devices. Home networks supporting many connected devices all operate within a local area network (LAN), with each device connecting through the same network, enabling them to connect with one another.

Edge computing is most important for data delivery outside of centralized data centers at high bandwidth without latency issues, where information is critical. Consider, for example, the data powering autonomous vehicles outside of a central location, where data and computing power takes the place of driver inputs. The artificial intelligence (AI) and machine learning driving cars relies on data that can be delivered in milliseconds, requiring computing and data to be as close as possible. In this scenario, any lag in data delivery could mean the difference between life and death.

Where real time data delivery is critical, edge computing:

  • Reduces latency and increases speed. Data loses its relevancy based on the time it takes to process it. Edge computing delivers data quickly in applications where it is quickly needed, including data for autonomous driving and manufacturing that require instantaneous data analysis.

  • Increases security. Critical business operations that rely on instantaneous data delivery are the most vulnerable to attack. Edge security allows organizations to distribute data analysis tools across enterprises, expanding the point of attack surface and reducing risk.

  • Saves money. Edge computing optimizes the flow of data, and results in better categorization and data management. Retaining critical data in edge servers allows for greater scalability, by eliminating costly bandwidth needed to connect locations. Computing on the edge also helps to reduce data redundancy created during data transfer from where it is stored on an edge server, for example, to the cloud.

  • Improves reliability. The Internet of things (IoT) covers territories and rural environments where internet connectivity is less optimized. Edge devices store and process data locally, improving connectivity in these areas where temporary connectivity issues might normally impact data communications.
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