Edge computing has become increasingly popular in recent years as more and more companies have started to realize the benefits of this technology. Edge computing involves processing data closer to where it is generated, which can reduce latency and improve efficiency. In this article, we will provide an overview of the underlying technologies and platforms for edge computing.
What is Edge Computing?
Edge computing is a distributed computing model where processing is done closer to the edge of the network, rather than in a centralized location. The purpose of edge computing is to bring computation and data storage closer to where it is needed, which can reduce latency, improve security, and increase efficiency.
Edge computing has become increasingly important as the amount of data generated by IoT devices and other sources has grown. By processing data at the edge of the network, companies can reduce the amount of data that needs to be transmitted to a central location, which can help reduce network congestion and improve the overall performance of the system.
Underlying Technologies for Edge Computing
There are several underlying technologies that are used in edge computing. These include:
Internet of Things (IoT) Devices
IoT devices are the main source of data that is processed at the edge of the network. These devices include sensors, cameras, and other devices that are used to collect data.
Edge Computing Servers
Edge computing servers are used to process data at the edge of the network. These servers are typically small, low-power devices that are designed to be deployed in remote locations.
Artificial Intelligence (AI) and Machine Learning (ML)
AI and ML are used to process the data that is collected by IoT devices. These technologies are used to identify patterns and anomalies in the data, which can be used to improve the performance of the system.
Cloud Computing
Cloud computing is used to provide additional processing power and storage for edge computing systems. Cloud computing is used to store data that is collected by IoT devices, as well as to process data that is too complex to be processed at the edge of the network.
Platforms for Edge Computing
There are several platforms that are used for edge computing. These platforms provide tools and services that are designed to make it easier for companies to deploy and manage edge computing systems. Some of the most popular platforms for edge computing include:
Microsoft Azure IoT Edge
Microsoft Azure IoT Edge is a platform that provides tools for deploying and managing edge computing systems. The platform includes a runtime environment that can be used to run custom code on edge devices, as well as tools for managing the deployment and configuration of edge devices.
Amazon Web Services (AWS) IoT Greengrass
AWS IoT Greengrass is a platform that provides tools for deploying and managing edge computing systems. The platform includes a runtime environment that can be used to run custom code on edge devices, as well as tools for managing the deployment and configuration of edge devices.
Google Cloud IoT Edge
Google Cloud IoT Edge is a platform that provides tools for deploying and managing edge computing systems. The platform includes a runtime environment that can be used to run custom code on edge devices, as well as tools for managing the deployment and configuration of edge devices.
Conclusion
Edge computing is a rapidly growing technology that is being used by companies to improve the performance and efficiency of their systems. By processing data at the edge of the network, companies can reduce latency and improve the overall performance of their systems. There are several underlying technologies and platforms that are used in edge computing, including IoT devices, edge computing servers, AI and ML, and cloud computing. By understanding these technologies and platforms, companies can make more informed decisions about how to deploy and manage their edge computing systems.
