Applications of Edge AI in Network Traffic Optimization
The edge AI offers numerous types of applications in the network traffic optimization which helps in the ability to process data locally and more efficiently at the edge of the network. below are some of the applications of the edge AI that are available in the network traffic optimization:
- Real-Time Traffic Analysis: The realtime traffic can be achieved by using the edge AI because the edge AI algorithms can easily analyze the network traffic and their patterns in the real time and check for the security threats. If there are any threats present then it can also very easily make the required changes.
- Predictive Traffic Management: The edge AI can easily predict the future traffic of the network on the basis of the historical data and the current trends, by analyzing the traffic patterns the edge devices can dynamically allocate the resources and then adjust routing configurations accordingly.
- Dynamic Bandwidth Allocation: Edge AI enables the dynamic bandwidth allocation on the basis of the real traffic conditions and the requirements of the application.
- Traffic Optimization: The edge AI algorithms can easily optimize the traffic flow and it does this by shaping the data packets and prioritizing the data accordingly.
- Localized Content Delivery: Edge AI offers the facility of the localized content delivery and it does this by caching the frequently accessed content at the edge of the network.
Edge AI for Network Traffic Optimization
The edge AI for the network traffic algorithm refers to the various applications of the artificial intelligence and its algorithms and techniques at the edge of a network which are used in order to optimise and manage the network and its traffic more efficiently, So in this article we will understand what is edge AI, applications, advantages and disadvantages of using edge AI.
Edge AI means that we deploy AI models directly onto the network devices. for example, the routers switches or even the edge computing devices we deploy these in order to perform various tasks such as traffic prediction analysis as well as optimisation and this happens in the real time without having the need to send the data to a cloud or some centralised server.