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With innovative cloud-based networking technologies, telecom providers are lowering costs, empowering employees, and optimizing procedures.
Fremont, CA: The very nature of networking is evolving. Old network topologies and systems are unable to keep up with the growing demand for next-generation features and services, necessitating the development of new data connectivity mechanisms for communication service providers and their consumers. Cloud-based next-generation networks are crucial for growth, and digitally modernizing a network infrastructure to make it more open, frictionless, and optimized is critical for continued expansion. As data and networks become more commoditized, telecommunications companies must look for opportunities to save money and innovate.
Transforming the network infrastructure with
5G represents a significant advancement in networking and communication technology. However, as networking capabilities have improved, our demands on those networks have kept pace, and in some cases outpaced them, resulting in disruptions. This is where the concept of edge computing comes into play. In a nutshell, it's a technique for offloading data processing from a centralized cloud to point-of-use devices. By reserving bandwidth and processing power for more critical or large workloads, it is possible to free up bandwidth and processing power. By optimizing operations and putting data processing closer to the action, the network may simultaneously boost responsiveness and reduce latency.
At the moment, AI is being used to assess and forecast network disruptions for telecommunications businesses in advance, reducing reaction times and service interruptions. It's a critical component of automation, as it keeps the network working as more people rely on it. As more customers and businesses place increasing demands on the network, timely and effective resolution of customer complaints takes on a new significance.
Automation based on intent is the most recent advancement in network administration. Defining an ideal end state and then adapting, maintaining, and upgrading network architecture to get there can be time- and cost-consuming. With AI, network operators may now just define the desired end state and let AI handle any necessary changes and revisions. Even better, intentions may be conveyed and understood in a common language, freeing up time and resources for higher-priority tasks.
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