The introduction of 5G technology is a complex operation involving a new way of looking at systems architecture that can help expand the use of cloud models such as mobile edge computing (MEC), massive machine-type communications and other low-latency applications. Artificial intelligence (AI) and networking will need to play a crucial role in leveraging the many benefits of 5G at scale to successfully accommodate many moving parts.
AI and networking: The complexities of network automation
Software-defined networks (SDN) are the way of the future, as brownfield legacy hardware no longer needs to constrain functionality. While SDNs deliver a whole host of advantages, they can get overwhelming if not orchestrated well. First, there's the challenge associated with the division of networking functions themselves—between virtual network functions (VNF), physical network functions (PNF) and cloud-native network functions (CNF).
The 5G network can be divided into network slices depending on individual service-level agreements (SLAs). Hundreds of thousands of slices are not out of the question. Managing the provisioning and deployment of these will necessitate AI and networking.
In addition, agile DevOps cycles mean network provisioning will have to happen quickly and scale up and down depending on enterprise technology needs. The complexities of network automation grow even more as 5G will have to be tailored to every company's changing needs to meet SLAs.
Speed at scale matters, too. Companies won't wait days or weeks for network setup—they'll need one in hours, if not minutes. Network security, especially as more key functionality migrates to the edge, also continues to be essential. As technologies change, networking orchestration and management will have to keep up without service interruptions. The shifting sands of 5G network automation at scale will need AI for the life cycle management of provisioned networks.
5G network automation and AI
AI and machine learning (ML) algorithms study network behavior and help deliver efficiencies when managing networks at scale. They can help with the provisioning of physical devices and the deployment and provisioning of virtual devices. Using predictive analytics, algorithms can forecast when network failures are imminent—and where. Such intelligence helps operators proactively remediate underlying problems and avoid potentially damaging shutdowns.
AI and networking's capacity to detect deviations from the norm can help flag failures in security compliance and problems across a variety of hybrid environments.
AI's impact on 5G network automation
When most of the 5G network slicing, orchestration and management chores move on to AI systems, 5G network automation should decrease the chance of manual errors involved in provisioning and deploying networks at scale. Companies should realize more efficiencies in service without undue interruptions and downtime. Customer service metrics should improve as network providers are able to proactively put out fires at lower costs.
Another significant advantage of AI and networking is that it helps users see patterns in network performance and usage that might not have ordinarily been used. The study of long-term trend lines allows companies to find new efficiencies at the edges.
The promise of 5G is revitalizing the ground game as the primary driver for many technologies that demand low-latency and high-bandwidth applications. As the demand for 5G grows, providers will have to depend on virtualization to spin off slices at scale. And management and orchestration of these virtual networks will need the automation that AI can deliver.
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