Operators Take 5G to the Next Stage With Cloud-Native Network Optimization

As adoptions of fifth generation (5G) wireless technology continue to expand, telecoms offer unparalleled network capabilities for the fast data relays and low latencies required by connected devices. Operators are leveraging their 5G networks to further configure their offerings, adapt to new market trends and deliver different combinations of services and features.

Through on-demand probe technology and predictive analytics, telecoms can transition from manual to proactive monitoring, but they can also uncover network patterns, pinpoint anomalies and take dynamic measures as needed. Predictive analytics utilizes built-in Artificial Intelligence (AI) and Machine Learning (ML) embedded into next-generation service assurance solutions to analyze the massive amounts of data generated by networks daily. AI has natural advantages over humans in analyzing large volumes of data, finding patterns and establishing a baseline of network behavior to make predictions. These approaches also include forecasting network traffic or evaluating new 5G service deployments to uncover performance issues and predict future network failures.

In this article, we look at the role of automated monitoring for maintaining operator networks, particularly as they move to 5G standalone (SA) deployment and network slicing. We also look at how network engineers can personalize their views of the network and gain end-to-end visibility to ensure end-to-end (from the RAN to the core) service quality.

5G: Increased network complexity

In the past, network monitoring for operators involved a hands-on approach that was sufficient for 3G and 4G technologies. However, these manual processes take resources away from other network operations. They’re also inefficient for deploying and evaluating new 5G rollouts where complete network visibility is essential. Such an approach requires behavior and usage benchmarks to ensure adequate security and business performance. Engineers can then rely on network alerts to indicate problems which are not purely technical.

For example, operators can combine network tracking with business analytics to meet the performance needs of a specific slice of users, address recurrent problems within a particular service or evaluate new service deployments. That’s especially important as the new 5G standard exponentially increases the number of unique datasets and packets that move across the network.

In addition to monitoring overall network functionality, operators face further complexity because they also need to track different service slices. These processes are both tedious and impossible to perform when there are hundreds of network slices that need to be monitored. Operators require automation and predictive analytics to analyze the data and look for issues. For example, forecasting slice congestion and preemptively adding more capacity to a specific slice.

Telcos are also familiar with the challenges of 5G, non-standalone (NSA) networks since they’re similar to previous 4G deployments. However, as the industry transitions to 5G standalone (SA), companies face the challenge of tracking many more metrics and network functions as well as new security threats.

Operators need to closely monitor consumption and usage and then strictly allocate the right amount of resources in advance, reacting quickly to any possibility of resource exhaustion. Lacking comprehensive visibility, operators risk underutilizing the network resources. However, with the right monitoring solution in place, telcos can predict network failures by scheduling regular data forecasts and trend analyses.

Cloud-native, automated network monitoring

Cloud-Native Functions (CNF) are essential for enabling the fast network deployments and high scalability characteristic of 5G. Both dynamic and automated, CNFs enable the network infrastructures, operations and management that 5G use cases depend on, from connected cars and smart cities to digital healthcare and Industry 4.0 manufacturing. But many operators struggle to monitor and manage CNFs along with their hybrid network architectures which entail a mix of legacy technology and virtualized functions.

Moreover, 5G offers capabilities to deploy private networks which require accurate performance tracking and pose their own sets of challenges. Operators must set aside resources to maintain private networks and this applies to startups working on 5G innovations as well as large, technology-advanced enterprises that have private networks on their own campuses and require these same monitoring capabilities.

And some operators will choose a cloud-based approach to maintaining their networks. They gain the advantages of cost reductions and not having to use on-prem hardware. However, these companies have essentially traded one type of complexity for another and now must tackle new, unfamiliar methods of network monitoring. For example, in terms of 5G root cause analysis, operators still would have to decide whether the source of a failure stems from the RAN, the core, the cloud or the actual network itself.

Full network visibility within the 5G-ready cloud

As telecoms grapple with cloud-based networks and struggle to manage CNFs alongside their own network architectures new 5G use cases enabled by edge computing and network slicing are on the increase. At the same time, consumers and companies continue to demand high service levels for voice, data and video.

The speed of new innovations has led to unparalleled density and complexity across the telco industry. Through the assurance delivered by AI and machine learning (ML), operators are proactively monitoring network performance at scale. This approach is enabling telecoms to achieve the goal of introducing high levels of intelligent automation into their operations and network maintenance.

RADCOM offers an automated assurance platform that seamlessly integrates into the 5G core as a cloud-native function (CNF). As the leading expert in 5G service assurance solutions, RADCOM provides real-time subscriber analytics and E2E troubleshooting to identify root-cause failures and optimize network performance. By leveraging AI and ML capabilities, it automates network monitoring and enables pre-emptive rather than reactive network operations. These new core technologies coexist alongside legacy networks and are designed for operators to seamlessly handle the substantial increase in 5G data traffic. For more information, visit https://radcom.com.

This content is sponsored by RADCOM.

— Kerry Doyle, Light Reading contributor for RADCOM