Taking an AIOps Approach to Improve Network Performance Management

At a recent NetworkX webinar, Heavy Reading and NETSCOUT took a closer look at how telecommunications operators are rethinking artificial intelligence for operations (AIOps) to address 5G network challenges. This blog, and the series that follows, delves into how next-gen AI is being used to improve application and performance management and anomaly detection, as well as to deliver critically important monetization insights.

AIOps increasingly is viewed as a vital approach that operators should consider due to the very nature of today’s 5G network landscape, which is completely different from previous generations. As a result of 5G, there is disaggregation with a great deal more vendor diversity in the network. In addition, there is distributed and cloud-based technology and increasing demands for low-latency, high-bandwidth, and ultrareliable services. This is leading to new approaches in the way networks are designed and managed.

Programmability also impacts 5G networks, introducing collaborative pathways, innovation for service development, and viable means for monetization. AIOps hold the potential to change how networks are secured and managed. 
Of course, it will require an extensive level of visibility for these new capabilities to be viable in a vastly different network environment.

Network Complexity Adds to the Challenge

As the scale and scope of 5G networks increase, operators face greater challenges in achieving effective network performance and security management. As the complexity increases with multiple-layer network stacks and more configuration languages and tools, so too does the risk to network stability and resilience. To combat these changes and potential risks, operators are focusing on AIOps strategies to introduce end-to-end network and service automation. This will allow for real-time network reactions, rather than being reactive to events, thus ensuring the best customer service experience.

Because large 5G networks can generate petabytes of data daily across many different domains, as well as third parties, operators are left with siloed data sources that are then difficult to process in a timely manner. Real-time analysis of multiple network sources is needed for better security, agility, enhanced operations, and efficiency.

AIOps to the Rescue

As we all know, AI and machine learning  (ML) are very good at performing repetitive tasks quickly and accurately. That is why AIOps is so important. When AI is used properly, it can deliver insights that are fed into intelligent automation, empowering real-time actions. AIOps has the opportunity to harness data and intelligence to achieve performance enhancements, optimize the network, reduce congestion, and predict and detect performance and security issues. It also holds the potential to maximize network utilization and reduce power consumption, which is particularly important to operators.

For many operators, the shift to AIOps is going to start with operations, such as analyzing data and offering recommendations and assistance for network performance management. According to recent research conducted by Heavy Reading, the top AI application in 2023 by a wide margin was network performance management .*

_{(*Source: Omdia Telecoms AI Contracts Tracker - publicly announced contracts 3Q23)}

The bottom line for AIOps success rests with the quality of the data on which the system relies. Having data in silos, such as the radio access network (RAN), edge, access, core, and cloud, highlights the importance of end-through-end visibility. For example, if something happens in the RAN, it could have a downstream effect on something in the core or even in the cloud—or vice versa. All of the interconnected parts of the network form logical connections for service delivery. They all have to be working in harmony, and that requires visibility.

For 5G networks to deliver on their promise for operators, as well as for clients and customers, there is a robust need to detect failures and degradation before they become a problem. AIOps have the capabilities, when armed with the right data and intelligence, to feed anomalies into AI-driven network-assurance and security-assurance solutions to head off issues quickly. It also allows for ongoing optimization planning of the network.

In today’s increasingly complex 5G network environment, AIOps is an important facilitator of network performance. Visibility will be key to driving AI insights into intelligent automation. AIOps hold the potential to directly contribute to reducing costs and streamlining operations, which is a top priority for operators.

In our next blog, we’ll continue this discussion, looking at why data needs to be mined and curated in order to fully support AIOps’ vital mission.

Learn more about AIOps for telecom.