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Enhancing Network Performance Management with an AIOps Approach - Vinay Sharma, Regional Director, India and SAARC, NETSCOUT

Today's 5G network landscape represents a significant departure from previous generations. With this network, we see greater vendor diversity and increased disaggregation within the network. The shift towards distributed and cloud-based technologies, combined with rising demands for low-latency, high-bandwidth, and ultra-reliable services, is driving the development of new network design and management strategies.  Programmability also influences 5G networks by creating opportunities for collaboration, fostering innovation in service development, and offering new monetization avenues.

Network Complexity

As 5G networks expand in scale and scope, operators encounter bigger challenges in effectively managing network performance and security. With the growing complexity of multi-layer network stacks and an array of configuration languages and tools, the risks to network stability and resilience also increase.  To address these changes and mitigate potential risks, operators are adopting Artificial Intelligence for operations (AIOps) strategies to implement end-to-end network and service automation. This approach enables real-time network responses, moving from a reactive to a proactive stance, thereby ensuring an optimal customer service experience.  AIOps has the potential to transform network security and management, aligning with the complexities of the current 5G landscape. However, a high level of visibility for these new capabilities is needed to be viable in a vastly different network environment.

Industry Challenges for Telco AI – The Data Challenge

Large 5G networks generate petabytes of data every day across diverse domains and third-party sources, resulting in siloed data that is challenging to process on time.

  •         Data is not real-time to drive modern software-defined services
  •         Massive volumes of network-level data take a very long time to assemble and analyze
  •         Data is not always provided in an easy consumable standard open format
  •         Not sufficient focus on application quality of experience, by user, by service, by location
  •         Lack of correlation of user identification to data, service, and application the user is utilizing
  •         Lack of understanding of how network services are being utilized down to a user or enterprise level
  •         Inability to know how security issues are affecting the network and user experience
  •         Inability to see the network as end-to-end service provides only ‘piecemeal data’
  •         Lack of Business Intelligence – Device and Application Behavior on the network

Real-time analysis of various network sources is essential for improving security, agility, operations, and efficiency.

AIOps - Transforming Network Management

The growing complexity of networks and services is compelling communications service providers (CSPs) to depend more on high-quality, curated operational data to enhance network and service availability, performance, and profitability.  When service providers have timely access to the right data, disruptions that could otherwise take days to resolve can be fixed within minutes, minimizing and preventing negative impacts on business operations and the end-user experience.

AI and machine learning, excel at executing repetitive tasks quickly and accurately. This is what makes AIOps so crucial.  When this solution is well implemented, AI can provide insights that drive intelligent automation, enabling real-time actions. AIOps leverages data and intelligence to enhance performance, optimize networks, reduce congestion, and predict and detect performance and security issues. It also has the potential to maximize network utilization and reduce power consumption, which is especially important for operators.  For many operators, the transition to AIOps will begin with operations, focusing on data analysis and providing recommendations and support for network performance management. Recent research by Heavy Reading indicates that network performance management was the leading AI application in 2023 by a significant margin.

The success of AIOps fundamentally depends on the quality of the data it relies on. Siloed data sources, such as the radio access network (RAN), edge, access, core, and cloud, highlight the necessity of end-to-end visibility. For instance, an issue in the RAN can have downstream effects on the core or even the cloud, and vice versa. All interconnected components of the network must function in harmony for effective service delivery, and this harmony requires comprehensive visibility across the entire network.

For 5G networks to fulfill their potential for operators, clients, and customers, it's crucial to detect failures and degradations before they escalate into significant problems. AIOps, when equipped with accurate data and intelligence, can identify anomalies and integrate them into AI-driven network and security assurance solutions, swiftly addressing issues. Additionally, AIOps enables continuous optimization planning for the network.

AIOps is crucial for enhancing network performance in today’s increasingly complex 5G network environment.  Achieving visibility is essential for leveraging AI insights into intelligent automation. AIOps has the potential to significantly reduce costs and streamline operations, making it a top priority for operators.

The right AIOps solution will ensure critical enhanced telemetry data is made available in a time-sensitive and highly digestible format to accelerate revenue and business growth. Incorporating machine learning (ML) and artificial intelligence (AI) models, the solution further enhances and automates processes to support the ongoing evolution of an organization’s operations.