In today’s era of 5G and beyond, communications service providers (CSPs) face numerous challenges such as the escalating demand for new business-focused applications, service quality improvements, and real-time issue resolution. However, the obstacles don’t end there - in addition, operators are grappling with the complexities of monitoring and troubleshooting new and innovative solutions such as those resulting from 5G and the Internet of Things (IoT), and they need to do all of this without onboarding additional resources.
As network technologies continue to evolve and services become increasingly complex, operators are responding to these challenges by transforming network service assurance. However, this transformative shift is posing a new set of challenges that operators need to assess and resolve. Although not a one-and-done initiative, the future of network success lies in real-time visibility of subscriber/device issues, the seamless application of artificial intelligence (AI), and adapting to cloud-native architecture amidst changing 3GPP standards.
As the complexities of network technologies continue to intensify, AI will play a pivotal role in the success and transformation of network service assurance. To illustrate, AI-driven analysis will improve network efficiency, ensure faster issue resolution, and enhance customer experiences. In this article, I will explore viewing subscriber issues in real time, the power of real-time AI, the challenges of moving to a cloud-native architecture, service fulfilment versus service assurance, and how and when to use the data acquired.
Overcoming the obstacles of a changing network landscape
The future of network service assurance lies in developing a cutting-edge, three-tiered approach - real-time visibility, applying AI in real time, and overcoming cloud-native challenges - all of which require innovative tactics.
- Real-time visibility of subscriber issues versus real-time visibility of network functions: Traditionally, the focus of network service assurance was to ensure seamless connectivity and was accomplished by monitoring network functions, as well as the infrastructure. However, today’s competitive landscape in conjunction with increasing network complexity and rising customer expectations has created a shift in focus. Having the ability to understand customer experiences and issues encountered in real time has become paramount. This enables CSPs to quickly detect and resolve service issues in real time, and proactively address potential issues before they negatively affect user experiences.
- The application of AI in real time: Given the increasing complexities of networks, manual monitoring and intervention have become less efficient and less effective, and this is where AI plays a pivotal role. Ready to revolutionise network service assurance, real-time AI can analyse the vast amounts of data generated by the network infrastructure and user devices to identify patterns, predict potential issues, and prescribe actions to optimise network performance. Poised to revolutionise network service assurance, the application of AI in real time enables operators to analyse subscriber-level details, which allows CSPs to quickly detect and resolve issues, improve network efficiency, and enhance the overall customer experience.
- Moving to a cloud-native architecture: Along with supporting private networks, the increasing demands of businesses and subscribers alike has made transitioning to cloud-native architecture essential. However, this evolution comes with its own set of challenges. Foremost is adopting ever evolving 3GPP standards, which continue to redefine network specifications and grow in complexity. The downstream affects require operational teams to adapt to new tools, protocols, and processes. To keep pace and have the skills and knowledge to manage and assure cloud-native networks, teams are now subjected to a continuous learning process. The transition to a cloud-native architecture and its inherent challenges further solidifies the need for AI-assisted assurance systems. AI-assisted assurance systems will enable operational teams to become automation driven.
The future of network service assurance relies on a paradigm shift from mere real-time visibility of network functions to include real-time visibility of subscriber issues. And this approach relies on AI’s real-time application to address the increasing complexity of network management and enable proactive troubleshooting.
Service assurance: A fulfilment perspective versus a network perspective
Service assurance can be viewed from both a fulfilment and network perspective. From a fulfilment perspective, service assurance focuses on the end-to-end process of delivering services to subscribers, including order management, provisioning, and activation. This viewpoint stresses seamless service delivery to meet customer expectations but not on the operation of the systems. While service fulfilment requires real-time data, AI/ML, and is cloud-native, it does not take into consideration real-time customer traffic of the services.
On the other hand, service assurance from a network perspective acquires data and concentrates on monitoring and optimising the health and performance of the network infrastructure to deliver positive customer experiences, which is foundational to this perspective. While individually each perspective is important, it takes a comprehensive approach for service assurance success. Operators that address service assurance from both angles can better manage network resources, provide high-quality services, and deliver exceptional levels of customer satisfaction.
When and how to use real-time, KPI-time, and fast-time data
To understand subscribers’ experiences, service providers have relied on the data they collect from between the nodes in what were standard interfaces. 5G networks have upgrades in security built into the network core, making data acquisition of customers’ experience more challenging. In effect, the data is encrypted, and the network software vendors must now facilitate this data acquisition via the introduction of vTaps (virtual taps).
Given the importance of data timeliness, using the acquired data to take corrective network actions may prove to be extremely difficult. Data timeliness or when you have the data can be viewed as split into three buckets: real-time (within a second), fast-time (within a minute), and KPI-time (every 5/15 minutes or longer). Using AI for quick decisions, real-time network data is used for immediate tasks like identifying network faults and managing traffic. Fast-time data, while considered real time is not instant, is primarily used for tasks like checking the quality of service and determining, from the subscribers’ perspective, how well services are performing. Using deep analytics for insights, KPI-time data is used for longer-term tasks and offers a detailed look at the network's overall performance. To get the most from the data collected, operators must understand what data is needed and how to use it. This is essential to understanding network impacts prior to embarking on the automated network journey.
Overcoming network challenges through AI
The continued success of network service assurance requires a paradigm shift, taking it from just having visibility of network functions to also providing real-time visibility of subscriber issues. This approach has the means to deliver a more customer-centric model which, over time, will open the door to enhancing the subscriber experience and driving automation. Already critical to network success, AI’s real-time application will continue to take on an increasingly pivotal role in addressing the complexities of network management and enabling seamless and proactive troubleshooting.
As the industry continues its transition to cloud-native architecture and adapts to ever-evolving 3GPP standards, operational teams will find themselves navigating escalating complexities. With AI playing a central role in delivering real-time insights, operational teams will shift from manual operations to become an automation-driven operational team, making end-to-end customer and network visibility a reality.
