Network Provisioning: Service Assurance in New Ways
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A path toward service assurance utopia is created by operators’ new ‘ways’
Improved customer experience is always at the top of operators’ minds – happy subscribers translate into loyal customers, but maintaining network performance in the black isn’t easy. When networks become more complex, identifying and resolving problems becomes more challenging. Manually investigating network issues before they significantly impact customer experience becomes more difficult as networks become more complex. In the case of human resources, there is only so much they can accomplish, especially when they are overwhelmed by an abundance of alarms and alerts and subsequent trouble tickets when a problem arises.
There need to be new, efficient, and less time-consuming ways for operators to manage network performance issues and faults. Operators are taking advantage of these new ‘ways’ by automating fault management through AI, machine learning, and analytics.
New approaches for a new era
As a result, today’s technology infrastructures and operations are transforming to keep up with constant network innovation – and in the process, traditional approaches to service assurance are fast becoming obsolete. With a more dynamic technology environment, increasing data volumes, and the acceleration towards autonomous operations, the latest generation of platforms will need to integrate increasing interdependence from various domains and service offerings.
Supporting modern service assurance is trouble ticketing, which includes receiving, assessing, correlating, and resolving incidents impacting the network, setting in motion both reactive and proactive processes – which, if performed correctly, should maintain or increase network quality. A successful trouble-ticketing process requires operators to have complete visibility of their network environment, both horizontally and vertically, and to make timely and accurate decisions to maximize process efficiency. To date, operators have been able to serve various domains using different service assurance platforms. Operators face complex and dynamic ecosystems today as a result of these legacy platforms and operations, which were built to serve monolithic environments. Currently, they must reconcile the solutions and capabilities of multiple in-house suppliers and partners. When it comes to delivering the levels of network efficiency and quality expected today, customization-driven approaches are no longer viable. However, conventional methods of integration are costly, inefficient, and fragile. 5G networks only add to this complexity, which means traditional service assurance methods are no longer viable.
The gap between service assurance requirements and capabilities is narrowing
Consequently, operators need to be ready to respond quickly to evolving customer needs and new business opportunities, without the burden of inadequate service assurance systems. Currently, operators are moving towards more proactive, predictive, autonomous operations, where decision-making can be fully automated, allowing them to remove employees from routine, prosaic tasks and focus on critical customer-impacting issues.
Service assurance is critical to this shift; operators are now looking at ways to use AI and machine learning insights to deliver new applications and network services quickly and resolve network issues within a fraction of the time previously needed.
Operators will move from reactive service assurance processes that fix existing issues to predictive approaches that anticipate problems before they affect the network. The transformative power of AI can be used to address underlying problems affecting multiple systems.
Operators can use big data, machine learning, and analytics to identify patterns in monitoring, capacity, and automation data across complex technology infrastructure, improving their overall service assurance processes. Using technology in this context will enable operators to close the loop on closed-loop automation to the point where human involvement becomes minimal.
Predictive analytics in the age of service assurance
Predictive analytics paired with service assurance will play an important role in zero-touch closed-loop automation. When network faults and failures can be predicted before they happen, networks will be managed differently. Operators still need to evolve their processes to support such capabilities, even though they can be applied to real-world scenarios already.
- As a starting point, a consensus must be established between internal teams about what should be predicted: It will take some time to identify and prioritize what constitutes a high-priority issue, but once it is identified, it can be immediately addressed. For service providers to address AI-driven predictions, they will have to adapt existing processes and competencies, which will require operations to become flexible and open to rapid change, something that the industry is moving toward through the adoption of AIOps.
- By applying unsupervised machine learning techniques to historical data sets, operators are already addressing these new concepts in service assurance: By identifying patterns and predicting their likely recurrence, failure and alarm records can be used to define and implement preventive maintenance processes. Similarly, analyzing historical ticket data in terms of a current issue being investigated and the resolution that fixed the issue before can be used to generate a recommendation engine that determines what to do next. Operators use these applications of AI to manage their networks more efficiently, by reducing the time they spend on analyzing problems and allowing them to concentrate on those issues that affect the customer experience.
Towards a service assurance utopia, innovation is the key
Service quality is vital in a rapidly evolving and dynamic telecommunications landscape. As 5G applications and services become more prevalent, customer expectations are only set to rise, while at the same time tolerance for the disappointing performance, unreliable availability, and slow response times are declining. The increased velocity of network provisioning through automation requires operators to ensure their service assurance tools and processes keep pace with these advances and become more automated. Innovative approaches to operations will ensure that the services consumed today and in the future run smoothly even in the face of growing network complexity by the rapid application of emerging technologies, including AI/ML.