5G marks an inflection point for operators. The disaggregation of software and hardware in 5G enables operators to move telecommunication workloads to public or hybrid public/private cloud infrastructures, giving them unprecedented agility and flexibility to deliver exceptional customer experiences and realize cost efficiencies. However, the full benefit of running large-scale telecommunication services in the cloud can only be achieved if cloud adoption is accompanied by a comprehensive approach to network analysis and automation supported by cloud-based big data and AI.
Today,Azure for Operatorsis introducing a network analytics solution accelerator program, providing a standardized approach to data acquisition and visualization that aids operators on their journey toward complete end-to-end AI Operations (AIOps). The solution employs the same operational techniques and capabilities that Microsoft uses to manage Azure, packaged specifically for operator analytics. Our network analytics solution comprises existing Azure services combined with unique capabilities developed specifically for communications service providers, which allows network planners and engineers to visualize performance and troubleshoot service anomalies.
Disaggregated cloud native 5G networks add many new individual elements that must interwork effortlessly. These increasing interdependencies mean management and analytics tools can no longer run in relative isolation. Successfully deploying and managing end-to-end services in such environments requires the ability to analyze network and host platform data simultaneously from numerous sources. Only then can operators reactively and proactively diagnose issues, while ensuring operational costs are kept in check and that customers are always presented with the best user experiences.
With the scale and complexity of such services, network management needs to operate autonomously in a closed loop manner—taking operational insights on the health of network elements and the underlying distributed cloud infrastructure and ensuring a service is configured optimally.
At Microsoft, we understand this journey because Azure went through a similar evolution. In the early days, we recognized the challenges of troubleshooting across disparate services. To solve this, we established a common data analytics infrastructure that gave us a comprehensive view of how our services performed, which resulted in lower engineering overheads and better service quality.