Publié le 11 décembre 2018
As automation in service provisioning and activation gains traction, it is fast becoming clear that the advancement of this technology is where the future lies for communication service providers (CSPs). There is little doubt that the benefits from automation will lead to increased business growth (from higher revenues), lower OPEX and CAPEX and decreased customer churn (due to happier subscribers). Today, many operators have firmly implemented SDN, while NFV is gaining significant ground. This illustrates that CSPs are committed to positioning automation at the centre of everything they do.
SDN and NFV are invaluable advances in terms of automating infrastructure, but what about operations? What is the point of speeding up provisioning (via orchestration) if processes such as fault or incident management, problem detection, troubleshooting, service testing, service assurance and change impact analysis remain slow, manual and reactive?
Enter real-time active topology. Having a real-time, cross-domain network and live service topology model that covers legacy as well as next-generation networks and services is the key to both a successful SOC and automation stack. In short, having an accurate understanding of the network, services and customer infrastructure is the only way you can guarantee success when implementing large-scale automation. It is the glue that binds the customer’s services to the ever-changing shared infrastructure, and, as a result, service impact analysis, common cause analysis and change impact analysis become viable and can be easily deployed. With the customer-to-infrastructure dependency analysis provided by real-time topology, you can achieve proactive assurance more easily. Machine learning algorithms can become far more accurate if populated with richer data. Real-time topology enriches and contextualizes the mountains of network data ingested (faults, test data and logs) in relation to services and customers. Real-time topology greatly improves fault deduplication, and machine learning algorithms can help to facilitate automation.
CSPs who invest in their infrastructure now—with a view to gaining visibility of their networks and an in-depth understanding of the data, as well as dependency relationships—will reap the rewards in the near-to-long term as the benefits of automation come into focus. Operators need technology that can achieve these goals if they want to offer fully assured services in the highly automated future.
To learn more about why dynamic topology is a powerful ally in today’s race for automation, read “Network Visibility & Data Veracity – the Keys to Operational Simplicity,” by James Crawshaw, Senior Analyst at Heavy Reading.