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Summary

Today's operators are migrating from hardware-based networks to software-based networks, and the technologies driving this change are software-defined networking (SDN) and network functions virtualization (NFV). Migrating to a software-mediated network introduces several challenges, not the least of which is the complexity of managing a hybrid networking environment during transformation, which includes monitoring and assuring physical and virtual network functions in tandem. Despite this complexity, operators need to make this network transformation today to address competitive web-scale disruption, exponential data traffic increases, and rising cost pressures, all the while maintaining a high quality of service.

Cost savings through network optimization, and the agility to launch new revenue-generating services are some of the major benefits of SDN/NFV. However, the real asset for operators will be the increase in network and usage data generated over a software-mediated network, and to truly realize the benefits of SDN/NFV, this influx of data needs to be monitored across the full network, end-to-end (E2E), and analyzed using advanced analytics.

Intelligence across the network, from the core to access, will drive actionable business insights

CSPs are demanding E2E visibility into their networks so that they can correlate data sets at all levels of the network to include operations, services, applications, and the customer view. The goal is to automate real-time decisions and drive better business outcomes.

Depending on the data set's level of sophistication, operators can leverage collected and reported analytics around network faults and defined parameters to improve network operations such as resource planning, service migration, and troubleshooting. The major aims of these data sets are to build closed loops to configure and assure services automatically and to optimize the network resources needed to deliver these services by predicting faults, bottlenecks, and other potential networking problems. This becomes especially compelling in a hybrid networking environment, where the interoperability between physical and virtual network functions is crucial.

The next level of analytics is more predictive in nature. Advanced algorithms will be used to learn from the data generated in real time for better network optimization. A major benefit is that these advanced analytics will enable networks to become "self-healing." However, they will also drive actionable insights – such as sending customized services to specific customers based on their network usage – and enhance the customer experience.

Clearly, analytics will play a critical role in addressing the complexity and other challenges associated with network transformation through SDN/NFV. Operators will turn to trusted partners that can provide E2E network visibility and business insights through advanced big data analytics.

Appendix

Author

Stephanie Gibbons, Principal Analyst, Network Infrastructure & Software

stephanie.gibbons@ovum.com

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