NetOps

Trend 11: Open, standards-based, software-defined networks and network function virtualization

Current network conversions focus on programmability, which is achieved by softwaredefined networks (SDNs) and network function virtualization (NFV). SDNs help make networks flexible and create an agile networking landscape. NFV, with the help of cloud computing platforms, drives capacity scaling.

Together, these twin trends make automation and DevOps practices possible in the networking space. The broad technologies in NetOps include orchestration platforms like Cloudify, which drives zero-touch provisioning across multiple cloud platforms and edge devices; infrastructure as code based on templates using Terraform, Tosca and other standards; NFV where businesses deploy the entire virtual private cloud with just a click of a button using a DevOps pipeline; configuration management tools like Ansible and even open-source white box hardware which is deployed and configured in a completely automated manner.

With the emergence of technologies like multi-access edge computing, applications are moving closer to edge devices to get better throughput and scalability. Customers are also looking for flexibility in deploying virtual network functions, either on the edge device or in the edge cloud and across multiple public cloud platforms.

A tier-one U.S. telecommunications client brought efficiencies to their enterprise when they launched over 50 headend locations supporting 10,000-plus cable modem customers using the latest DevOps technologies. By virtualizing their locations, they saw more than 50% savings in capital and operating expenditures. They also reduced their service and site launches from a month to less than a week, leading to 75% acceleration for time to market.

NetOps

Trend 12: Open-source, closed-loop AI Ops

Monitoring and service assurance solutions traditionally used legacy tools to manage the network and adhere to the agreed service-level agreement. But, with only a simple network management protocol in place, they had limited monitoring capabilities. The latest trend is moving toward real-time, intent-driven solutions, which are mostly distributed and cloudenabled. A wide range of open-source platforms is being used for data analytics, including ELK Stack, Prometheus and Grafana. These systems are matured and widely deployed by customers in production.

On the horizon is AI/ML-based smart service assurance, which offers robotic cognitive automation, topology discovery and automated recovery to create a zerotouch assurance system effectively. Open-source big data solutions like Hadoop, Spark and TensorFlow, along with workflow engines like Camunda, will be used for data correlation and issue remediation. These solutions will also be used for fault prediction to avoid impact on business services, capacity management to prevent service degradation and automated feedback loops to improve service assurance continuously.

Using a rule-based alert management framework, a tier-one U.S. telecommunications company reduced their alarm volume by nearly 85%. This, along with AI/ML-based probable root cause analysis and automated recovery, helped the client make significant savings in operating expenditures.

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