Current network conversions focus on programmability, which is achieved by software-defined networks (SDNs) and network function virtualization (NFV). SDNs 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.
IBN caters to business cases such as network slicing and server security rather than to individual tasks. The IBN controller takes care of the right changes to the network to manifest the intent.
A tier-one U.S. telecommunications company launched over 50 headend locations (master distribution centers for converting television signals to cable transmission), supporting over 10,000 cable modem customers using the latest DevOps technologies. By virtualizing its locations, the client achieved 50% savings in capital and operating expenditures. It also reduced the duration of service and site launches from a month to less than a week, accelerating time to market by 75%.
Monitoring and service assurance solutions traditionally used legacy tools to manage and operate a network. With telcos leveraging 5G, IoT, and edgecomputing use cases to drive monetization, network response and reliability become the linchpin of the service level agreement. The latest trend is moving toward real-time, intent-driven solutions, mostly distributed and cloud-enabled. A wide range of opensource platforms, including ELK Stack, Prometheus, and Grafana, are used for data analytics. These systems are matured and widely deployed by customers in production.
AI/ML smart service assurance offers cognitive network planning, topology discovery, and automated recovery to create a zero-touch, closed-loop assurance system effectively. Open-source big data solutions like Hadoop, Spark, and TensorFlow, along with workflow engines like Camunda, are used for data correlation and issue remediation. These solutions are also used for fault prediction to avoid impact on business services; for capacity management to prevent service degradation; and for automated feedback loops to improve service assurance continuously.
A U.S. telecom company partnered with Infosys to develop a rule-based alert management framework. By using this framework, the company was able to do AI/ ML-based probable root cause analysis and automated recovery. The client reduced its alarm volumes by nearly 85% and lowered operating expenditure significantly.
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