Machine Learning & AI Take Aim at Network Complexity, Customer Experience

James Crawshaw
5/11/2018
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The complexity of communications networks seems to increase inexorably with the deployment of new services, such as SD-WAN and new technologies, such as SDN and NFV. To meet ever-rising customer expectations, operators need to increase the intelligence of their network operations, planning and optimization. Machine learning and artificial intelligence (AI) will be key to automating network operations and optimizing the customer experience.


To compete with the OTT players, telcos need to be nimbler by accelerating network automation. Join us in Austin, Texas from May 14-16 for our fifth-annual Big Communications Event as we tackle challenges like automation. The event is free for communications service providers -- secure your seat today!


Researchers in communication networks are tapping into ML and AI techniques to optimize network architecture, control and management, to enable more automation in network operations. One such example is the Knowledge-Defined Networking paradigm. Meanwhile, practitioners are involved in initiatives such as the Telecom Infra Project's Artificial Intelligence and Applied Machine Learning Group, which has a work stream looking at ML-based network operations, optimization and planning.

AI and ML approaches are beginning to emerge in the networking domain to address the challenges of virtualization and cloud computing. Increased complexity in networking and networked applications is driving the need for increased network automation and agility. Network automation platforms such as ONAP should incorporate AI techniques to deliver efficient, timely, and reliable management operations. (See Colt Sees ONAP as Longer-Term Industry Orchestration Standard.)

ML and AI promise to reveal new insights from network telemetry and flow data, enabling operators to predict capacity demands and scale their networks appropriately. These new techniques will add a layer of "intelligence" to today's state-of-the-art network management and automation toolsets. (See New Service Providers Powered by SDN, AI, Aim for Fast, Simple Service Delivery.)

To discover the key use cases for applying ML and AI to high-performance networks and learn more about the key technology enablers, join us in Austin next week (May 14-16) as we discuss the use of machine learning and AI for network automation at the fifth-annual Big Communications Event.

— James Crawshaw, Senior Analyst, Heavy Reading

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jhglavin
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jhglavin,
User Rank: Light Beer
5/21/2018 | 5:51:59 PM
Efficient NFV/SDN Requires AI/ML
For NFV & SDN to be effective and efficient, then AI & ML are critical to their existence in the telco networks.  The generation of virtualized network functions, and their underlying networking infrastructure, requires that the functionality be generated only as needed and exactly when they are needed.  Such exact timing requires AI & ML tools being applied to the massive data loads being generated from the networks, and it is truly exciting to see the R&D labs delivering the tools to make this efficiency a reality. 
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