AI is the Key to an end-to-end management
copyright by inform.tmforum.org
Communications service providers are transforming their businesses and networks by adopting business process automation, network functions virtualization (NFV), software-defined networking (SDN), cloud-based applications and, in the case of mobile operators, 5G technology. Managing all this is the biggest challenge they face, and analytics and machine learning are critical for success.
Traditional network management solutions were developed for physical networks and assurance is reactive in this case. But now operators need to be able to manage hybrid networks made up of physical and virtual components, and assurance must be predictive.
As George Glass, Chief Architect, BT, notes in our new Quick Insights report Data analytics and AI: Key to end-to-end management :
“What we’re moving toward is real-time decisioning, where information is gathered and processed in real time, even huge amounts of data. As activities happen in the network, those events can be mirrored into a data cache and processed in real
time.”
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What’s your intent?
In software-defined networks, management must happen at many levels – the physical network, logical (virtualized) resources, services running on top of this infrastructure and in the customer’s domain, whether this is a customer’s self-service portal, smartphone or an IoE. Also, management must extend beyond an operator’s borders to include partners’ networks and services.
To accomplish this, operators are beginning to use intent-based management that relies on orchestration, data analytics, policy, and machine learning to autonomically provision, configure and assure their networks and the services they deliver to customers.
Analytics are critical for their role in automating closed control loops, which help operators guarantee and optimize performance and reduce operating costs (see graphic below).
Getting started with machine learning
What operators want is to be able to predict problems. This requires machine learning, which is a form of artificial intelligence that includes the development of applications that ‘learn’ based on flexible, evolving analytical models. Every operator we spoke with for this report said machine learning in network and service management is going to be critical, but it’s still very early days.
[…]
read more – copyright by inform.tmforum.org
AI is the Key to an end-to-end management
copyright by inform.tmforum.org
Communications service providers are transforming their businesses and networks by adopting business process automation, network functions virtualization (NFV), software-defined networking (SDN), cloud-based applications and, in the case of mobile operators, 5G technology. Managing all this is the biggest challenge they face, and analytics and machine learning are critical for success.
Traditional network management solutions were developed for physical networks and assurance is reactive in this case. But now operators need to be able to manage hybrid networks made up of physical and virtual components, and assurance must be predictive.
What’s your intent?
In software-defined networks, management must happen at many levels – the physical network, logical (virtualized) resources, services running on top of this infrastructure and in the customer’s domain, whether this is a customer’s self-service portal, smartphone or an IoE. Also, management must extend beyond an operator’s borders to include partners’ networks and services.
To accomplish this, operators are beginning to use intent-based management that relies on orchestration, data analytics, policy, and machine learning to autonomically provision, configure and assure their networks and the services they deliver to customers.
Analytics are critical for their role in automating closed control loops, which help operators guarantee and optimize performance and reduce operating costs (see graphic below).
Getting started with machine learning
What operators want is to be able to predict problems. This requires machine learning, which is a form of artificial intelligence that includes the development of applications that ‘learn’ based on flexible, evolving analytical models. Every operator we spoke with for this report said machine learning in network and service management is going to be critical, but it’s still very early days.
[…]
read more – copyright by inform.tmforum.org
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