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Book · October 021 citations reads 35,829 authorsBog'liq 4 PROFESSIONAL ENGLISHPutting ML to (net) work
Machine learning is useful but only when fed tons of relevant data. On the Enterprise access
network, that includes live packets off the wired network, wireless metrics from WLAN controllers,
SYSLOG data from different network servers, ad other network data Sources. Machine learning is
used to quickly analyze all this different data, correlating it across different network layers. This is
something that's not practically possible with people trying to manually correlate it.
The beauty of these machine learning solutions is that they can be used without server agents, client
software or intrusive architectural changes – using the data already running over the network.
Central to machine learning is the use of massively-scalable cloud computing resources,
sophisticated big data repositories and analytics algorithms that turn everything into meaningful and
understandable actions that network managers can take.
Once analyzed, this data is distilled to surface trends and patterns impacting the performance of
every device on the network. The resulting insights, not clearly visibility or easily achieved by
network managers, tell IT staff exactly where, when and why user connectivity falters.
Because every client network transaction is analyzed by machines, pinpointing precisely where the
network is struggling and quickly be determined.
Are issues occurring on a specific VLAN? In a specific location? Is the problem with a certain Wi-
Fi access point or group of APs? A certain type of device? Is it an application problem? DNS or
DHCP issue? For a given group users? What are some concrete actions I can take to improve DNS
experience in my network? Without machine learning, getting answers to these questions can take
days or even weeks.
The right time at the right place
Given the invasion of new data now hitting enterprise access networks, machine learning and AI
couldn't be more welcome technologies for taking the pressure of network managers to do more
with less.
While AI is simply a general term describing automating manual or complex tasks, machine
learning is a toolkit of algorithms that enable automatic learning from ‘big data’ already running
over today's networks.
Armed with these technologies, network managers can now better understand where they have
issues with user experience, get recommendations of actions to take and, ultimately, automate the
configuration and operation of the infrastructure. This is network nirvana by almost any definition.
See more at:
https://www.networkworld.com/article/3256013/lan-wan/ai-machine-learning-
and-your-access-network.html
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