87
Good problems to have for AI
One big problem is today's operational challenge in dealing with the mass of user, device,
application and network service data traversing the enterprise access infrastructure. Machine
learning, if applied properly, is an ideal solution for making sense of all this data to figure out how
all the different parts of the network are behaving with each other.
A second big problem is the need to automate the network within a grand closed loop. The use AI
and all this “big data” is key to making this happen. But first, the industry must get the ‘making
sense of the data’ part right among many other things.
Today, network managers must wade through volumes of data from Wi-Fi controllers, server logs,
wired packet data and application transactions, analyzing and correlating all this data to determine
the health of network as well as trends and patterns of network behavior across the stack that impact
user performance. Then, they manually apply changes to the network with no real way to
definitively determine whether those changes worked or not.
Conventional network management and monitoring tools, never designed or developed to deal with
these 21st century realities, are ill-equipped to automate this process.
