One of the biggest trends in the networking industry for the foreseeable future will be the huge explosion in the number of devices set to be connected around the world. The vast majority of these will be Internet of Things (IoT) sensors, which will in turn require a significant rethinking of how networks work in order to make sure they operate effectively.
By 2030, some estimates suggest the number of connected devices will reach 500 billion, with initiatives such as smart connected cities one of the key driving forces behind this. As more people move to urban environments, adding more IoT sensors to control and manage these environments will be essential.
However, this poses challenges for traditional networks. Shadi Salama, channel leader for the east region at Cisco Middle East, stated in an article for Tahawul Tech that these new connections mean that it will no longer be possible to manage and control such networks manually, as they will simply be "too complex, too cumbersome and too complicated" for human operators to deal with.
Therefore, traditional networking models that are not able to scale up and deliver the high performance now required in this new era will have to be replaced with new solutions that are both easier to manage, more secure and more intuitive.
Mr Salama said: "Intuitive networks shift from the traditional manual, time-intensive, static mode of operation, towards one that is capable of continuously learning from the data that it manages for an organisation."
He added that with advanced machine learning techniques, the more data these networks manage, the more capable they will become, as they learn through analytics and adapt in order to become more efficient and responsive.
An intuitive network embeds these machine learning and analytics technologies at a foundational level, and automates the edge of the network, where the IoT devices will sit. This makes them particularly useful for applications in the next generation of smart city networks, where IoT sensors are deployed across a wide variety of use cases, from monitoring traffic flows to studying air quality, and making changes to systems accordingly.
Networks with these technologies are able to learn and make predictions from the data they manage, which allows them to overcome the limitations of traditional static programming. For example, they will be able to identify a model of what normal baseline behaviour on the network looks like, and highlight anything unusual. It can then proactively make changes without the need for human intervention or approval to address any anomalies or intrusions.
The benefits of a more modern, intelligent networking framework can be significant. Mr Salama highlighted research from IDC that found firms that invest in up-to-date networks can improve their growth rate for revenue, customer retention and profit by as much as two to three times.
"For digital organisations, the network is the foundation of their business and success," he added.