Since the early days of the Internet (Arpanet in 1970), the topic of Routing Protocol Convergence Time (time required to detect and reroute traffic in order to handle a link/node failure) has been a top- of-mind issue. A number of protocols and technologies have been developed and deployed at a large scale with the objective of improving overall network reliability. Although such approaches have dramatically evolved, they all rely on a reactive approach: upon detecting a network failure, the traffic is rerouted onto an alternative path. In contrast, a proactive approach would rely on a different paradigm consisting in rerouting traffic before the occurrence of a predicted failure onto an alternate path that meets application Service Level Agreement (SLA) requirements.
Myth or reality? The notion of prediction refers to the ability to anticipate/forecast a network state (such as a dark/grey failure) that would impact the application experience, but also to determine whether an alternative path that is free of failures exists. This short white paper introduces the emergence of a Predictive Internet using learning technologies along with few results derived from the deployment of such technology at scale.
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