Enterprises are increasingly adopting cloud-based services, such as SaaS applications, which in turn increases reliance on the Internet to deliver WAN traffic. The use of traditional multi-protocol label switching (MPLS) services makes sub-optimal use of costly backhaul WAN bandwidth, given that many critical applications and services are no longer internal in data centers. As a result, enterprises are migrating to hybrid WAN architectures and SD-WAN technology that combines direct Internet access (DIA), IP VPN tunnels, and traditional MPLS circuits.
Over the last several years, a robust market of SD-WAN vendors and managed SD-WAN providers has arisen to meet this need. SD-WAN has been architected to provide a certain amount of resilience, leveraging automated actions. A number of these automated functions are able to track network and path characteristics of the data plane tunnels between SD-WAN devices and use the collected information to compute optimal paths for data traffic, automatically redirecting data traffic to the best available path in the event of an issue occurring. This function, while automatic, remains reactive, only able to initiate the change when conditions deteriorate to the point at which the user's performance is impacted.
Predictive analytics builds on this reactive functionality by taking those characteristics, including packet loss, latency, and jitter, and applying a broad range of statistical time-series methods to predict probabilities of traffic disruption for different applications and use these forecasts to provide recommended path selections to the network, in order to avoid the probable issue occurring and impacting user performance.