A recommender system architecture for predictive telecom network management

Current telecom networks generate massive amounts of monitoring data consisting of observations on network faults, configuration, accounting, performance, and security. Due to the ever increasing degree of complexity of networks, coupled with specific constraints (legal, regulatory, increasing scale of management in heterogeneous networks), the traditional reactive management approaches are increasingly stretched beyond their capabilities. A new network management paradigm is required that takes a preemptive rather than reactive approach to network management.

This work presents the design and specification of E-Stream, a predictive recommendation based solution to automated network management. The architecture of E-Stream illustrates the challenges of leveraging vast volumes of management data to identify preemptive corrective actions. Such design challenges are mitigated by the components of E-Stream, which together form a single functional system. The EStream approach starts by abstracting trace information to extract sequences of events relevant to interesting incidents in the network. After observing event sequences in incoming event streams, specific appropriate actions are selected, ranked, and recommended to preempt the predicted incidents.