Abstract:
A method and apparatus for managing a virtual private network are disclosed. For example, the method collects performance data for at least one parameter from a plurality of customer edge routers and a plurality of provider edge routers associated with the virtual private network. The method correlates the performance data for identifying one or more performance exceptions based on a threshold for each of the at least one parameter, and performs a trending analysis using the one or more performance exceptions to predict a potential problem that will impact the virtual private network.
Abstract:
A method and apparatus for managing a virtual private network are disclosed. For example, the method collects performance data for at least one parameter from a plurality of customer edge routers and a plurality of provider edge routers associated with the virtual private network. The method correlates the performance data for identifying one or more performance exceptions based on a threshold for each of the at least one parameter, and performs a trending analysis using the one or more performance exceptions to predict a potential problem that will impact the virtual private network.
Abstract:
A method and apparatus for managing a virtual private network are disclosed. For example, the method collects performance data for at least one parameter from a plurality of customer edge routers and a plurality of provider edge routers associated with the virtual private network. The method correlates the performance data for identifying one or more performance exceptions based on a threshold for each of the at least one parameter, and performs a trending analysis using the one or more performance exceptions to predict a potential problem that will impact the virtual private network.
Abstract:
A method and apparatus for managing a virtual private network are disclosed. For example, the method collects performance data for at least one parameter from a plurality of customer edge routers and a plurality of provider edge routers associated with the virtual private network. The method correlates the performance data for identifying one or more performance exceptions based on a threshold for each of the at least one parameter, and performs a trending analysis using the one or more performance exceptions to predict a potential problem that will impact the virtual private network.