Abstract: Techniques for detecting root cause for transaction degradation using causal Bayesian networks are disclosed. In some embodiments, various states associated with an application comprising transactions and components are determined, wherein the determined states are associated with the application transactions and components. The determined states are used as input to build a Bayesian network whose nodes represent application transactions and components. A root cause set comprising one or more application components that is associated with a transaction degradation is inferred by traversing the Bayesian network.

Abstract: Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application.

Abstract: Various techniques associated with application performance monitoring are disclosed. In some embodiments, a subset of application methods is configured to capture response time metrics, and response time metrics for a prescribed application transaction are computed by summing corresponding response time metrics of methods of the subset that are executed during each transaction invocation. Method and transaction response time metrics are collected for each of a plurality of observation intervals, and the collected response time metrics are analyzed to identify anomalous method and transaction states. Co-occurring anomalous transaction and method states are correlated to identify a set of hotspot methods for the transaction, wherein hotspot methods comprise expected root causes for anomalies of the transaction.

Abstract: A method for discovering a link between an entry-exit device of a computer network and a customer edge device is disclosed. The customer edge device includes an address and the entry/exit point device includes a routing table. Interfaces with the routing table and addresses of the computer network related to the entry/exit point device are obtained. Either a routing protocol or the routing table is used to obtain routing information. The routing information is compared to a selected address to find a match. The routing information the matches the selected address represents the address of the customer edge device.

Abstract: Network discovery is performed to detect routers and to collect discovery data from them. The discovery data is analyzed to identify zones, i.e., autonomous routing domains and subdomain areas. Network traffic is monitored at border router ports to obtain zone traffic data. The zone traffic data is analyzed to evaluate per-zone or inter-zone traffic parameters.

Abstract: Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application.

Abstract: Various techniques associated with application performance monitoring are disclosed. In some embodiments, a subset of application methods is configured to capture response time metrics, and response time metrics for a prescribed application transaction are computed by summing corresponding response time metrics of methods of the subset that are executed during each transaction invocation. Method and transaction response time metrics are collected for each of a plurality of observation intervals, and the collected response time metrics are analyzed to identify anomalous method and transaction states. Co-occurring anomalous transaction and method states are correlated to identify a set of hotspot methods for the transaction, wherein hotspot methods comprise expected root causes for anomalies of the transaction.

Abstract: Techniques for detecting root cause for transaction degradation using causal Bayesian networks are disclosed. In some embodiments, various states associated with an application comprising transactions and components are determined, wherein the determined states are associated with the application transactions and components. The determined states are used as input to build a Bayesian network whose nodes represent application transactions and components. A root cause set comprising one or more application components that is associated with a transaction degradation is inferred by traversing the Bayesian network.

Abstract: Methods, computer-readable media and computing systems are provided for analyzing service impacts on one or more VPNs on a computer network. A topology of each VPN may be identified. A protocol failure may be detected between routers of the computer network. It may then be determined how the VPN is affected by the protocol failure based on the identified topology and VRF information obtained from routers of the computer network.

Abstract: Network discovery is performed to detect routers and to collect discovery data from them. The discovery data is analyzed to identify zones, i.e., autonomous routing domains and subdomain areas. Network traffic is monitored at border router ports to obtain zone traffic data. The zone traffic data is analyzed to evaluate per-zone or inter-zone traffic parameters.

Abstract: A method for discovering a link between an entry-exit device of a computer network and a customer edge device is disclosed. The customer edge device includes an address and the entry/exit point device includes a routing table. Interfaces with the routing table and addresses of the computer network related to the entry/exit point device are obtained. Either a routing protocol or the routing table is used to obtain routing information. The routing information is compared to a selected address to find a match. The routing information the matches the selected address represents the address of the customer edge device.

Abstract: Methods, computer-readable media and computing systems are provided for analyzing service impacts on one or more VPNs on a computer network. A topology of each VPN may be identified. A protocol failure may be detected between routers of the computer network. It may then be determined how the VPN is affected by the protocol failure based on the identified topology and VRF information obtained from routers of the computer network.