Abstract: Some embodiments of the invention provide methods for performing root cause analysis for non-deterministic anomalies in a datacenter. For instance, the method of some embodiments identifies a root cause for degradation in performance of one or more components in a network of the datacenter. This method collects and generates resource consumption data regarding resources consumed by a set of components in this network. The method performs a first analysis on the collected and/or generated data to identify an instance in time when one or more components, while still operational, are possibly suffering from performance degradation. The method then performs a second analysis on the collected and/or generated data associated with the identified time instance to identify a root cause of a performance degradation of at least one component in the network.

Abstract: The disclosure provides an approach for prescriptive analytics for network services. Embodiments include receiving one or more rules for anomaly detection. Embodiments include receiving metric data of one or more services and collecting context data related to the metric data. Embodiments include determining a baseline for the metric data. Embodiments include detecting an anomaly based on analysis of the metric data in view of the baseline for the metric data and the one or more rules for anomaly detection. Embodiments include associating the anomaly with a subset of the context data that is related to the anomaly. Embodiments include determining a score for the anomaly based on the analysis and determining that a notification should be generated based on the score. Embodiments include providing the notification to a user interface for display. The notification comprises includes an indication of the anomaly and the subset of the context data.

Abstract: Examples herein describe systems and methods for impact assessment in a Telco network. An impact assessment engine can receive a scenario that species a symptom for a network resource. The scenario can be specified on an administrative console. The impact assessment engine can construct failure signatures in a codebook matrix. The codebook matrix correlates problems at impacted network resources to network symptoms. Based on the scenario, the impact assessment engine can generate synthetic failure signatures and isolate a subset based on which of the synthetic failure signatures are most common. Then, the system can identify tenants and services in the Telco network impacted by the subset. This can allow administrators or an automated process to identify potential impacts before a scenario occurs.

Abstract: Examples herein describe systems and methods for impact assessment in a Telco network. An impact assessment engine can receive a scenario that species a symptom for a network resource. The scenario can be specified on an administrative console. The impact assessment engine can construct failure signatures in a codebook matrix. The codebook matrix correlates problems at impacted network resources to network symptoms. Based on the scenario, the impact assessment engine can generate synthetic failure signatures and isolate a subset based on which of the synthetic failure signatures are most common. Then, the system can identify tenants and services in the Telco network impacted by the subset. This can allow administrators or an automated process to identify potential impacts before a scenario occurs.

Abstract: The disclosure provides an approach for prescriptive analytics for network services. Embodiments include receiving one or more rules for anomaly detection. Embodiments include receiving metric data of one or more services and collecting context data related to the metric data. Embodiments include determining a baseline for the metric data. Embodiments include detecting an anomaly based on analysis of the metric data in view of the baseline for the metric data and the one or more rules for anomaly detection. Embodiments include associating the anomaly with a subset of the context data that is related to the anomaly. Embodiments include determining a score for the anomaly based on the analysis and determining that a notification should be generated based on the score. Embodiments include providing the notification to a user interface for display. The notification comprises includes an indication of the anomaly and the subset of the context data.

Abstract: Examples herein describe systems and methods for alarm prioritization in a Telco network. A prioritization engine can receive root cause problems and impacted network components from a network analysis module. The prioritization engine can define a mapping matrix that weights the problems according to network component type and service level. Using the problem weights, the prioritization engine can construct a problem-impact matrix that includes impact weights. The impact weights can be summed for each problem, as can the problem weights. The problems with the highest summed weights can then be prioritized first. The summed weights can also be based on predicted failure costs, such that the most expensive problems are prioritized first. The prioritization engine can send prioritized alerts for the problems for display on a graphical user interface (“GUI”).

Abstract: Some embodiments of the invention provide methods for performing root cause analysis for non-deterministic anomalies in a datacenter. For instance, the method of some embodiments identifies a root cause for degradation in performance of one or more components in a network of the datacenter. This method collects and generates resource consumption data regarding resources consumed by a set of components in this network. The method performs a first analysis on the collected and/or generated data to identify an instance in time when one or more components, while still operational, are possibly suffering from performance degradation. The method then performs a second analysis on the collected and/or generated data associated with the identified time instance to identify a root cause of a performance degradation of at least one component in the network.

Abstract: Examples herein describe systems and methods for alarm prioritization in a Telco network. A prioritization engine can receive root cause problems and impacted network components from a network analysis module. The prioritization engine can define a mapping matrix that weights the problems according to network component type and service level. Using the problem weights, the prioritization engine can construct a problem-impact matrix that includes impact weights. The impact weights can be summed for each problem, as can the problem weights. The problems with the highest summed weights can then be prioritized first. The summed weights can also be based on predicted failure costs, such that the most expensive problems are prioritized first. The prioritization engine can send prioritized alerts for the problems for display on a graphical user interface (“GUI”).

Abstract: Examples herein describe systems and methods for alarm prioritization in a Telco network. A prioritization engine can receive root cause problems and impacted network components from a network analysis module. The prioritization engine can define a mapping matrix that weights the problems according to network component type and service level. Using the problem weights, the prioritization engine can construct a problem-impact matrix that includes impact weights. The impact weights can be summed for each problem, as can the problem weights. The problems with the highest summed weights can then be prioritized first. The summed weights can also be based on predicted failure costs, such that the most expensive problems are prioritized first. The prioritization engine can send prioritized alerts for the problems for display on a graphical user interface (“GUI”).

Abstract: Examples herein describe systems and methods for alarm prioritization in a Telco network. A prioritization engine can receive root cause problems and impacted network components from a network analysis module. The prioritization engine can define a mapping matrix that weights the problems according to network component type and service level. Using the problem weights, the prioritization engine can construct a problem-impact matrix that includes impact weights. The impact weights can be summed for each problem, as can the problem weights. The problems with the highest summed weights can then be prioritized first. The summed weights can also be based on predicted failure costs, such that the most expensive problems are prioritized first. The prioritization engine can send prioritized alerts for the problems for display on a graphical user interface (“GUI”).

Abstract: Some embodiments of the invention provide methods for performing root cause analysis for non-deterministic anomalies in a datacenter. For instance, the method of some embodiments identifies a root cause for degradation in performance of one or more components in a network of the datacenter. This method collects and generates resource consumption data regarding resources consumed by a set of components in this network. The method performs a first analysis on the collected and/or generated data to identify an instance in time when one or more components, while still operational, are possibly suffering from performance degradation. The method then performs a second analysis on the collected and/or generated data associated with the identified time instance to identify a root cause of a performance degradation of at least one component in the network.

Abstract: Some embodiments of the invention provide methods for performing root cause analysis for non-deterministic anomalies in a datacenter. For instance, the method of some embodiments identifies a root cause for degradation in performance of one or more components in a network of the datacenter. This method collects and generates resource consumption data regarding resources consumed by a set of components in this network. The method performs a first analysis on the collected and/or generated data to identify an instance in time when one or more components, while still operational, are possibly suffering from performance degradation. The method then performs a second analysis on the collected and/or generated data associated with the identified time instance to identify a root cause of a performance degradation of at least one component in the network.