Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-aware service assurance in a Telco network. A machine learning engine can receive key performance indicators (“KPIs”) and physical faults related to virtual and physical network components, respectively. The machine learning engine can apply spatial and temporal analysis to define how models process the KPIs and faults and issue alerts for predictively remediating the network components. The machine learning engine can analyze the impact of these alerts on network health. This can include experimenting with different alert models and tuning how the machine learning engine processes the KPIs and faults based on which models are positively impacting network health compared to others. Based on newly detected patterns, event correlations, and anomalies, the machine learning engine can tune the model criteria to more accurately prevent problems from occurring.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.

Abstract: Examples herein describe systems and methods for self-healing in a Telco network function virtualization cloud. KPI attributes for virtual network functions can be mapped to physical fault notifications to create synthesized alerts. The synthesized alerts can include information from both a virtual and physical layer, allowing a self-healing action framework to determine root causes of problems in the Telco cloud. Remedial actions can then be performed in either the virtual or physical layer of the Telco cloud. Remedial actions in one layer can be based on root causes identified in the other, which can allow for remediation before network downtime occurs.