# US 2020/0401936 A1 · Self-aware service assurance in a 5g telco network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401936 A1 - Document ID: US20200401936A1 - Status: Published application - Kind: A1 - Application number: 16/535,121 - Filed: 2019-08-08 - Published: 2020-12-24 - Earliest claimed: 2019-06-20 - Applicants: Vmware, Inc. - Inventors: Radhakrishna Embarmannar Vijayan; Thatayya Naidu Venkata Polamarasetty - Web page: https://patentgrep.com/patent/US20200401936A1 ## CPC - G06N 20/00: Machine learning - G06F 11/3447: {Performance evaluation by modeling} - G06F 11/3452: {Performance evaluation by statistical analysis} - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06F 2201/81: Threshold - G06F 2201/815: Virtual - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## 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. ## Claims ### Claim 1 A method for self-aware service assurance for a software-defined data center (“SDDC”), comprising: receiving key performance indicators (“KPIs”) of a virtual component in the SDDC; receiving physical fault information from a physical component in the SDDC; issuing an alert based on a model that specifies symptoms and a problem associated with the symptoms, wherein the symptoms are selected based on spatial analysis that links events at the virtual component and the physical component, wherein the symptoms include dynamic KPI thresholds, and wherein the alert notifies an orchestrator to perform a corrective action; analyzing, by a machine learning engine, network stability related to the virtual and physical components; and based on the analysis of network stability, tuning the model symptoms. ### Claim 2 (depends on 1) The method of claim 1, further comprising receiving, on a graphical user interface (“GUI”), a user selection of which KPIs are used as symptoms in the model, wherein adjusting the model symptoms includes changing the symptoms to a new group of KPIs discovered by spatial analytics of the machine learning engine. ### Claim 3 (depends on 1) The method of claim 1, wherein tuning the model symptoms includes changing KPI thresholds based on temporal analysis indicating a new pattern of KPI values for a period of time. ### Claim 4 (depends on 1) The method of claim 1, further comprising switching to a new machine learning algorithm based on analysis of the network stability, wherein tuning the model symptoms is based on results from the new machine learning algorithm. ### Claim 5 (depends on 1) The method of claim 1, further comprising storing the KPIs in a time series database in association with time periods, wherein the KPIs in the time series database are used to recognize KPI patterns, the KPI patterns being used to tune the model symptoms. ### Claim 6 (depends on 1) The method of claim 1, further comprising storing objects in a graph database to represent relationships between physical and virtual network components in the SDDC, wherein spatial analysis uses nodes from the graph database to determine which KPIs and faults to include as symptoms in the model. ### Claim 7 (depends on 1) The method of claim 1, wherein the model compares packet drop rates to a dynamic threshold, wherein the packet drop rates are analyzed in real time and based on historical values to determine a change to the dynamic threshold. ### Claim 8 A non-transitory, computer-readable medium comprising instructions that, when executed by a processor, perform stages for self-aware service assurance for a software-defined data center (“SDDC”), the stages comprising: receiving key performance indicators (“KPIs”) of a virtual component in the SDDC; receiving physical fault information from a physical component in the SDDC; issuing an alert based on a model that specifies symptoms and a problem associated with the symptoms, wherein the symptoms are selected based on spatial analysis that links events at the virtual component and the physical component, wherein the symptoms include dynamic KPI thresholds, and wherein the alert notifies an orchestrator to perform a corrective action; analyzing, by a machine learning engine, network stability related to the virtual and physical components; and based on the analysis of network stability, tuning the model symptoms. ### Claim 9 (depends on 8) The non-transitory, computer-readable medium of claim 8, the stages further comprising receiving, on a graphical user interface (“GUI”), a user selection of which KPIs are used as symptoms in the model, wherein adjusting the model symptoms includes changing the symptoms to a new group of KPIs discovered by spatial analytics of the machine learning engine. ### Claim 10 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein tuning the model symptoms includes changing KPI thresholds based on temporal analysis indicating a new pattern of KPI values for a period of time. ### Claim 11 (depends on 8) The non-transitory, computer-readable medium of claim 8, the stages further comprising switching to a new machine learning algorithm based on analysis of the network stability, wherein tuning the model symptoms is based on results from the new machine learning algorithm. ### Claim 12 (depends on 8) The non-transitory, computer-readable medium of claim 8, the stages further comprising storing the KPIs in a time series database in association with time periods, wherein the KPIs in the time series database are used to recognize KPI patterns, the KPI patterns being used to tune the model symptoms. ### Claim 13 (depends on 8) The non-transitory, computer-readable medium of claim 8, the stages further comprising storing objects in a graph database to represent relationships between physical and virtual network components in the SDDC, wherein spatial analysis uses nodes from the graph database to determine which KPIs and faults to include as symptoms in the model. ### Claim 14 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein the model compares packet drop rates to a dynamic threshold, wherein the packet drop rates are analyzed in real time and based on historical values to determine a change to the dynamic threshold. ### Claim 15 A system for performing self-aware service assurance for a software-defined data center (“SDDC”), comprising: a non-transitory, computer-readable medium containing instructions; and a processor that executes the instructions perform stages comprising: receiving key performance indicators (“KPIs”) of a virtual component in the SDDC; receiving physical fault information from a physical component in the SDDC; issuing an alert based on a model that specifies symptoms and a problem associated with the symptoms, wherein the symptoms are selected based on spatial analysis that links events at the virtual component and the physical component, wherein the symptoms include dynamic KPI thresholds, and wherein the alert notifies an orchestrator to perform a corrective action; analyzing, by a machine learning engine, network stability related to the virtual and physical components; and based on the analysis of network stability, tuning the model symptoms. ### Claim 16 (depends on 15) The system of claim 15, the stages further comprising receiving, on a graphical user interface (“GUI”), a user selection of which KPIs are used as symptoms in the model, wherein adjusting the model symptoms includes changing the symptoms to a new group of KPIs discovered by spatial analytics of the machine learning engine. ### Claim 17 (depends on 15) The system of claim 15, wherein tuning the model symptoms includes changing KPI thresholds based on temporal analysis indicating a new pattern of KPI values for a period of time. ### Claim 18 (depends on 15) The system of claim 15, the stages further comprising switching to a new machine learning algorithm based on analysis of the network stability, wherein tuning the model symptoms is based on results from the new machine learning algorithm. ### Claim 19 (depends on 15) The system of claim 15, the stages further comprising storing objects in a graph database to represent relationships between physical and virtual network components in the SDDC, wherein spatial analysis uses nodes from the graph database to determine which KPIs and faults to include as symptoms in the model. ### Claim 20 (depends on 15) The system of claim 15, wherein the model compares packet drop rates to a dynamic threshold, wherein the packet drop rates are analyzed in real time and based on historical values to determine a change to the dynamic threshold.