# US 12,119,981 B2 · Improving software defined networking controller availability using machine learning techniques > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,119,981 B2 - Document ID: US12119981B2 - Status: Granted - Kind: B2 - Application number: 17/756,907 - Filed: 2019-12-05 - Published: 2023-01-19 - Granted: 2024-10-15 - Earliest claimed: 2019-12-05 - Assignees: Telefonaktiebolaget LM Ericsson (publ) - Applicants: Telefonaktiebolaget LM Ericsson (publ) - Inventors: Ashutosh Bisht; Siva Kumar Perumalla; Aakash Agarwal; Tanmoy Bhowmik; Hema Gopalakrishnan; Hanamantagoud V Kandagal - Web page: https://patentgrep.com/patent/US12119981B2 ## CPC - H04L 41/06: Management of faults, events, alarms or notifications - H04L 47/127: by using congestion prediction - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - H04L 41/147: for predicting network behaviour - H04L 41/16: using machine learning or artificial intelligence - H04L 43/0805: by checking availability - H04L 43/0876: Network utilisation, e.g. volume of load or congestion level - H04L 45/64: using an overlay routing layer - G06N 3/08: Learning methods - G06N 20/00: Machine learning - H04L 41/064: {involving time analysis} - H04L 41/0654: using network fault recovery (ring fault isolation or reconfiguration in loop networks without recovery actions by a network management system H04L12/437) - H04L 41/122: of virtualised topologies, e.g. software-defined networks [SDN] or network function virtualisation [NFV] - H04L 43/10: Active monitoring, e.g. heartbeat, ping or trace-route - H04L 43/16: Threshold monitoring - H04L 43/20: the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV - H04L 45/08: {Learning-based routing, e.g. using neural networks or artificial intelligence} - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method of managing a controller of a software defined networking (SDN) network is implemented by a computing device in the SDN network. The method includes receiving status information for the controller, receiving usage information for the operating environment, generating at least one failure prediction for the controller based on the received status information, and outputting prediction information for the at least one failure prediction. ## Claims ### Claim 1 A method of managing a software defined networking (SDN) controller of an SDN network implemented by a computing device in the SDN network, the method comprising: receiving status information for the SDN controller, wherein the status information for the SDN controller comprises network configuration and network events; receiving usage information for an operating environment, wherein the operating environment includes processors, memories, and network resources, and wherein the usage information comprises usage of the processors, memories, and network resources; generating, by use of a machine learning model, at least one failure prediction for the SDN controller based on the status information for the SDN controller, the usage information of the operating environment, historic status information of the SDN controller, and historic usage information of the operating environment, wherein the at least one failure prediction for the SDN controller is generated by the machine learning model utilizing the status information and the usage information as inputs to the machine learning model, and trained on historic status information of the SDN controller and historic usage information of the operating environment; determining whether the at least one failure prediction for the SDN controller exceeds a configured threshold; in response to the at least one failure prediction exceeding the configured threshold, outputting prediction information as an output from the machine learning model for the at least one failure prediction for the SDN controller, wherein the prediction information includes a probability of failure over a given time period and a root cause for failure of the SDN controller, wherein the root cause for failure is based on at least one input to the machine learning model; and sending the prediction information to a correction unit for the SDN controller or to the SDN controller to implement a corrective action by the SDN controller for the root cause for failure. ### Claim 2 (depends on 1) The method of claim 1, wherein the status information for the SDN controller and the usage information for the operating environment is received from a monitor. ### Claim 3 (depends on 1) The method of claim 1, wherein the status information for the SDN controller includes internal packet processing queue sizes. ### Claim 4 A non-transitory machine-readable storage medium comprising computer program code which, when executed by a computer managing a software defined networking (SDN) controller of an SDN network implemented by a computing device in the SDN network, causes performing of operations comprising: receiving status information for the SDN controller, wherein the status information for the SDN controller comprises network configuration and network events; receiving usage information for an operating environment, wherein the operating environment includes processors, memories, and network resources, and wherein the usage information comprises usage of the processors, memories, and network resources; generating, by use of a machine learning model, at least one failure prediction for the SDN controller based on the status information for the SDN controller, the usage information of the operating environment, historic status information of the SDN controller, and historic usage information of the operating environment, wherein the at least one failure prediction for the SDN controller is generated by the machine learning model utilizing the status information and the usage information as inputs to the machine learning model, and trained on historic status information of the SDN controller and historic usage information of the operating environment; determining whether the at least one failure prediction for the SDN controller exceeds a configured threshold; in response to the at least one failure prediction exceeding the configured threshold, outputting prediction information as an output from the machine learning model for the at least one failure prediction for the SDN controller, wherein the prediction information includes a probability of failure over a given time period and a root cause for failure of the SDN controller, wherein the root cause for failure is based on at least one input to the machine learning model; and sending the prediction information to a correction unit for the SDN controller or to the SDN controller to implement a corrective action by the SDN controller for the root cause for failure. ### Claim 5 (depends on 4) The non-transitory machine-readable storage medium of claim 4, wherein the status information for the SDN controller and the usage information for the operating environment is received from a monitor. ### Claim 6 (depends on 4) The non-transitory machine-readable storage medium of claim 4, wherein the status information for the SDN controller includes internal packet processing queue sizes. ### Claim 7 A computing device for managing a software defined networking (SDN) controller of an SDN network implemented by the computing device in the SDN network, the computing device comprising: a set of processors; and a non-transitory machine-readable medium having stored therein a prediction unit, the set of processors to execute the prediction unit to: receive status information for the SDN controller, wherein the status information for the SDN controller comprises network configuration and network events; receive usage information for an operating environment; generate, by use of a machine learning model, at least one failure prediction for the SDN controller based on the status information for the SDN controller, the usage information of the operating environment, historic status information of the SDN controller, and historic usage information of the operating environment, wherein the at least one failure prediction for the SDN controller is generated by the machine learning model utilizing the status information and the usage information as inputs to the machine learning model, and trained on historic status information of the SDN controller and historic usage information of the operating environment; determine whether the at least one failure prediction for the SDN controller exceeds a configured threshold; in response to the at least one failure prediction exceeding the configured threshold, output prediction information as an output from the machine learning model for the at least one failure prediction for the SDN controller, wherein the prediction information includes a probability of failure over a given time period and a root cause for failure of the SDN controller, wherein the root cause for failure is based on at least one input to the machine learning model; and send the prediction information to a correction unit for the SDN controller or to the SDN controller to implement a corrective action by the SDN controller for the root cause for failure. ### Claim 8 (depends on 7) The computing device of claim 7, wherein the status information for the SDN controller and the usage information for the operating environment is received from a monitor. ### Claim 9 (depends on 7) The computing device of claim 7, wherein the status information for the SDN controller includes internal packet processing queue sizes.