# US 2019/0377652 A1 · Application health monitoring based on historical application health data and application logs > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0377652 A1 - Document ID: US20190377652A1 - Status: Published application - Kind: A1 - Application number: 16/000,916 - Filed: 2018-06-06 - Published: 2019-12-12 - Earliest claimed: 2018-06-06 - Assignees: VMware, Inc. - Applicants: Vmware, Inc. - Inventors: SIDHARTHA SAHOO; Vipul Chaudhary; Sandeep L. Hegde; Vimal Das Kammath Veerakumar; Arunvijai Sridharan - Web page: https://patentgrep.com/patent/US20190377652A1 ## CPC - G06F 11/3409: {for performance assessment} - G06F 11/302: {where the computing system component is a software system} - G06F 11/3428: {Benchmarking} - G06F 11/3447: {Performance evaluation by modeling} - G06F 11/3476: {Data logging (G06F11/14, G06F11/2205 take precedence)} - G06F 11/3688: {for test execution, e.g. scheduling of test suites} - G06N 20/00: Machine learning - G06F 2201/805: Real-time - G06F 2201/81: Threshold - G06F 2201/865: Monitoring of software - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Techniques for monitoring health of an application based on historical application health data and application logs are disclosed. In one embodiment, the historical application health data and the historical application logs associated with a period may be obtained. The application may include multiple services running therein. Priority of services may be determined based on the historical application health data associated with a portion of the period. Priority of exceptions associated with each of the services may be determined based on the historical application health data and the historical application logs associated with the portion of the period. Further, an application regression model may be trained by correlating the priority of the services, the associated priority of the exceptions, and the corresponding historical application health data. The health of the application may be monitored by analyzing real-time application logs using tested application regression model. ## Claims ### Claim 1 A method comprising: obtaining historical application health data and historical application logs associated with an application for a period; determining priority of services associated with the application based on the historical application health data associated with a portion of the period; determining priority of exceptions associated with each of the services based on the historical application health data and the historical application logs associated with the portion of the period; training an application regression model by correlating the priority of the services, the associated priority of the exceptions, and the corresponding historical application health data; testing the application regression model based on the historical application health data and the historical application logs associated with a remaining portion of the period; and real-time monitoring health of the application for an upcoming period using the application regression model based on the testing. ### Claim 2 (depends on 1) The method of claim 1, wherein real-time monitoring the health of the application comprises: retrieving real-time application logs associated with the application; and predicting the health of the application for the upcoming period by analyzing the real-time application logs using the application regression model. ### Claim 3 (depends on 1) The method of claim 1, wherein testing the application regression model comprises: predicting health data of the application for the remaining portion of the period using the trained application regression model; and determining accuracy of the trained application regression model by comparing the predicted health data of the application with the historical application health data associated with the remaining portion of the period, wherein the trained application regression model is used to monitor the health data of the application for the upcoming period when the accuracy is greater than or equal to a predefined threshold. ### Claim 4 (depends on 3) The method of claim 3, further comprising: retraining the trained application regression model with historical application health data and historical application logs associated with a modified period when the accuracy is less than the predefined threshold. ### Claim 5 (depends on 1) The method of claim 1, wherein the historical application health data comprise historical services health data of the services, and the historical application logs comprise historical service logs of the services. ### Claim 6 (depends on 5) The method of claim 5, wherein determining the priority of the services comprises: analyzing criticality of the services using a trained diagnosis model by correlating the historical application health data with the associated historical services health data; and determining the priority of the services by assigning weightages to the services based on the criticality of the services. ### Claim 7 (depends on 6) The method of claim 6, wherein the diagnosis model is trained using supervised machine learning and natural language processing methods. ### Claim 8 (depends on 5) The method of claim 5, wherein determining the priority of the exceptions associated with each of the services comprises: determining the exceptions associated with each of the services by analyzing the historical service logs using a trained component model; analyzing criticality of the exceptions associated with each of the services using the trained component model by correlating each exception with the associated historical services health data and the associated historical application health data; and determining the priority of the exceptions by assigning weightages to the exceptions based on the criticality of the exceptions. ### Claim 9 (depends on 8) The method of claim 8, wherein the component model is trained using supervised machine learning and natural language processing methods. ### Claim 10 A system comprising: a data training unit is to: obtain historical application health data and historical application logs associated with an application for a period; determine priority of services associated with the application based on the historical application health data associated with a portion of the period; determine priority of exceptions associated with each of the services based on the historical application health data and the historical application logs associated with the portion of the period; train an application regression model by correlating the priority of the services, the associated priority of the exceptions, and the corresponding historical application health data; and test the application regression model based on the historical application health data and the historical application logs associated with a remaining portion of the period; and a real-time application monitoring unit is to monitor health of the application for an upcoming period using the application regression model based on the testing. ### Claim 11 (depends on 10) The system of claim 10, wherein the real-time application monitoring unit is to: retrieve real-time application logs associated with the application; and predict the health of the application for the upcoming period by analyzing the real-time application logs using the application regression model. ### Claim 12 (depends on 10) The system of claim 10, wherein the data training unit is to: predict health data of the application for the remaining portion of the period using the trained application regression model; and determine accuracy of the trained application regression model by comparing the predicted health data of the application with the historical application health data associated with the remaining portion of the period, wherein the trained application regression model is used to monitor the health data of the application for the upcoming period when the accuracy is greater than or equal to a predefined threshold. ### Claim 13 (depends on 12) The system of claim 12, wherein the data training unit is to: retrain the trained application regression model with historical application health data and historical application logs associated with a modified period when the accuracy is less than the predefined threshold. ### Claim 14 (depends on 10) The system of claim 10, wherein the historical application health data comprise historical services health data of the services, and the historical application logs comprise historical service logs of the services. ### Claim 15 (depends on 14) The system of claim 14, wherein the data training unit is to: analyze criticality of the services using a trained diagnosis model by correlating the historical application health data with the associated historical services health data; and determine the priority of the services by assigning weightages to the services based on the criticality of the services. ### Claim 16 (depends on 14) The system of claim 14, wherein the data training unit is to: determine the exceptions associated with each of the services by analyzing the historical service logs using a trained component model; analyze criticality of the exceptions associated with each of the services using the trained component model by correlating each exception with the associated historical services health data and the associated historical application health data; and determine the priority of the exceptions by assigning weightages to the exceptions based on the criticality of the exceptions. ### Claim 17 A non-transitory machine-readable storage medium encoded with instructions that, when executed by a processor, cause the processor to: obtain historical application health data and historical application logs associated with an application for a period; determine priority of services associated with the application based on the historical application health data associated with a portion of the period; determine priority of exceptions associated with each of the services based on the historical application health data and the historical application logs associated with the portion of the period; train an application regression model by correlating the priority of the services, the associated priority of the exceptions, and the corresponding historical application health data; test the application regression model based on the historical application health data and the historical application logs associated with a remaining portion of the period; and monitor health of the application for an upcoming period using the application regression model based on the testing. ### Claim 18 (depends on 17) The non-transitory machine-readable storage medium of claim 17, wherein real-time monitoring the health of the application comprises: retrieving real-time application logs associated with the application; and predicting the health of the application for the upcoming period by analyzing the real-time application logs using the application regression model. ### Claim 19 (depends on 17) The non-transitory machine-readable storage medium of claim 17, wherein testing the application regression model comprises: predicting health data of the application for the remaining portion of the period using the trained application regression model; and determining accuracy of the trained application regression model by comparing the predicted health data of the application with the historical application health data associated with the remaining portion of the period, wherein the trained application regression model is used to monitor the health data of the application for the upcoming period when the accuracy is greater than or equal to a predefined threshold. ### Claim 20 (depends on 19) The non-transitory machine-readable storage medium of claim 19, further comprising instructions that, when executed by the processor, cause the processor to: retrain the trained application regression model with historical application health data and historical application logs associated with a modified period when the accuracy is less than the predefined threshold. ### Claim 21 (depends on 17) The non-transitory machine-readable storage medium of claim 17, wherein the historical application health data comprise historical services health data of the services, and the historical application logs comprise historical service logs of the services. ### Claim 22 (depends on 21) The non-transitory machine-readable storage medium of claim 21, wherein determining the priority of the services comprises: analyzing criticality of the services using a trained diagnosis model by correlating the historical application health data with the associated historical services health data; and determining the priority of the services by assigning weightages to the services based on the criticality of the services. ### Claim 23 (depends on 21) The non-transitory machine-readable storage medium of claim 21, wherein determining the priority of the exceptions associated with each of the services comprises: determining the exceptions associated with each of the services by analyzing the historical service logs using a trained component model; analyzing criticality of the exceptions associated with each of the services using the trained component model by correlating each exception with the associated historical services health data and the associated historical application health data; and determining the priority of the exceptions by assigning weightages to the exceptions based on the criticality of the exceptions.