Optimizing application performance with machine learning
Media, methods, and systems are disclosed for optimizing performance of a running application in connection with a group-based communication system. Log data is collected regarding prior metrics for applications that have encountered performance events. Application state information is monitored and a machine-learning model mapping application metrics to performance outcomes predicts whether the running application will encounter a performance event. The machine-learning model mapping application metrics to performance outcomes is trained based on the collected logs. Based on whether a degradation outcome will be impactful, an application performance parameter may be degraded.
1. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by a processor, perform a method for optimizing performance of a running application in connection with a group-based communication system, the method comprising:
collecting log data relating to prior performance of the running application in connection with the group-based communication system;
monitoring application state information regarding an application state of the running application;
based on the application state information and a machine-learning model, predicting a future performance event that will impact the running application,
wherein the machine-learning model maps one or more application metrics to one or more performance outcomes and is trained based on collected information associated with the log data; and
responsive to determining a first data object associated with an impactful outcome of the future performance event and a second data object associated with a non-impactful outcome of the future performance event, discarding the second data object associated with the non-impactful outcome of the future performance event while maintaining the first data object associated with the impactful outcome of the future performance event.
2. The non-transitory computer-readable media of claim 1 , wherein the one or more application metrics comprise:
a rate of incoming real-time messages received by the running application; and
a size of an application data store associated with the application state for rendering application details associated with the group-based communication system.
3. The non-transitory computer-readable media of claim 1 , wherein at least a part of the machine-learning model is periodically updated by the group-based communication system and transmitted to the running application to facilitate the running application in discarding the second data object associated with the non-impactful outcome of the future performance event.
4. The non-transitory computer-readable media of claim 3 , the method further comprising:
prefetching one or more group-based communication system objects from the group-based communication system based on the machine-learning model in anticipation of future access of the one or more group-based communication system objects by a user of the running application.
5. The non-transitory computer-readable media of claim 1 , wherein the machine-learning model is periodically updated and utilized by the group-based communication system to parameterize data provided to the running application in response to the running application requesting one or more group-based communication system objects.
6. The non-transitory computer-readable media of claim 5 , wherein the data provided to the running application is parameterized based on prior usage rates of data previously requested by one or more users of the group-based communication system.
7. The non-transitory computer-readable media of claim 1 , wherein the machine-learning model is periodically updated and utilized by the group-based communication system to facilitate prioritizing data to be pushed to the running application in anticipation of future access of a group-based communication system object by a user of the running application.
8. A method for optimizing performance of a running application in connection with a group-based communication system, the method comprising:
collecting log data relating to prior performance of the running application in connection with the group-based communication system;
monitoring application state information regarding an application state of the running application;
based on the application state information and a machine-learning model, predicting a future performance event that will impact the running application,
wherein the machine-learning model maps one or more application metrics to one or more performance outcomes and is trained based on collected information associated with the log data; and
responsive to determining a first data object associated with an impactful outcome of the future performance event and a second data object associated with a non-impactful outcome of the future performance event, discarding the second data object associated with the non-impactful outcome of the future performance event while maintaining the first data object associated with the impactful outcome of the future performance event.
9. The method of claim 8 , wherein the one or more application metrics comprise:
a rate of incoming real-time messages received by the running application; and
a size of an application data store associated with the application state for rendering application details associated with the group-based communication system.
10. The method of claim 8 , wherein at least a part of the machine-learning model is periodically updated by the group-based communication system and transmitted to the running application to facilitate the running application in discarding the second data object associated with the non-impactful outcome of the future performance event.
11. The method of claim 8 , further comprising:
prefetching one or more group-based communication system objects from the group-based communication system based on the machine-learning model in anticipation of future access of the one or more group-based communication system objects by a user of the running application.
12. The method of claim 8 , wherein the machine-learning model is periodically updated and utilized by the group-based communication system to parameterize data provided to the running application in response to the running application requesting one or more group-based communication system objects.
13. The method of claim 12 , wherein the data provided to the running application is parameterized based on prior usage rates of data previously requested by one or more users of the group-based communication system.
14. The method of claim 13 , wherein the machine-learning model is periodically updated and utilized by the group-based communication system to facilitate prioritizing data to be pushed to the running application in anticipation of future access of a group-based communication system object by a user of the running application.
15. A system comprising at least one processor and at least one non-transitory memory storing computer executable instructions that when executed by the at least one processor cause the system to carry out actions comprising:
collecting log data relating to prior performance of a running application in connection with a group-based communication system;
monitoring application state information regarding an application state of the running application;
based on the application state information and a machine-learning model, predicting a future performance event that will impact the running application,
wherein the machine-learning model maps one or more application metrics to one or more performance outcomes and is trained based on collected information associated with the log data; and
responsive to determining a first data object associated with an impactful outcome of the future performance event and a second data object associated with a non-impactful outcome of the future performance event, discarding the second data object associated with the non-impactful outcome of the future performance event while maintaining the first data object associated with the impactful outcome of the future performance event.
16. The system of claim 15 , wherein the one or more application metrics comprise:
a rate of incoming real-time messages received by the running application; and
a size of an application data store associated with the application state for rendering application details associated with the group-based communication system.
17. The system of claim 15 , wherein at least a part of the machine-learning model is periodically updated by the group-based communication system and transmitted to the running application to facilitate the running application in discarding the second data object associated with the non-impactful outcome of the future performance event.
18. The system of claim 15 , the actions further comprising:
prefetching one or more group-based communication system objects from the group-based communication system based on the machine-learning model in anticipation of future access of the one or more group-based communication system objects by a user of the running application.
19. The system of claim 15 , wherein the machine-learning model is periodically updated and utilized by the group-based communication system to parameterize data provided to the running application in response to the running application requesting one or more group-based communication system objects.
20. The system of claim 19 , wherein the data provided to the running application is parameterized based on prior usage rates of data previously requested by one or more users of the group-based communication system.