System and methods for decomposing events from managed infrastructures that includes a feedback signalizer functor
An event clustering system that has an extraction engine in communication with a managed infrastructure. A signalizer engine includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The signalizer engine determines one or more common characteristics or features from events. The signalizer engine uses the common features of events to produce clusters of events relating to the failure or errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. A feedback signalizer functor is provided that is a supervised machine learning approach to train to reproduce a situation. In response to production of the clusters one or more physical changes in a managed infrastructure hardware is made, where the hardware supports the flow and processing of information.
1. An event clustering system, comprising:
an extraction engine in communication with a managed infrastructure;
a signalizer engine that includes one or more of a non-negative matrix factorization (NMF) engine, a k-means clustering engine and a topology proximity engine, the signalizer engine determining one or more common characteristics or features from events, the signalizer engine using the common features of events to produce clusters of events relating to the failure or errors in the managed infrastructure, where membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information;
a feedback signalizer functor that is a signalizing event analyzer which responds to user interactions with already formed situations; and
wherein in response to production of the clusters one or more physical changes in a managed infrastructure hardware is made, where the hardware supports the flow and processing of information.
2. The system of claim 1 , wherein the feedback signalizer functor is configured to learn how to replicate a same situation when a new alert occurs.
3. The system of claim 1 , wherein the feedback signalizer functor is configured to create similar situations when a new alert occurs.
4. The system of claim 1 , wherein the feedback signalizer functor is configured to train a signalizer functor to reproduce a situation at different degrees of precision.
5. The system of claim 1 , wherein the feedback signalizer functor is configured to train a signalizer functor to reproduce a situation at different degrees of precision with the use of neural networks.
6. The system of claim 1 , wherein the feedback signalizer functor is configured to train a signalizer functor to reproduce a situation at different degrees of precision with the use of a feed forward neural net with the standard configuration of an input layer, configurable hidden layers.
7. The system of claim 1 , wherein the feedback signalizer functor is configured to train a signalizer functor to reproduce a situation at different degrees of precision using a feed forward neural net with a standard configuration of an input layer.
8. The system of claim 1 , wherein the feedback signalizer functor is configured to train a signalizer functor to reproduce a situation at different degrees of precision using a feed forward neural net with a standard configuration of an input layer and configurable hidden layers.
9. The system of claim 1 , wherein the feedback signalizer functor provides deep learning with a single output layer.
10. The system of claim 1 , wherein the feedback signalizer functor is configured to respond to a configurable set of queues to learn and unlearn situations.
11. The system of claim 1 , wherein the feedback signalizer functor is configured to run in farmd.
12. The system of claim 1 , wherein the feedback signalizer functor listens for audited actions on a situation.
13. The system of claim 12 , wherein the audited actions are collected together into a set of collections of actions.
14. The system of claim 1 , wherein the extraction engine is configured to receive messages from the managed infrastructure and produce events that relate to the managed infrastructure.
15. The system of claim 1 , wherein the extraction engine converts the events into words and subsets used to group the events into clusters that relate to failures or errors in the managed infrastructure.
16. The system of claim 1 , wherein managed infrastructure hardware includes at least one of: computers, network devices, appliances, mobile devices, applications, connections of any of the preceding, and text or numerical values from which those text or numerical values which indicate a state of any hardware of the managed infrastructure.
17. The system of claim 1 , wherein the managed infrastructure generates data that includes attributes.
18. The system of claim 17 , wherein the data that includes attributes is selected from at least one of: time, source a description of the event, and textural or numerical values indicating a state of the managed infrastructure.
19. The system of claim 1 , wherein physical changes made to the managed infrastructure hardware that create physical and virtual links between the managed infrastructure and a system server.
20. The system of claim 1 , wherein physical changes made to the managed infrastructure includes changes to links from the server to a system high speed storage.
21. The system of claim 1 , wherein managed infrastructure generated data is selected from at least one of, time, source a description of: an event, textural or numerical values indicating a state of the managed infrastructure.