System and method of automated fault correction in a network environment
Automated fault correction in a network environment comprises identifying a pattern in a set of network events and generating a set of substantiating data for the identified patterns. The method can also identify an occurrence probability value for each network event and generate root cause data based on a ranking for the network events using a set of parameters including the occurrence probability. The method can also be directed to performing a regression of the root cause data against a set of historic data and selecting the root cause with a predefined accuracy as an acceptable candidate. The acceptable candidate is then provided for assisted learning for automated fault correction.
1. A method of automated fault correction in a network environment implemented by at least one computing device, the method comprising:
identifying a pattern in a set of network events;
generating a set of substantiating data for the identified pattern;
determining an occurrence probability value for each network event;
generating root cause data based on a ranking for the network events using a set of parameters including the occurrence probability;
performing an automatically configured regression of the root cause data against a set of historic data comprising historic known root cause scenarios;
selecting a root cause with a predefined accuracy as an acceptable candidate based on the performed regression; and
presenting the acceptable candidate for assisted learning for automated fault correction.
2. The method of claim 1 , wherein the identifying the pattern further comprises automatically identifying patterns in the set of network events based on topology data, a historical alarm and historical event data.
3. The method of claim 1 , wherein the set of substantiating data is generated based on topology data and a set of time stamp data.
4. The method of claim 1 , wherein the historic event data comprises a set of parameters.
5. The method of claim 4 , wherein the set of parameters include one or more of an occurrence probability rank, severity, chronological order or a topological relationship value.
6. The method of claim 1 wherein the accuracy of the selected root cause is validated based on the historic event data.
7. A system for automated fault correction in a network environment, comprising:
a processor; and
a memory coupled to the processor configured to be capable of executing programmed instructions comprising and stored in the memory to:
identify a pattern in a set of network events;
generate a set of substantiating data for the identified pattern;
identify an occurrence probability value for each network event;
generate root cause data based on a ranking for the network events using a set of parameters including the occurrence probability;
perform an automatically configured regression of the root cause data against a set of historic data comprising historic known root cause scenarios;
select a root cause with a predefined accuracy as an acceptable candidate based on the performed regression; and
present the acceptable candidate for assisted learning for automated fault correction.
8. The system of claim 7 , wherein the identify the pattern further comprises automatically identifying the pattern in the set of network events based on topology data, a historical alarm and historical event data set.
9. The system of claim 7 , wherein the set of substantiating data is generated based on topology data and a set of time stamp data.
10. The system of claim 7 , wherein the historic event data comprises a set of parameters.
11. The system of claim 10 , wherein the set of parameters include one or more of an occurrence probability rank, severity, chronological order or a topological relationship value.
12. A non-transitory computer readable medium having stored thereon instructions for web browser classification comprising executable code that, when executed by one or more processors, causes the one or more processors to:
identify a pattern in a set of network events;
generate a set of substantiating data for the identified pattern;
identify an occurrence probability value for each network event;
generate root cause data based on a ranking for the network events using a set of parameters including the occurrence probability;
perform an automatically configured regression of the root cause data against a set of historic data comprising historic known root cause scenarios;
select a root cause with a predefined accuracy as an acceptable candidate based on the performed regression; and
present the acceptable candidate for assisted learning for automated fault correction.