Disentangled graph learning for incremental causal discovery and root cause analysis
A computer-implemented method for locating root causes is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, and locating the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.
1. A computer-implemented method for locating root causes, the method comprising:
detecting a trigger point from entity metrics data and key performance indicator (KPI) data;
generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph; and
locating the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.
2. The computer-implemented method of claim 1 , wherein the trigger point detection involves constructing an initial system state space by using a time-lagged trajectory matrix.
3. The computer-implemented method of claim 2 , wherein the time-lagged trajectory matrix leverages non-linear kernel-based matrix decomposition to learn a robust subspace.
4. The computer-implemented method of claim 1 , wherein a disentangle graph learning-based incremental causal discovery framework is employed to generate the learned causal graph.
5. The computer-implemented method of claim 1 , wherein a prediction layer is used to predict future time-series data on the learned causal graph.
6. The computer-implemented method of claim 1 , wherein, when the learned causal graph and a root cause list converge, a root causes report for system recovery is generated.
7. The computer-implemented method of claim 1 , wherein the learned causal graph is input into a root cause analysis algorithm to output top pods as the root causes of system failures.
8. A computer program product for locating root causes, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
detecting a trigger point from entity metrics data and key performance indicator (KPI) data;
generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph; and
locating the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.
9. The computer program product of claim 8 , wherein the trigger point detection involves constructing an initial system state space by using a time-lagged trajectory matrix.
10. The computer program product of claim 9 , wherein the time-lagged trajectory matrix leverages non-linear kernel-based matrix decomposition to learn a robust subspace.
11. The computer program product of claim 8 , wherein a disentangle graph learning-based incremental causal discovery framework is employed to generate the learned causal graph.
12. The computer program product of claim 8 , wherein a prediction layer is used to predict future time-series data on the learned causal graph.
13. The computer program product of claim 8 , wherein, when the learned causal graph and a root cause list converge, a root causes report for system recovery is generated.
14. The computer program product of claim 8 , wherein the learned causal graph is input into a root cause analysis algorithm to output top pods as the root causes of system failures.
15. A computer processing system for locating root causes, comprising:
a memory device for storing program code; and
a processor device, operatively coupled to the memory device, for running the program code to:
detect a trigger point from entity metrics data and key performance indicator (KPI) data;
generate a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph; and
locate the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.
16. The computer processing system of claim 15 , wherein the trigger point detection involves constructing an initial system state space by using a time-lagged trajectory matrix.
17. The computer processing system of claim 16 , wherein the time-lagged trajectory matrix leverages non-linear kernel-based matrix decomposition to learn a robust subspace.
18. The computer processing system of claim 15 , wherein a disentangle graph learning-based incremental causal discovery framework is employed to generate the learned causal graph.
19. The computer processing system of claim 15 , wherein a prediction layer is used to predict future time-series data on the learned causal graph.
20. The computer processing system of claim 15 , wherein, when the learned causal graph and a root cause list converge, a root causes report for system recovery is generated.