Method and apparatus for root cause and critical pattern prediction using virtual directed graphs
AssigneeVMware, Inc.
InventorsMazda A. MARVASTI, Arnak POGHOSYAN, Ashot HARUTYUNYAN, Naira GRIGORYAN
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Abstract
An approach to root cause determination in a complex systems based on monitoring and event data is disclosed. It includes a historical analysis of events with their probabilistic correlations. Applying information measures between the random variables which embody those events one can detect origins of problems and generate real-time recommendations for their locations in a hierarchical system. Estimation of system bottlenecks, as well as the risk of “black swan”-type events are also computed. The processes are based on a statistical processing of a virtual directed graph produced from historical events.
Claims20
A method for determining a causative event in an information technology infrastructure comprising:
- collecting historical event data with a computer;
- generating an undirected graph, each node being an event and each edge being a probability of those events historically occurring simultaneously;
- reducing the undirected graph;
- determining graph adjacency of the undirected graph;
- generating a directed graph, each node being the event and each edge being the conditional probability of a second event occurring if the first event occurs;
- and
- from the directed graph, creating and outputting an historical list of recommendations to a user, the historical list of recommendations comprising an at least one event deemed to be the most likely causative event.
A machine readable medium with instructions stored thereon for determining a causative event in an information technology infrastructure comprising:
- collecting historical event data with a computer;
- generating an undirected graph, each node being an event and each edge being a probability of those events historically occurring simultaneously;
- reducing the undirected graph;
- determining graph adjacency of the undirected graph;
- generating a directed graph, each node being the event and each edge being the conditional probability of a second event occurring if the first event occurs;
- and
- from the directed graph, creating and outputting an historical list of recommendations to a user, the historical list of recommendations comprising an at least one event deemed to be the most likely causative event.
Dependencies are inferred from the claim text.