Computing diagnostic explanations of network faults from monitoring data
View Patent ↗A system and method for network fault diagnosis in a network having network elements is presented. The method comprises creating a network causality model, generating Boolean expressions from the network causality model, converting the Boolean expressions into SAT sets, receiving network monitoring results, correlating these monitoring results with the SAT sets, and enumerating all possible diagnostic explanations of potential faults, properly annotated. Creating a network causality model can comprise creating, for each network element, an element-specific causality model, stitching together all network elements using the element-specific causality models and a network topology, retrieving monitoring state and propagation information, and generating the network causality model using the stitched together network elements and the monitoring state and propagation information. Stitching together network elements can comprise adding causes and implies dependency between appropriate network elements and/or adding and connecting reachable and not-reachable states. The network causality model can comprise network element states.
1. A method for network fault diagnosis in a network having a plurality of network elements, comprising steps of:
creating a network causality model;
generating Boolean expressions from said network causality model;
converting said Boolean expressions into SAT sets;
receiving network monitoring results;
correlating said network monitoring results with said SAT sets; and
enumerating all possible diagnostic explanations of potential faults and displaying a ranked fault list having said explanations with annotations on a computer;
where the step of creating a network causality model comprising steps of:
for each network element of the plurality of network elements, creating an element-specific causality model;
stitching together all network elements using the plurality of element-specific causality models and a network topology;
retrieving monitoring state and propagation information; and
generating the network causality model using the stitched together network elements and the monitoring state and propagation information.
2. The method according to claim 1 , the step of stitching together network elements comprises at least one of:
adding a causes dependency and an implies dependency between appropriate network elements; and
adding and connecting reachable and not-reachable states.
3. The method according to claim 1 , wherein the element-specific causality model instantiates a type of network element.
4. The method according to claim 1 , wherein the plurality of network elements and element-specific causality models are organized into a taxonomy and categorized.
5. A non-transitory computer readable medium having computer readable program for operating on a computer for network fault diagnosis in a network having a plurality of network elements, said program comprising instructions that cause the computer to perform steps of:
creating a network causality model;
generating Boolean expressions from said network causality model;
converting said Boolean expressions into SAT sets;
receiving network monitoring results;
correlating said network monitoring results with said SAT sets; and
enumerating all possible diagnostic explanations of potential faults and displaying a ranked fault list having said explanations with annotations on a computer;
where the step of creating a network causality model comprising steps of:
for each network element of the plurality of network elements, creating an element-specific causality model;
stitching together all network elements using the plurality of element-specific causality models and a network topology;
retrieving monitoring state and propagation information; and
generating the network causality model using the stitched together network elements and the monitoring state and propagation information.
6. The non-transitory computer readable medium according to claim 5 , the step of stitching together network elements comprises at least one of:
adding a causes dependency and an implies dependency between appropriate network elements; and
adding and connecting reachable and not-reachable states.
7. The non-transitory computer readable medium according to claim 5 , wherein the element-specific causality model instantiates a type of network element.
8. The non-transitory computer readable medium according to claim 5 , wherein the plurality of network elements and element-specific causality models are organized into a taxonomy and categorized.
9. A system for network fault diagnosis in a network having a plurality of network elements, comprising:
a network causality model;
SAT sets; and
network monitoring results, wherein the network causality model generates the SAT sets using Boolean expressions, the network monitoring results and the SAT sets are correlated to enumerate all possible diagnostic explanations of potential faults, and a ranked fault list having said explanations with annotations are displayed on a display device further comprising:
a plurality of element-specific causality models, each element-specific causality model created from one of the plurality of network elements;
a network topology; and
monitoring state and propagation information, wherein the plurality of network elements are stitched together using the plurality of element-specific causality models and the network topology, and the network causality model is generated using the stitched together network elements and the monitoring state and propagation information.
10. The system according to claim 9 , wherein the stitching together of the network elements comprises at least one of:
adding a causes dependency and an implies dependency between appropriate network elements; and
adding and connecting reachable and not-reachable states.
11. The system according to claim 9 , wherein the element-specific causality model instantiates a type of network element.
12. The system according to claim 9 , wherein the plurality of network elements and element-specific causality models are organized into a taxonomy and categorized.