A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for ranking detected anomalies, the method comprising: generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule; ranking nodes of the graph based on the edge weights; and ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies. 2. The method of claim 1, further comprising: alerting an end user of the detected anomalies based on the ranking of the detected anomalies. 3. The method of claim 1, wherein the plurality of rules are defined by an administrator for checking whether certain metrics in the rules meet particular conditions. 4. The method of claim 1, wherein the plurality of rules each comprises: at least one metric describing a state of one or more resources; and at least one condition, wherein each condition is defined for a corresponding metric. 5. The method of claim 1, wherein the detected anomalies are detected by observing for anomalous metrics and/or anomalous log through statistical analysis. 6. The method of claim 1, wherein edges are defined in the graph when two or more metrics and their corresponding conditions are connected in a given rule via a logical operation. 7. The method of claim 1, wherein edge weights are higher in value when a severity level is higher. 8. The method of claim 1, wherein value for a given edge weight of a given edge connecting two nodes varies depending on a type of logical operation which connects the metrics corresponding to the two nodes in a given rule. 9. The method of claim 1, wherein ranking nodes comprises: computing an importance value for a given node based on all edge weights of edges connected to the given node; and upon computing importance values for all nodes, ranking the nodes based on the importance values. 10. The method of claim 1, wherein ranking nodes comprises: computing an influence value for a given node and treating the graph as a social graph; and upon computing influence values for all nodes, ranking the nodes based on the influence values. 11. A system for ranking anomalies comprising: at least one memory element comprising instructions for a graph generation module, a node ranking module, and an anomaly ranking module; at least one processor coupled to the at least one memory element; and wherein the graph generation module that when executed by the at least one processor is configured to generate a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule; wherein the node ranking module that when executed by the at least one processor is configured to rank nodes of the graph based on the edge weight; wherein the anomaly ranking module that when executed by the at least one processor is configured to rank detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies 12. The system of claim 11, wherein the graph generation module that when executed by the at least one processor is further configured to defining edges in the graph by when two or more metrics and their corresponding conditions are connected in a given rule via a logical operation. 13. The system of claim 11, wherein the graph generation module that when executed by the at least one processor is further configured to assigning higher edge weight values when a severity level assigned to the given rule is higher. 14. The system of claim 11, wherein value for a given edge weight of a given edge connecting two nodes being assigned by the graph generation module varies depending on a type of logical operation which connects the metrics corresponding to the two nodes in a given rule. 15. The system of claim 11, wherein the node ranking module that when executed by the at least one processor is configured to, compute an importance value for a given node based on all edge weights of edges connected to the given node, and upon computing importance values for all nodes, rank the nodes based on the importance values. 16. A computer-readable non-transitory medium comprising one or more instructions, for identifying anomalies and alerting, that when executed on a processor configure the processor to perform one or more operations comprising: generate a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule; rank nodes of the graph based on the edge weights; and rank detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies. 17. The medium of claim 16, wherein the operations further comprise: alerting an end user of the detected anomalies based on the ranking of the detected anomalies. 18. The medium of claim 16, wherein the operations further comprise: assigning higher edge weight values when a severity level of the given rule is higher. 19. The medium of claim 16, wherein the operations further comprise: varying edge weight values based on a type of logical operation connecting the metrics in the given rule. 20. The medium of claim 16, wherein the operations further comprise: computing an influence value for a given node and treating the graph as a social graph; and upon computing influence values for all nodes, ranking the nodes based on the influence values.