Model for performance tuning applications
First Claim
1. A method for diagnosing an application, comprising:
- passing an initial data set describing the application and its current state to a knowledge base, said knowledge base defined as a decision tree having one or more diagnosis nodes representing leaf nodes, one or more analysis nodes representing non-leaf nodes, and one or more symptom branches connecting between analysis nodes and other analysis nodes, or between analysis nodes and diagnosis nodes;
traversing said decision tree by following branches corresponding to symptoms experienced by the application until a diagnosis node is reached; and
interrupting said traversing if an analysis node is reached that requests additional information regarding the application.
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Abstract
In order to diagnose applications, a specialized knowledge base may be created that is static upon creation but may become dynamic when traversed. The knowledge base may be defined as a decision tree having one or more diagnosis nodes, one or more analysis nodes, and one or more symptom branches. The diagnosis nodes are leaf nodes and indicate proposed diagnoses and/or proposed remedies for an application. The symptom branches may connect analysis nodes to other analysis nodes or analysis nodes to diagnosis nodes, and may indicate possible symptoms of the application. The analysis nodes may be non-leaf nodes and indicate information required to determine which symptom branches to follow during traversal. Additionally, the analysis nodes may indicate additional information required from a collector agent before traversal can be continued. This allows the knowledge base to be utilized dynamically, improving performance and reliability.
28 Citations
26 Claims
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1. A method for diagnosing an application, comprising:
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passing an initial data set describing the application and its current state to a knowledge base, said knowledge base defined as a decision tree having one or more diagnosis nodes representing leaf nodes, one or more analysis nodes representing non-leaf nodes, and one or more symptom branches connecting between analysis nodes and other analysis nodes, or between analysis nodes and diagnosis nodes;
traversing said decision tree by following branches corresponding to symptoms experienced by the application until a diagnosis node is reached; and
interrupting said traversing if an analysis node is reached that requests additional information regarding the application. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An apparatus for diagnosing an application, comprising:
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a memory;
an initial data set to decision tree passer coupled to said memory;
a decision tree traverser coupled to said initial data set to decision tree passer and to said memory; and
a traversal interrupter coupled to said decision tree traverser. - View Dependent Claims (13, 14)
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15. An apparatus for diagnosing an application, comprising:
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means for passing an initial data set describing the application and its current state to a knowledge base, said knowledge base defined as a decision tree having one or more diagnosis nodes representing leaf nodes, one or more analysis nodes representing non-leaf nodes, and one or more symptom branches connecting between analysis nodes and other analysis nodes, or between analysis nodes and diagnosis nodes;
means for traversing said decision tree by following branches corresponding to symptoms experienced by the application until a diagnosis node is reached; and
interrupting said traversing if an analysis node is reached that requests additional information regarding the application. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25)
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26. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method for diagnosing an application, comprising:
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passing an initial data set describing the application and its current state to a knowledge base, said knowledge base defined as a decision tree having one or more diagnosis nodes representing leaf nodes, one or more analysis nodes representing non-leaf nodes, and one or more symptom branches connecting between analysis nodes and other analysis nodes, or between analysis nodes and diagnosis nodes;
traversing said decision tree by following branches corresponding to symptoms experienced by the application until a diagnosis node is reached; and
interrupting said traversing if an analysis node is reached that requests additional information regarding the application.
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Specification