Method and apparatus for gathering statistical measures

• US 20050216241A1
• Filed: 03/28/2005
• Published: 09/29/2005
• Est. Priority Date: 03/29/2004
• Status: Abandoned Application

• Current Assignee: Certagon Ltd.
• Original Assignee: Certagon Ltd.
• Inventors: Nehab, Smadar, Entin, Gadi
• Application Number:
  US11/092,447
• Publication Number:
  US 20050216241A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0715 in a system implementing multitasking multitasking per se G06F9/46
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 11/366 using diagnostics G06F11/0703 takes precedence
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06F 2201/87 Monitoring of transactions
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

According to the invention, a data model and method and apparatus for performing content and context modeling are disclosed. The method dynamically classifies and gathers selective information on various monitored systems to detect content related problems and provide context for diagnosing the root cause of these problems. The selected, monitored information for classification is converted to a plurality of dimensions that may be preconfigured, added incrementally after the monitored system is in production, or when a need for more advanced analysis or for wider context arise.

Claims

1. A method for context modeling to detect content related problems in a monitored system and diagnose a root cause of said problems, said method comprising the steps of:

defining a data model comprising at least a plurality of dimensions and a plurality of tuple schemas;

wherein each of said plurality of dimensions defines content to be collected and each of said plurality of tuple schemas defines a context in which said content is analyzed;

collecting a plurality of raw objects on said monitored system;

dynamically deriving dimension values on said plurality of dimensions from said raw objects to generate events;

dynamically classifying each of said events to tuples based on the dimension values of each said events; and

for each of said tuples computing statistical measures based on at least one measure value defined in said tuple schema.

2. The method of claim 1, wherein said statistical measures of each of said tuples are aggregated over a specific interval.

3. The method of claim 2, wherein said step of computing statistical measures of each of said tuples further comprises the step of using cells to determine a baseline of said monitored system.

4. The method of claim 3, wherein said monitored system comprises an enterprise software application (ESA).

5. The method of claim 1, wherein each of said dimensions defines a monitored entity in said monitored system.

6. The method of claim 1, wherein each of said plurality of dimensions comprises at least one of:

a service, a function, a service link, a transaction, and an external system.

7. The method of claim 6, wherein said dimensions are incrementally added by a user.

8. The method of claim 6, wherein each of said plurality of tuple schemas comprises any of a service by function, a transaction by service, all services, and all functions.

9. The method of claim 8, wherein said tuple schemas are configured by a user.

10. The method of claim 1, wherein said measured value comprises any of a throughput, a response time, and a monetary value.

11. The method of claim 1, wherein each of said events comprises any of a canonical message, and an input data class.

12. The method of claim 11, wherein said canonical message comprises pairs of dimensions and dimension values.

13. The method of claim 1, wherein each of said raw objects comprises any of a service call, a raw message, and a system parameter.

14. A computer software product readable by a machine, tangibly embodying a program of instructions executable by the machine to implement a method for context modeling to detect content related problems in a monitored system and to diagnose a root cause of said problems, said method comprising the steps of:

defining a data model comprising a plurality of dimensions and a plurality of tuple schemas;

wherein each of said plurality of dimensions defines content to be collected and each of said plurality of tuple schemas defines a context in which said content is analyzed;

collecting a plurality of raw objects on said monitored system;

dynamically deriving dimension values on said plurality of dimensions from said raw objects to generate events;

dynamically classifying each of said events to tuples based on the dimension values of said events; and

for each of said tuples computing statistical measures based on at least one measure value defined in said tuple schema.

15. A context analyzer for performing context modeling of a monitored system, said context analyzer comprising:

a classifier for dynamically classifying a plurality of events to a plurality of tuples; and

a statistics calculator for calculating statistics according to at least one predefined measured value.

16. The context analyzer of claim 15, said classifier further comprising:

means for classifying said plurality of events to a plurality of tuples based on dimension values of said events.

17. The context analyzer of claim 16, wherein each said dimension values is associated with a dimension.

18. The context analyzer of claim 17, wherein said dimension is defined in a tuple schema.

19. The context analyzer of claim 18, said tuple schema comprising a definition of said measured value.

20. The context analyzer of claim 18, wherein said dimension comprises any of a service, a function, a service link, a transaction, and an external system.

21. The context analyzer of claim 18, wherein each of said tuple schema defines a relation between said dimension, wherein said relation is any of a service by function, a transaction by service, all services, and all functions.

22. The context analyzer of claim 18, wherein said measured value comprises any of a throughput, a response time, and a monetary value.

23. A method for performing content and context modeling, comprising the steps of:

collecting raw objects;

extracting dimension values from raw messages;

generating canonical messages;

updating relevant tuples based on said dimension values;

updating statistical measures;

saving statistical measures of a tuple in at least one cell; and

saving said cell in a database.

24. An automated monitoring system, comprising:

a plurality of data collectors, for capturing service call data;

a correlator for classifying raw objects received from said data collectors;

a context analyzer for analyzing events and classifying said events into corresponding tuples and calculating statistics accordingly; and

a database for receiving and storing said statistics.