System and method for monitoring performance of arbitrary groupings of network infrastructure and applications

• US 20060020923A1
• Filed: 06/15/2005
• Published: 01/26/2006
• Est. Priority Date: 06/15/2004
• Status: Abandoned Application

• Current Assignee: K5 Systems Incorporated
• Original Assignee: K5 Systems Incorporated
• Inventors: Chung, Richard Y., Lo, Kevin H.
• Application Number:
  US11/152,964
• Publication Number:
  US 20060020923A1
• Time in Patent Office: Days
• US Class Current: 717/127
• 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 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• 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 2201/81 Threshold
• CPC Class Codes: G06F 2201/835 Timestamp
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: H04L 41/0213 Standardised network management protocols, e.g. simple network management protocol [SNMP]
• CPC Class Codes: H04L 41/0681 Configuration of triggering conditions
• CPC Class Codes: H04L 41/0893 Assignment of logical groups to network elements
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• CPC Class Codes: H04L 41/5032 Generating service level reports
• CPC Class Codes: H04L 41/5054 Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
• CPC Class Codes: H04L 41/5064 Customer relationship management
• CPC Class Codes: H04L 43/00 Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 43/028 : by filtering
• CPC Class Codes: H04L 43/065 : related to network devices
• CPC Class Codes: H04L 43/067 : using time frame reporting
• CPC Class Codes: H04L 43/16 : Threshold monitoring
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Systems, methods and computer program products for monitoring performance of arbitrary groupings of network infrastructure and applications. A method, system and computer program monitor arbitrary groupings of executing software applications and execution infrastructure to detect deviations in performance. A group of software applications and execution infrastructure components is selected arbitrarily and without constraint to define a managed unit. Data from the software applications and execution infrastructure components of the at least one managed unit is acquired. The acquired data is statistically analyzed to identify deviations in operating behavior of the at least one managed unit to indicate a statistically significant probability that an operating anomaly exists within the at least one managed unit.

Claims

1. A method of monitoring arbitrary groupings of executing software applications and execution infrastructure to detect deviations in performance, said method comprising:

selecting a group of software applications and execution infrastructure components arbitrarily and without constraint to define a managed unit;

acquiring data from the software applications and execution infrastructure components of the at least one managed unit;

statistically analyze the acquired data to identify deviations in operating behavior of the at least one managed unit to indicate a statistically significant probability that an operating anomaly exists within the at least one managed unit.

2. The method of claim 1 wherein a second group of software applications and execution infrastructure components are selected arbitrarily and without constraint to define a second managed unit, and wherein the managed unit and second managed unit are non-mutually exclusive.

3. The method of claim 1 wherein the statistical analysis includes derivation of at least statistical means and standard deviations of at least a subset of data elements within the acquired data.

4. The method of claim 1 wherein the statistical analysis includes derivation of covariance matrices of at least a subset of data elements within the acquired time-series data.

5. The method of claim 1 wherein the statistical analysis includes a principal component analysis (PCA) data for at least a subset of data elements within the acquired time-series data.

6. The method of claim 1 wherein said acquired data includes monitored data.

7. The method of claim 6 wherein the monitored data includes SNMP data.

8. The method of claim 6 wherein the monitored data includes transactional response values.

9. The method of claim 6 wherein the monitored data includes trapped data.

10. The method of claim 1 wherein said acquired data includes business process data.

11. The method of claim 10 wherein the business process data describes a specified end-user process.

12. The method of claim 1 further including pre-processing data received from the at least one managed unit.

13. The method of claim 1 wherein the statistical analysis yields a single difference measurement to indicate the performance deviation of a given managed unit.

14. The method of claim 1 wherein the statistical analysis includes a training step to provide different weights to multiple statistical calculations within the statistical analysis.

15. The method of claim 14 wherein the training step includes user-configurable training weights.

16. The method of claim 1 wherein said data from the managed unit includes historical performance and availability data.

17. The method of claim 1 wherein the data is acquired as an in-band process.

18. The method of claim 1 wherein the data is acquired as an out-of-band process.

19. The method of claim 1 wherein the managed unit includes multiple third party software applications.

20. The method of claim 1 wherein the managed unit includes multiple third party execution infrastructure components.

21. The method of claim 2 wherein the managed unit includes multiple third party software applications that are distributed across the Internet and wherein the second managed unit includes multiple third party software applications that are distributed across the Internet.

22. The method of claim 1 wherein a second group of software applications and execution infrastructure components are selected arbitrarily and without constraint to define a second managed unit, and wherein the method also statistically analyzes the acquired data to identify deviations in operating behavior of the second managed unit to indicate a statistically significant probability that an operating anomaly exists within the at least one managed unit, and wherein the method operates on the first managed unit and the second managed unit simultaneously.

23. The method of claim 1 wherein the statistical analysis of the acquired data to identify deviations in operating behavior of the managed unit operates in real-time relative to the operation of the executing software applications and execution infrastructure of the managed unit.