Method and apparatus for locating faults in an optical network

• US 7,155,123 B2
• Filed: 05/10/2002
• Issued: 12/26/2006
• Est. Priority Date: 05/10/2002
• Status: Active Grant

• Current Assignee: RPX Corporation
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Levy, David S., Mitev, Peter H.
• Primary Examiner(s): SINGH, DALZID E
• Application Number:
  US10/142,475
• Publication Number:
  US 20030210908A1
• Time in Patent Office: 1,691 Days
• Field of Search: 398/1, 398/7, 398/10, 398/14, 398/17, 398/33, 398/18, 398/20, 398/58, 398/21, 370/242, 370/395.21
• US Class Current: 398/33
• CPC Class Codes: H04B 10/07 Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems
• CPC Class Codes: H04B 10/0791 Fault location on the transmission path
• CPC Class Codes: H04B 10/0793 Network aspects, e.g. central monitoring of transmission parameters
• CPC Class Codes: H04B 10/07953 Monitoring or measuring OSNR, BER or Q

Abstract

Method and apparatus for fault localization in an optical network using time trend correlation of end node performance parameters and intermediate node performance parameters, such as QoS and quasi-Q factors, respectively. The method and apparatus are bit-rate and protocol independent, enabling testing for each channel of a diverse dense wavelength division multiplexed traffic system using a single set of hardware.

Claims

1. A methods comprising:

monitoring historic and current optical layer performances of at least some of a plurality of network elements (NEs) forming a communications path within a communications network to extract thereby historic and current measures of performances;

correlating a historic time trend for the historic measure of optical layer performance of each NE with a historic time trend of an end node performance measurement to identify thereby historic fault conditions associated with the monitored NEs;

identifying a historic underperforming NE using a respective historic time trend correlation;

correlating a current time trend for the current measure of optical layer performance of each NE with a current time trend of an end node performance measurement to identify thereby future fault conditions associated with the monitored NEs; and

predicting a future underperforming NE by comparing the current time trend correlation with the historic time trend correlation.

2. The method of claim 1, wherein said measure of optical layer performance comprises performance data extracted from an eye diagram associated with a NE.

3. The method of claim 2, wherein said performance data extracted from an eye diagram comprises quasi-Q factor data.

4. The method of claim 1, wherein said end node performance measurement comprises performance data derived from a communications protocol layer.

5. The method of claim 4, wherein said communications protocol comprises one of a SONET protocol, an SDH protocol and a GigabitEthernet protocol.

6. The method of claim 1, wherein said end node performance measurement is determined using FibreChannel.

7. The method of claim 1, wherein said end node performance measurement comprises bit error rate (BER) data.

8. The method of claim 1, wherein said end node performance measurement comprises jitter data.

9. The method of claim 1, wherein said end node performance measurement comprises an optical layer performance measurement.

10. The method of claim 1, wherein;

said step of identifying an underperforming NE comprises determining that the correlation between the time trend data of respective optical layer performance and the time trend data of said end node performance measurement has diverged by a threshold amount.

11. The method of claim 10, wherein said threshold amount of divergence comprises an order of magnitude difference from a nominal correlation.

12. The method of claim 10, wherein said threshold amount is calculated as a percentage deviation from a nominal correlation level.

13. The method of claim 12, wherein said nominal correlation level is determined with respect to correlation trend data of said NE.

14. The method of claim 12, wherein said nominal correlation level is determined with respect to correlation trend data of a plurality of NEs.

15. A method, comprising:

monitoring historic and current optical layer performances for each of a plurality of intermediate network elements (NEs) in a communications path;

monitoring historic and current quality of service (QoS) performances for an end NE in the communications path; and

correlating historic time trend data for said monitored historic optical layer performance and said historic QoS performance;

determining a NE proximate a historic degradation in said communications path using said changes in historic correlation;

correlating current time trend data for said monitored current optical layer performance and said current QoS performance; and

predicting a future underperforming NE by comparing the changes in current correlation with historic changes in correlation.

16. The method of claim 15, wherein said measure of optical layer performance comprises performance data extracted from an eye diagram associated with a NE.

17. The method of claim 16, wherein said performance data extracted from an eye diagram comprises quasi-Q factor data.

18. The method of claim 15, wherein the end node performance monitoring comprises at least one of a bit error rate (BER) and a jitter level.

19. The method of claim 15, wherein the end node performance monitoring comprises an optical layer performance measurement.

20. The method of claim 15, wherein:

said step of determining a NE proximate a degradation comprises determining that the correlation between the rime trend data of respective optical layer performance and the time trend data of said end node QoS performance measurement has diverged by a threshold amount.

21. A network manager for managing a plurality of network elements (NEs) in a communications system, said network manager performing the steps of:

receiving, from each of a plurality of intermediate NEs in a communications path, historic and current data indicative of respective optical layer performance;

receiving, from an end node in said communications path, historic and current data indicative of a quality of service (QoS) performance level;

correlating, for each of said plurality of intermediate NEs, historic time trend data of respective optical layer performance and historic end node QoS performance;

in response to a degradation in said communications path, using said historic correlations to identify a NE proximate a historic degradation condition;

correlating, for each of said plurality of intermediate NEs, current time trend data of respective optical layer performance and current end node QoS performance; and

predicting a NE proximate a future degradation condition by comparing the current correlated time trend data with historic correlated time trend data.

22. The network manager of claim 21, wherein received optical layer performance data comprises performance data extracted from an eye diagram associated with a NE.

23. The network manager of claim 22, wherein said performance data extracted from an eye diagram comprises quasi-Q factor data.

24. The network manager of claim 21, wherein said end node performance data comprises at least one of a bit error rate (BER) and a jitter level.

25. A computer readable medium for storing software instructions which, when executed, perform the steps of:

receiving current and historic optical layer performance data for each of a plurality of intermediate network elements (NEs) in a communications path;

receiving current and historic quality of service (QoS) performance data for an end NE in the communications path;

correlating historic time trend data for said monitored historic optical layer performance data and said historic QoS performance data;

determining a NE proximate a historic degradation in said communications path using said changes in said correlation;

correlating current time trend data for monitored current optical layer performance data and current QoS performance data; and

predicting the NE proximate a future degradation by comparing the changes in the current correlation with historic time trend correlation.