DEVICE AND METHOD FOR FAULT MANAGEMENT OF SMART DEVICE

• US 20140115400A1
• Filed: 09/16/2013
• Published: 04/24/2014
• Est. Priority Date: 10/23/2012
• Status: Active Grant

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: MOON, Yong-Hyuk, KIM, Jeong-Nyeo, CHUNG, Bo-Heung, KIM, Dae-Won, MOON, Hwa-Shin, HAN, Jin-Hee
• Granted Patent: US 9,235,463 B2
• Time in Patent Office: Days
• US Class Current: 714/37
• CPC Class Codes: G06F 11/0748 in a remote unit communicating with a single-box computer node experiencing an error/fault remote testing G06F11/2294
• 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 11/0793 Remedial or corrective actions recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: H04L 41/0631 using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
• CPC Class Codes: H04L 41/0883 Semiautomatic configuration, e.g. proposals from system

Abstract

There is provided a method of fault management of a smart device including comparing a value of a fault detection indicator (hereinafter referred to as ‘FDI’) in a normal state, which detects faults generated in the smart device, with respect to at least one performance indicator, with an FDI value observed in real time and detecting the faults by calculating a relative variation level of the observed values, and creating a diagnosis object (hereinafter referred to as ‘DO’) including a cause and a countermeasure of the detected fault and analyzing the fault.

Claims

1. A method of fault management of a smart device, comprising:

comparing a value of a fault detection indicator (hereinafter referred to as ‘

FDI’

) in a normal state, which detects faults generated in the smart device, with respect to at least one performance indicator, with an FDI value observed in real time and detecting the faults by calculating a relative variation level of the observed values; and

creating a diagnosis object (hereinafter referred to as ‘

DO’

) including a cause and a countermeasure of the detected fault and analyzing the fault.

2. The method according to claim 1, wherein the performance indicators include at least one of network delay, channel bandwidth, and CPU load.

3. The method according to claim 1, further comprising generating a profile including the FDI value in a normal state, before detecting the faults.

4. The method according to claim 3, wherein the generating of the profile includes:

monitoring FDI values when each performance indicator is in a normal state;

calculating an average value and a variance value of the monitored FDI values;

identifying a probability distribution model that is the most appropriate for an individual FDI by analyzing the calculated average value and variance values; and

storing profiles including the average value, the variance value, and the probability distribution model of the FDI values.

5. The method according to claim 4, further comprising extracting average values of monitored resulting values of the FDI values at a predetermined interval after the monitoring of the FDI values, wherein the calculating of the average value includes calculating an average of the average values at a predetermined interval of monitored resulting values of the FDI values.

6. The method according to claim 4, wherein the generating of the profile is repeatedly performed under different conditions including a measured time zone, a target device, a target application, and a target event.

7. The method according to claim 1, wherein the detecting of the fault includes:

collecting FDI observation values of the smart device;

calculating an average value of the collected FDI observation values;

detecting a profile appropriate for the observed FDI;

changing the average value of the calculated observation values to a random variable of a specific probability distribution model using information on the detected profile;

calculating an FDI level using the random variable of the specific probability distribution model; and

determining whether the fault is generated based on the calculated FDI level value.

8. The method according to claim 7, wherein the detecting of the profile includes detecting a corresponding profile by considering at least one of a time zone in which the FDI is observed, a target application service, and the smart device.

9. The method according to claim 7, wherein the determining whether the fault is generated includes determining that a fault is generated when an absolute value of the calculated FDI level value is not 0

10. The method according to claim 1, wherein the analyzing of the fault includes:

listing at least one repair configured with a pair of a subset of the FDI and the DO;

checking association of the at least one DO; and

selecting a DO having the highest relevance value in the at least one DO.

11. The method according to claim 10, wherein the checking of the association includes:

calculating a relative occurrence frequency of the FDI level in repairs including a specific DO; and

calculating an association degree between the specific DO and the FDI according to the calculated relative occurrence frequency.

18. The device according to claim 10, further comprising a repair listing unit configured to list at least one repair configured with a pair of a subset of the FDI and the DO.

19. The device according to claim 18, wherein the repair listing unit is configured to sort the listed at least one repair based on specific criteria (for example, date of repair generation).

12. A device for fault management of a smart device comprising:

an FDI level calculating unit configured to compare a value of a fault detection indicator (hereinafter referred to as ‘

FDI’

) in a normal state, which detects faults generated in the smart device, with respect to at least one performance indicator, with an FDI value observed in real time and detect the faults by calculating a relative variation level of the observed values;

an association degree checking unit configured to check association with a diagnosis object (hereinafter referred to as ‘

DO’

) including a cause and a countermeasure of the detected fault; and

a relevance estimating unit configured to compare an association degree value checked by the association degree checking unit and create a DO having a maximum relevance value.

13. The device according to claim 12, further comprising:

a profile generating unit configured to generate a profile including the FDI value in a normal state; and

a profile DB configured to store the generated FDI profile,

wherein the FDI level calculating unit is configured to detect and use the FDI value in a normal state from the FDI profile DB.

14. The device according to claim 13, wherein the profile generating unit is configured to calculate an average value and a variance value of FDI values monitored when each performance indictor is in a normal state, identify a probability distribution model that is the most appropriate for an individual FDI by analyzing the calculated average value and variance is values, and store profiles including the average value, the variance value, and the probability distribution model of the FDI values in the FDI profile DB.

15. The device according to claim 14, wherein the profile generating unit is configured to extract average values of the monitored resulting values of the FDI values at a predetermined interval, and then calculate an average of the average values at a predetermined interval.

16. The device according to claim 14, wherein the profile generating unit is configured to repeatedly generate profiles under different conditions including a measured time zone, a target device, a target application, and a target event.

17. The device according to claim 12, wherein the FDI level calculating unit is configured to collect FDI observation values of the smart device, calculate an average of the collected FDI observation values, detect a profile appropriate for the observed FDI, change the calculated average to a random variable of a specific probability distribution model using the detected profile value, calculate an FDI level using the random variable of the specific probability distribution model, and determine whether the fault is generated based on the calculated FDI level value.

20. The device according to claim 12, wherein the association degree checking unit is configured to calculate a relative occurrence frequency of the FDI level in repairs including the specific DO, and calculate an association degree between the specific DO and the FDI according to the calculated relative occurrence frequency.