Network quality evaluation based on network metrics

1 . A method for evaluating performance of a Passive Optical Network (PON), the method comprising:

obtaining quantitative values of a plurality of network performance metrics for an optical network terminal (ONT) for a customer in the PON, the PON including an optical line terminal (OLT) optically connected to a plurality of optical network terminals (ONTs) via a plurality of optical fibers, wherein each of the quantitative values corresponds to a different network performance metric of the plurality of network performance metrics, and the network performance metrics include two or more of an alarm duration, a number of alarms, a session duration, a number of session stops, or a packet discard rate;

for each of the plurality of network performance metrics:

analyzing the quantitative value of the network performance metric to assign an individual qualitative value to the network performance metric based on the quantitative value of the network performance metric;

analyzing the individual qualitative values assigned to each of the plurality of network performance metrics to determine an overall qualitative value for the ONT; and

providing an indication of the overall qualitative value for the ONT to at least one of a user interface or another computing device.

2 . The method of claim 1 , further comprising:

determining that the overall qualitative value for the ONT is below a threshold overall quality; and

transmitting a control signal to automatically reboot a component of the PON in response to determining that the overall qualitative value is below the threshold overall quality.

3 . The method of claim 1 , wherein analyzing the individual qualitative values to determine an overall qualitative value includes:

analyzing the individual qualitative values assigned to each of the plurality of network performance metrics to determine the overall qualitative value for the ONT by inputting the plurality of network performance metrics into a machine learning model to obtain the overall qualitative value.

4 . The method of claim 3 , wherein the machine learning model is trained with training sets of network performance metrics labelled with known overall quality metrics qualitative values.

5 . The method of claim 1 ,

wherein each of the individual qualitative values is a category selected from the group of: excellent, good, fair, and bad; and

wherein analyzing the individual qualitative values to determine an overall qualitative value includes:

determining the overall qualitative value is excellent if each of the individual qualitative values is categorized as excellent;

determining the overall qualitative value is bad if at least one of the individual qualitative values is categorized as bad; and

determining the overall qualitative value is good or fair if at least one of the individual qualitative values is not categorized as excellent and each of the individual qualitative values is not categorized as bad.

6 . The method of claim 1 , wherein analyzing the individual qualitative values to determine the overall qualitative value for the ONT includes:

determining that at least one rule of a plurality of rules for determining overall qualitative values applies to the ONT based on the individual qualitative values; and

applying the at least one rule to the ONT to determine the overall qualitative value based on the individual qualitative values.

7 . The method of claim 1 , wherein analyzing each of the plurality of network performance metrics includes:

applying at least one rule, of a plurality of rules for assigning individual qualitative values, to at least one of the network performance metrics to assign an individual qualitative value to the at least one of the network performance metrics.

8 . The method of claim 7 , wherein:

the plurality of rules for assigning individual qualitative values includes at least a first rule and a second rule, wherein:

the first rule provides that an individual qualitative value assigned to at least one of the network performance metrics is categorized as excellent if the at least one of the network performance metrics falls within a first range associated with the at least one of the network performance metrics; and

the second rule provides that an individual qualitative value assigned to at least one of the network performance metrics is categorized as good or fair if the at least one of the network performance metrics falls within a second range associated with the at least one of the network performance metrics.

9 . The method of claim 8 , wherein:

the plurality of rules for assigning individual qualitative values further includes a third rule, wherein:

the third rule provides that an individual qualitative value assigned to at least one of the network performance metrics is categorized as bad if the at least one of the network performance metrics falls within a third range associated with the at least one of the network performance metrics.

10 . A computing device for evaluating performance of a Passive Optical Network (PON), the computing device comprising:

one or more processors; and

a non-transitory computer-readable memory storing instructions thereon that, when executed by the one or more processors, cause the computing device to:

obtain quantitative values of a plurality of network performance metrics for an optical network terminal (ONT) for a customer in the PON, the PON including an optical line terminal (OLT) optically connected to a plurality of optical network terminals (ONTs) via a plurality of optical fibers, wherein each of the quantitative values corresponds to a different network performance metric of the plurality of network performance metrics, and the network performance metrics include two or more of an alarm duration, a number of alarms, a session duration, a number of session stops, or a packet discard rate;

for each of the plurality of network performance metrics:

analyze the quantitative value of the network performance metric to assign an individual qualitative value to the network performance metric based on the quantitative value of the network performance metric;

analyze the individual qualitative values assigned to each of the plurality of network performance metrics to determine an overall qualitative value for the ONT; and

provide an indication of the overall qualitative value for the ONT to at least one of a user interface or another computing device.

11 . The computing device of claim 10 , wherein the instructions, when executed by the one or more processors, further cause the computing device to:

determine that the overall qualitative value for the ONT is below a threshold overall quality; and

transmit a control signal to automatically reboot a component of the PON in response to determining that the overall qualitative value is below the threshold overall quality.

12 . The computing device of claim 10 , wherein to analyze the individual qualitative values to determine an overall qualitative value, the instructions, when executed by the one or more processors, cause the computing device to:

analyze the individual qualitative values assigned to each of the plurality of network performance metrics to determine the overall qualitative value for the ONT by inputting the plurality of network performance metrics into a machine learning model to obtain the overall qualitative value.

13 . The computing device of claim 12 , wherein the machine learning model is trained with training sets of network performance metrics labelled with known overall qualitative values.

14 . The computing device of claim 10 ,

wherein each of the individual qualitative values is a category selected from the group of: excellent, good, fair, and bad; and

wherein to analyze the individual qualitative values to determine an overall qualitative value, the instructions, when executed by the one or more processors, cause the computing device to:

determine the overall qualitative value is excellent if each of the individual qualitative values is categorized as excellent;

determine the overall qualitative value is bad if at least one of the individual qualitative values is categorized as bad; and

determine the overall qualitative value is good or fair if at least one of the individual qualitative values is not categorized as excellent and each of the individual qualitative values is not categorized as bad.

15 . The computing device of claim 10 , wherein to analyze the individual qualitative values to determine the overall qualitative value for the ONT, the instructions, when executed by the one or more processors, cause the computing device to:

determine that at least one rule of a plurality of rules for determining overall qualitative values applies to the ONT based on the individual qualitative values; and

apply the at least one rule to the ONT to determine the overall qualitative value based on the individual qualitative values.

16 . The computing device of claim 10 , wherein to analyze each of the plurality of network performance metrics, the instructions, when executed by the one or more processors, cause the computing device to:

apply at least one rule, of a plurality of rules for assigning individual qualitative values, to at least one of the network performance metrics to assign an individual qualitative value to the at least one of the network performance metrics.

17 . The computing device of claim 16 , wherein:

the plurality of rules for assigning individual qualitative values includes at least a first rule and a second rule, wherein:

the first rule provides that an individual qualitative value assigned to at least one of the network performance metrics is categorized as excellent if the at least one of the network performance metrics falls within a first range associated with the at least one of the network performance metrics; and

the second rule provides that an individual qualitative value assigned to at least one of the network performance metrics is categorized as good or fair if the at least one of the network performance metrics falls within a second range associated with the at least one of the network performance metrics.

18 . The computing device of claim 17 , wherein:

the plurality of rules for assigning individual qualitative values further includes a third rule, wherein:

the third rule provides that an individual qualitative value assigned to at least one of the network performance metrics is categorized as bad if the at least one of the network performance metrics falls within a third range associated with the at least one of the network performance metrics.

19 . A non-transitory computer-readable memory storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

obtain quantitative values of a plurality of network performance metrics for an optical network terminal (ONT) for a customer in a Passive Optical Network (PON), the PON including an optical line terminal (OLT) optically connected to a plurality of optical network terminals (ONTs) via a plurality of optical fibers, wherein each of the quantitative values corresponds to a different network performance metric of the plurality of network performance metrics, and the network performance metrics include two or more of an alarm duration, a number of alarms, a session duration, a number of session stops, or a packet discard rate;

for each of the plurality of network performance metrics:

analyze the quantitative value of the network performance metric to assign an individual qualitative value to the network performance metric based on the quantitative value of the network performance metric;

analyze the individual qualitative values assigned to each of the plurality of network performance metrics to determine an overall quality metric qualitative value for the ONT; and

provide an indication of the overall qualitative value for the ONT to at least one of a user interface or another computing device.

20 . The non-transitory computer-readable memory of claim 19 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

determine that the overall qualitative value for the ONT is below a threshold overall quality; and

transmit a control signal to automatically reboot a component of the PON in response to determining that the overall qualitative value is below the threshold overall quality.