Optic power monitoring system
The technology may be utilized in network environments that use fiber optic cables to connect components. Components of the network perform functions as transceivers that transmit and receive light signals via the fiber cable. When optic power data received from the transceivers is recognized as a fault, an event is identified. The system logs each received event and converts all measurements into a common unit of measurement. The system analyzes aggregated data from the transceivers to detect patterns and trends, monitor the received data and provide real time fault predictions. For example, a machine learning process may recognize subtle trends or patterns in the data, and use the recognition to predict potential failures. The system uses the received data to create a graphical user interface (“GUI”) that represents the health of the network.
1 . An optic power monitoring system, comprising:
one or more processors communicatively coupled to a storage device, wherein the one or more processors execute application code instructions that are stored in the storage device to cause the optic power monitoring system to:
provide a platform that receives data from a plurality of components in an optic network, the data comprising light measurements being transmitted or received by the plurality of components via one or more fiber optic cables;
aggregate the data from the plurality of components to generate aggregated data;
determine, by a machine learning algorithm, in real time that a fault is existing or likely to occur in at least one of the plurality of components based on a machine learning analysis of the aggregated data to identify trends or patterns in the aggregated data, wherein the machine learning algorithm uses relationships between or among optical power levels, actions of the plurality of components, and failure events;
generate a recommendation to a user to repair the fault; and
display the recommendation to the user on a graphical user interface.
2 . The optic power monitoring system of claim 1 , wherein the graphical user interface displays a color coded representation of a performance of each cable providing light to each component.
3 . The optic power monitoring system of claim 1 , further comprising creating, by the machine learning algorithm, a model of the optic network based on the aggregated data.
4 . The optic power monitoring system of claim 3 , wherein levels of light received in milliwatts is converted to decibels per milliwatt.
5 . The optic power monitoring system of claim 1 , wherein the aggregated data is comprised of one or more of optic power levels, device types, and system topology.
6 . The optic power monitoring system of claim 1 , wherein one or more optical power levels received is measured in decibels per milliwatt.
7 . The optic power monitoring system of claim 1 , wherein the application code instructions further cause the one or more processors to establish a threshold for received light measurements based on performance metrics from historical data.
8 . An optic power monitoring system, comprising:
one or more processors communicatively coupled to a storage device, wherein the one or more processors execute application code instructions that are stored in the storage device to cause the optic power monitoring system to:
provide a platform that receives data from optic network components, the data comprising light measurements being transmitted or received by the optic network components via one or more fiber optic cables;
process the data from the optic network components to store the data in a single unit of measure;
log events of low optical power measured by the optic network components to generate logged events;
determine, based on the logged events, that a fault is existing or likely to occur, wherein a determination is made by a machine learning algorithm based on relationships between optical or among power levels, actions of the optic network components, and failure events;
generate a graphical user interface that displays aggregated data, faults, and potential faults to a user; and
generate for display a recommendation to repair the fault or a potential fault.
9 . The optic power monitoring system of claim 8 , wherein the optic network components comprise one or more of an optic cable, a storage array, a channel switch, or a server, and the optic network components are provided by a plurality of manufacturers.
10 . The optic power monitoring system of claim 8 , wherein the fault is a low level of light received from an optic cable at a component.
11 . The optic power monitoring system of claim 8 , wherein the determination based on the logged events that the fault is existing or likely to occur is determined by an artificial intelligence process.
12 . The optic power monitoring system of claim 8 , wherein the determination based on the logged events that the fault is existing or likely to occur is based on an analysis of the aggregated data to identify trends or patterns in the aggregated data.
13 . The optic power monitoring system of claim 8 , wherein each level of light received is measured in decibels per milliwatt.
14 . The optic power monitoring system of claim 13 , wherein levels of light are received in milliwatts and converted to decibels per milliwatt.
15 . The optic power monitoring system of claim 8 , wherein the graphical user interface displays a color coded representation of a performance of each cable providing light to each component.
16 . The optic power monitoring system of claim 8 , wherein the application code instructions further cause the one or more processors to establish a threshold for received light measurements based on performance metrics from historical data.
17 . A method to monitor optic power, comprising:
providing a platform that receives data from optic network components in an optic network, the data comprising light measurements being transmitted or received by the optic network components via one or more fiber optic cables;
aggregating the data from the optic network components to generate aggregated data;
determining, by a machine learning algorithm, in real time that a fault is existing or likely to occur in at least one of the optic network components based on a machine learning analysis of the aggregated data to identify trends or patterns in the aggregated data, wherein the machine learning algorithm uses determined relationships between or among optical power levels, actions of the optic network components, and failure events;
generating a recommendation to a user to repair the fault; and
generating for display the recommendation to be displayed to the user on a graphical user interface.
18 . The method of claim 17 , wherein the graphical user interface displays a color coded representation of a performance of each cable providing light to each component.
19 . The method of claim 17 , further comprising creating, by the machine learning algorithm, a model of the optic network based on the aggregated data.