Dynamically adjusting sample rates based on performance of a machine-learning based model for performing a network assurance function in a network assurance system
First Claim
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1. A method comprising:
- receiving, at a network assurance service, data regarding a monitored network, the received data including data provided to the network assurance service on a push basis;
interleaving, by the network assurance service, additional data regarding the monitored network received by polling one or more network elements in the monitored network on a pull basis with the data provided to the network assurance service on the push basis;
analyzing, by the network assurance service, the additional data received on the pull basis interleaved with the data provided to the network assurance service on the push basis using a machine learning-based model for performing a network assurance function for the monitored network;
detecting, by the network assurance service, a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric;
determining, by the network assurance service, whether the lowered performance of the machine learning-based model is correlated with a sample rate of the received data; and
increasing, by the network assurance service, the sample rate of the received data when it is determined that the lowered performance of the machine-learning based model is correlated with the sample rate of the received data.
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Abstract
In one embodiment, a network assurance service receives data regarding a monitored network. The service analyzes the received data using a machine learning-based model, to perform a network assurance function for the monitored network. The service detects a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric. When it is determined that the lowered performance of the machine-learning based model is correlated with the sample rate of the received data, the service adjusts the sample rate of the data.
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Citations
16 Claims
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1. A method comprising:
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receiving, at a network assurance service, data regarding a monitored network, the received data including data provided to the network assurance service on a push basis; interleaving, by the network assurance service, additional data regarding the monitored network received by polling one or more network elements in the monitored network on a pull basis with the data provided to the network assurance service on the push basis; analyzing, by the network assurance service, the additional data received on the pull basis interleaved with the data provided to the network assurance service on the push basis using a machine learning-based model for performing a network assurance function for the monitored network; detecting, by the network assurance service, a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric; determining, by the network assurance service, whether the lowered performance of the machine learning-based model is correlated with a sample rate of the received data; and increasing, by the network assurance service, the sample rate of the received data when it is determined that the lowered performance of the machine-learning based model is correlated with the sample rate of the received data. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An apparatus comprising:
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one or more network interfaces to communicate with a monitored network; a memory configured to store computer program instructions for performing a process; and a processor coupled to the one or more network interfaces and configured to execute the computer program instructions, wherein, upon execution of the program instructions, the processor is configured to; receive data regarding the monitored network, the received data including data provided to the network assurance service on a push basis; interleaving, by the network assurance service, additional data regarding the monitored network received by polling one or more network elements in the monitored network on a pull basis with the data provided to the network assurance service on the push basis; analyze the additional data received on the pull basis interleaved with the data provided to the network assurance service on the push basis using a machine learning-based model for performing a network assurance function for the monitored network; detect a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric; determine whether the lowered performance of the machine learning-based model is correlated with a sample rate of the received data; and increase the sample rate of the received data when it is determined that the lowered performance of the machine learning-based model is correlated with the sample rate of the received data. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A tangible, non-transitory, computer-readable medium storing program instructions that cause a network assurance service to execute a process comprising:
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receiving, at the network assurance service, data regarding a monitored network, the received data including data provided to the network assurance service on a push basis; interleaving, by the network assurance service, additional data regarding the monitored network received by polling one or more network elements in the monitored network on a pull basis with the data provided to the network assurance service on the push basis; analyzing, by the network assurance service, the additional data received on the pull basis interleaved with the data provided to the network assurance service on the push basis using a machine learning-based model for performing a network assurance function for the monitored network; detecting, by the network assurance service, a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric; determining, by the network assurance service, whether the lowered performance of the machine learning-based model is correlated with a sample rate of the received data; and increasing, by the network assurance service, the sample rate of the received data when it is determined that that the lowered performance of the machine learning-based model is correlated with the sample rate of the received data. - View Dependent Claims (16)
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Specification