| CPC H04L 41/5025 (2013.01) [G06F 11/3428 (2013.01); H04L 12/66 (2013.01); H04L 41/0661 (2023.05); H04L 41/0816 (2013.01); H04L 43/062 (2013.01); H04L 43/0817 (2013.01); H04L 43/16 (2013.01); H04L 67/133 (2022.05); H04L 67/51 (2022.05); H04L 67/56 (2022.05)] | 20 Claims |
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1. A system comprising:
an application control plane including a machine learning model,
wherein a training set for the machine learning model includes a history of a plurality of anomalies, conditions or states associated with a detection of each of the plurality of anomalies, and one or more successful remedial actions; and
a service group of a microservice architecture application, the service group including a plurality of services that interact to perform an overall application function,
wherein each of the plurality of services includes an application programming interface (API) and a data plane proxy that is communicatively coupled to the application control plane,
wherein the application control plane is configured to:
inject API traffic into a starting service of the service group,
wherein each data plane proxy of a corresponding service of the service group is configured to:
generate, based on the API traffic, a report including one or more metrics,
wherein the machine learning model is configured to:
receive, from the data plane proxy of the corresponding service of the service group, the report including the one or more metrics,
make a determination, based on the one or more metrics and at least one system benchmark, that a network issue has begun to degrade a performance of the microservice architecture application,
identify an anomaly from the plurality of anomalies that corresponds to the network issue, and
select, based on the determination and the anomaly identified from the plurality of anomalies, a remedial action from the one or more successful remedial actions, and
wherein the application control plane is further configured to:
execute the remedial action.
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