Network pathway diagnosis

A method, comprising:

• accessing sets of training data, a first set of training data of the sets of training data defining an association between (a) a first issue relating to a first cluster of computing nodes configured for executing a first set of containerized applications and (b) one or more network pathways of a first computer network, wherein the first computer network comprises the first cluster of computing nodes;
• training a machine learning model to select network pathways in computer networks for diagnosis based on the sets of training data;
• detecting a second issue, the second issue relating to a second cluster of computing nodes configured for executing a second set of containerized applications, the second cluster of computing nodes comprised within a second computer network;
• accessing a target set of characteristics associated with the second issue;
• applying the machine learning model to the target set of characteristics to select a target network pathway in the second computer network for diagnosis;
• and
• based, at least in part, on applying the machine learning model to select the target network pathway, diagnosing the target network pathway, wherein diagnosing the target network pathway comprises:
• executing one or more diagnostics corresponding to the target network pathway, wherein executing a first diagnostic of the one or more diagnostics comprises evaluating connectivity of the target network pathway by analyzing a configuration of the second computer network,
• wherein the method is performed by at least one device including a hardware processor.

A system, comprising:

• at least one device including a hardware processor;
• the system being configured to perform operations comprising:
• accessing sets of training data, a first set of training data of the sets of training data defining an association between (a) a first issue relating to a first cluster of computing nodes configured for executing a first set of containerized applications and (b) one or more network pathways of a first computer network, wherein the first computer network comprises the first cluster of computing nodes;
• training a machine learning model to select network pathways in computer networks for diagnosis based on the sets of training data;
• detecting a second issue, the second issue relating to a second cluster of computing nodes configured for executing a second set of containerized applications, the second cluster of computing nodes comprised within a second computer network;
• accessing a target set of characteristics associated with the second issue;
• applying the machine learning model to the target set of characteristics to select a target network pathway in the second computer network for diagnosis;
• and
• based, at least in part, on applying the machine learning model to select the target network pathway, diagnosing the target network pathway, wherein diagnosing the target network pathway comprises:
• executing one or more diagnostics corresponding to the target network pathway, wherein executing a first diagnostic of the one or more diagnostics comprises evaluating connectivity of the target network pathway by analyzing a configuration of the second computer network.