Microservices application network control plane
Disclosed embodiments are directed at systems, methods, and architecture for operating a control plan of a microservices application. The control plane corresponds with data plane proxies associated with each of a plurality of APIs that make up the microservices application. The communication between the data plane proxies and the control plane enables automatic detection of service groups of APIs and automatic repair of application performance in real-time in response to degrading service node conditions.
1. A method comprising:
establishing a microservice architecture application including a plurality of services, the plurality of services are each an application program interface (API) performing a piecemeal function of an overall application function, each service including a data plane proxy wherein the data plane proxy reports operation of each service of the plurality of services to an application control plane;
establishing an application performance benchmark for each service in the microservice architecture application;
identifying an anomaly in application operation based on exceeding a threshold associated with the application performance benchmark, the anomaly occurring on a first node associated with a first service of the plurality of services;
in response to said identifying, automatically executing iterative remedial actions on the microservice architecture application wherein each remedial action executed is from a preconfigured list and determined based on the anomaly; and
between each iterative remedial action, performing an evaluation of the microservice architecture application as compared to the application performance benchmark wherein an identification that the anomaly has improved or is improving ends subsequent implementation of iterative remedial actions.
2. The method of claim 1 , wherein the preconfigured list orders available remedial actions to iterate through based on a type of anomaly identified.
3. The method of claim 1 , wherein an order of the preconfigured list is determined based on a machine learning model determination, the method further comprising:
determining that a given remedial action has improved or is improving the anomaly; and
submitting data indicative of the given remedial action and the anomaly to the machine learning model as additional training data.
4. The method of claim 1 , wherein the preconfigured list orders available remedial actions to iterate through based on a heuristic schema.
5. The method of claim 1 , wherein the preconfigured list includes reversing a previous remedial action.
6. A system comprising:
a memory associated with a processor, wherein the memory includes instructions configured to cause the processor to instantiate a microservices application architecture control plane enabling administrative control over a microservice architecture application;
a distributed plurality of computing devices each instantiating service nodes that each include an application program interface (API) performing a piecemeal function of an overall application function, each service node including a data plane proxy, wherein the data plane proxy reports application operation of each service node to the microservices application architecture control plane;
the microservices application architecture control plane configured to establish an application performance benchmark for each service node in the microservice architecture application and identify an anomaly in application operation based on exceeding a threshold associated with the application performance benchmark, the anomaly occurring on a first node associated with a first service of the plurality of services;
wherein in response to said identification, the microservices application architecture control plane is configured to automatically execute iterative remedial actions on the microservice architecture application wherein each remedial action executed is from a preconfigured list and determined based on the anomaly, and
between each iterative remedial action, performing an evaluation of the microservice architecture application as compared to the application performance benchmark wherein an identification that the anomaly has improved or is improving ends subsequent implementation of iterative remedial actions.
7. The system of claim 6 , wherein the preconfigured list orders available remedial actions to iterate through based on a type of anomaly identified.
8. The system of claim 6 , wherein an order of the preconfigured list is determined based on a machine learning model determination, and the control plane is further configured to:
determine that a given remedial action has improved or is improving the anomaly; and
submit data indicative of the given remedial action and the anomaly to the machine learning model as additional training data.
9. The system of claim 6 , wherein the preconfigured list orders available remedial actions to iterate through based on a heuristic schema.
10. The system of claim 6 , wherein the preconfigured list includes reversing a previous remedial action.
11. A method comprising:
establishing a microservice architecture application including a plurality of services, the plurality of services are each an application program interface (API) performing a piecemeal function of an overall application function, each service including a data plane proxy wherein the data plane proxy reports operation of each service of the plurality of services to an application control plane;
establishing an application performance benchmark for each service in the microservice architecture application;
identifying an anomaly in application operation based on exceeding a threshold associated with the application performance benchmark, the anomaly occurring on a first node associated with a first service of the plurality of services; and
in response to said identifying, automatically executing iterative remedial actions on the microservice architecture application wherein each remedial action executed is from a preconfigured list and determined based on the anomaly, wherein an order of the preconfigured list is determined based on a machine learning model determination;
determining that a given remedial action has improved or is improving the anomaly; and
submitting data indicative of the given remedial action and the anomaly to the machine learning model as additional training data.
12. The method of claim 11 , wherein the preconfigured list orders available remedial actions to iterate through based on a type of anomaly identified.
13. The method of claim 11 , further comprising:
between each iterative remedial action, performing an evaluation of the microservice architecture application as compared to the application performance benchmark wherein an identification that the anomaly has improved or is improving ends subsequent implementation of iterative remedial actions.
14. The method of claim 11 , wherein the preconfigured list includes reversing a previous remedial action.
15. A method comprising:
establishing a microservice architecture application including a plurality of services, the plurality of services are each an application program interface (API) performing a piecemeal function of an overall application function, each service including a data plane proxy wherein the data plane proxy reports operation of each service of the plurality of services to an application control plane;
establishing an application performance benchmark for each service in the microservice architecture application;
identifying an anomaly in application operation based on exceeding a threshold associated with the application performance benchmark, the anomaly occurring on a first node associated with a first service of the plurality of services;
in response to said identifying, automatically executing iterative remedial actions on the microservice architecture application wherein each remedial action executed is from a preconfigured list and determined based on the anomaly, wherein the preconfigured list orders available remedial actions to iterate through based on a heuristic schema.
16. The method of claim 15 , further comprising:
between each iterative remedial action, performing an evaluation of the microservice architecture application as compared to the application performance benchmark wherein an identification that the anomaly has improved or is improving ends subsequent implementation of iterative remedial actions.
17. The method of claim 15 , wherein the preconfigured list orders available remedial actions to iterate through based on a type of anomaly identified.
18. The method of claim 15 , wherein the preconfigured list includes reversing a previous remedial action.