Method and system for proactively resolving application upgrade issues using a device emulation system of a customer environment

1. A method for managing a client environment, the method comprising:

obtaining, by a remediation orchestrator, a remediation request associated with a failed application upgrade on an emulation of a client device, wherein:

the client device is executing on a client environment, and

the failed application upgrade is performed in a device emulation container in a device emulation system, wherein the device emulation container comprises the emulation of the client device;

in response to the remediation request:

obtaining a remediation policy associated with the application upgrade;

obtaining application upgrade information associated with the application upgrade;

identifying remediation steps to service the remediation request using the application upgrade information and the remediation policy by:

initiating performance of basic remediation steps;

making a first determination that the basic remediation steps are not successful;

in response to the first determination:

identifying a previous application upgrade failure using a first machine learning algorithm, the application upgrade information, and an application remediation upgrade repository; and

initiating performance of first remediation steps associated with the previous application upgrade failure on the device emulation container;

initiating performance of the application upgrade and the remediation steps on the client device;

making a second determination that the remediation steps associated with the previous application upgrade failure are not successful;

in response to the second determination:

initiating performance of second remediation steps using a second machine learning algorithm on the device emulation container;

making a third determination that the second remediation steps are not successful;

in response to the third determination:

making a fourth determination that a retry count limit is not reached, wherein the remediation policy specifies the retry count limit; and

in response to the fourth determination:

 initiating performance of third remediation steps using the second machine learning algorithm on the device emulation container.

2. The method of claim 1 , wherein first machine learning algorithm comprises a k-nearest neighbors algorithm.

3. The method of claim 1 , wherein the second machine learning algorithm comprises a reinforcement learning algorithm.

4. The method of claim 1 , wherein the remediation steps comprise the third remediation steps.

5. A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for managing a client environment, the method comprising:

obtaining, by a remediation orchestrator, a remediation request associated with a failed application upgrade on an emulation of a client device, wherein:

the client device is executing on a client environment, and

the failed application upgrade is performed in a device emulation container in a device emulation system, wherein the device emulation container comprises the emulation of the client device;

in response to the remediation request:

obtaining a remediation policy associated with the application upgrade;

obtaining application upgrade information associated with the application upgrade;

identifying remediation steps to service the remediation request using the application upgrade information and the remediation policy by:

initiating performance of basic remediation steps;

making a first determination that the basic remediation steps are not successful;

in response to the first determination:

identifying a previous application upgrade failure using a first machine learning algorithm, the application upgrade information, and an application remediation upgrade repository; and

initiating performance of first remediation steps associated with the previous application upgrade failure on the device emulation container;

initiating performance of the application upgrade and the remediation steps on the client device;

making a second determination that the remediation steps associated with the previous application upgrade failure are not successful;

in response to the second determination:

initiating performance of second remediation steps using a second machine learning algorithm on the device emulation container;

making a third determination that the second remediation steps are not successful;

in response to the third determination:

making a fourth determination that a retry count limit is not reached, wherein the remediation policy specifies the retry count limit; and

in response to the fourth determination:

 initiating performance of third remediation steps using the second machine learning algorithm on the device emulation container.

6. The non-transitory computer readable medium of claim 5 , wherein first machine learning algorithm comprises a k-nearest neighbors algorithm.

7. The non-transitory computer readable medium of claim 5 , wherein the second machine learning algorithm comprises a reinforcement learning algorithm.

8. The non-transitory computer readable medium of claim 5 , wherein the remediation steps comprise the third remediation steps.

9. A system comprising:

a device emulation system;

a client environment;

a processor; and

memory comprising instructions, which when executed by the processor, perform a method comprising:

obtaining, by a remediation orchestrator, a remediation request associated with a failed application upgrade on an emulation of a client device, wherein:

the client device is executing on a client environment, and

the failed application upgrade is performed in a device emulation container in a device emulation system, wherein the device emulation container comprises the emulation of the client device;

in response to the remediation request:

obtaining a remediation policy associated with the application upgrade;

obtaining application upgrade information associated with the application upgrade;

identifying remediation steps to service the remediation request using the application upgrade information and the remediation policy by:

initiating performance of basic remediation steps;

making a first determination that the basic remediation steps are not successful;

in response to the first determination:

identifying a previous application upgrade failure using a first machine learning algorithm, the application upgrade information, and an application remediation upgrade repository; and

initiating performance of first remediation steps associated with the previous application upgrade failure on the device emulation container;

initiating performance of the application upgrade and the remediation steps on the client device;

making a second determination that the remediation steps associated with the previous application upgrade failure are not successful;

in response to the second determination:

initiating performance of second remediation steps using a second machine learning algorithm on the device emulation container;

making a third determination that the second remediation steps are not successful;

in response to the third determination:

making a fourth determination that a retry count limit is not reached, wherein the remediation policy specifies the retry count limit; and

in response to the fourth determination:

 initiating performance of third remediation steps using the second machine learning algorithm on the device emulation container.