Migrating data based on user experience as a service when developing and deploying a cloud-based solution

A computer system for automatically migrating a cloud-based solution onto a datacenter based on machine-learned user profile data and user experience data, comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
• populating and maintaining a corpus of datacenters, wherein maintaining the corpus of datacenters further comprises detecting current and previous computing capabilities and performance of each datacenter in the corpus;
• identifying different types of users using the cloud-based solution and generating a user profile for each type of user based on the machine-learned user profile data, wherein identifying the different types of users and generating the user profile based on the machine-learned user profile data further comprises automatically identifying a specific type of user and one or more types of computing tasks executed by the specific type of user, and associating the one or more types of computing tasks with the specific type of user and with a time in which the specific type of user executes the one or more types of computing tasks;
• using a machine-learning algorithm to detect a user experience with the cloud- based solution on one or more datacenters from the corpus of datacenters for each type of user based on the machine-learned user experience data, wherein detecting the user experience based on the machine-learned user experience data further comprises detecting the one or more types of computing tasks being executed by the user on the cloud-based solution by the specific type of user and the performance of the one or more datacenters during execution of the one or more types of computing tasks, detecting clicks by the specific type of user on the cloud-based solution, detecting response times or an elapsed time for performing specific computing activities, latency issues, and buffering or lagging issues;
• using the machine-learning algorithm to correlate, for the specific type of user and for the one or more types of computing tasks associated with the specific type of user, the machine-learned user profile data with the machine-learned user experience data and the current and previous computing capabilities and performance of each datacenter in the corpus;
• and
• automatically migrating the cloud-based solution onto the datacenter from the corpus of datacenters for the specific type of user based on the correlation between the machine-learned user profile data, the machine-learned user experience data, and the current and previous computing capabilities and performance of the datacenter, wherein the automatically migrating further comprises determining that the datacenter is equipped with a threshold amount of computing resources and capabilities for executing the one or more types of computing tasks associated with the specific type of user based on the correlation between the machine-learned user profile data, the machine-learned user experience data, and the current and previous computing capabilities and performance of the datacenter.

A computer program product for automatically migrating a cloud-based solution onto a datacenter based on machine-learned user profile data and user experience data, comprising:

• one or more tangible computer-readable storage devices and program instructions stored on at least one of the one or more tangible computer-readable storage devices, the program instructions executable by a processor, the program instructions comprising:
• populating and maintaining a corpus of datacenters, wherein maintaining the corpus of datacenters further comprises detecting current and previous computing capabilities and performance of each datacenter in the corpus;
• identifying different types of users using the cloud-based solution and generating a user profile for each type of user based on the machine-learned user profile data, wherein identifying the different types of users and generating the user profile based on the machine-learned user profile data further comprises automatically identifying a specific type of user and one or more types of computing tasks executed by the specific type of user, and associating the one or more types of computing tasks with the specific type of user and with a time in which the specific type of user executes the one or more types of computing tasks;
• using a machine-learning algorithm to detect a user experience with the cloud- based solution on one or more datacenters from the corpus of datacenters for each type of user based on the machine-learned user experience data, wherein detecting the user experience based on the machine-learned user experience data further comprises detecting the one or more types of computing tasks being executed by the user on the cloud-based solution by the specific type of user and the performance of the one or more datacenters during execution of the one or more types of computing tasks, detecting clicks by the specific type of user on the cloud-based solution, detecting response times or an elapsed time for performing specific computing activities, latency issues, and buffering or lagging issues;
• using the machine-learning algorithm to correlate, for the specific type of user and for the one or more types of computing tasks associated with the specific type of user, the machine-learned user profile data with the machine-learned user experience data and the current and previous computing capabilities and performance of each datacenter in the corpus;
• and
• automatically migrating the cloud-based solution onto the datacenter from the corpus of datacenters for the specific type of user based on the correlation between the machine-learned user profile data, the machine-learned user experience data, and the current and previous computing capabilities and performance of the datacenter, wherein the automatically migrating further comprises determining that the datacenter is equipped with a threshold amount of computing resources and capabilities for executing the one or more types of computing tasks associated with the specific type of user based on the correlation between the machine-learned user profile data, the machine-learned user experience data, and the current and previous computing capabilities and performance of the datacenter.