Machine learning-based resource customization to increase user satisfaction

A computer system for customizing computing resource allocation based on machine learning, the computer system comprising:

• a bus system;
• a storage device connected to the bus system, wherein the storage device stores program instructions;
• and
• a processor connected to the bus system, wherein the processor executes the program instructions to:
• evaluate a plurality of current users of a service based on attributes of each respective current user in the plurality of current users;
• generate a user value score for each respective current user in the plurality of current users based on the attributes of each respective current user;
• cluster the plurality of current users into a plurality of user groups based on the user value score of each respective current user in the plurality of current users;
• predict, using the machine learning, a likelihood of each prospective user in a plurality of prospective users becoming a paying customer based on collected behavior data corresponding to each prospective user;
• predict, using the machine learning, a likelihood of an application corresponding to the service and associated with a particular user will misbehave based on resource usage of the application and features of the application;
• customize resource allocation to the application corresponding to the service and associated with the particular user based on the likelihood that the application will misbehave and whether the particular user is a current user or a prospective user;
• and
• select the particular user from one of the plurality of current users and the plurality of prospective users, wherein the customized resource allocation represents a customized allocation of cloud resources to the application, and the cloud resources comprises at least one processor resource, memory resources, storage resources and network resources, wherein the user value score for each respective current user represents a degree of importance the each respective current user is to the service that the application corresponds to, wherein when the particular user is a prospective user, customizing of the resource allocation to the application is further based on the likelihood that the prospective user will convert to the paying customer, wherein the resource usage of the application is selected from a group consisting of processor usage, memory usage, storage usage, and network usage, and wherein the processor further executes the program instructions to:
• identify a classification for the application based on the resource usage of the application and the features of the application, wherein the classification represents an application class, category or type corresponding to the application.

A computer program product for customizing computing resource allocation based on machine learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• evaluating a plurality of current users of a service based on attributes of each respective current user in the plurality of current users;
• generating a user value score for each respective current user in the plurality of current users based on the attributes of each respective current user;
• clustering the plurality of current users into a plurality of user groups based on the user value score of each respective current user in the plurality of current users;
• predicting, using the machine learning, a likelihood of each prospective user in a plurality of prospective users becoming a paying customer based on collected behavior data corresponding to each prospective user;
• predicting, using the machine learning, a likelihood of an application corresponding to the service and associated with a particular user will misbehave based on resource usage of the application and features of the application;
• customizing resource allocation to the application corresponding to the service and associated with the particular user based on the likelihood that the application will misbehave and whether the particular user is a current user or a prospective user;
• and
• selecting the particular user from one of the plurality of current users and the plurality of prospective users, wherein the customized resource allocation represents a customized allocation of cloud resources to the application, and the cloud resources comprises at least one processor resource, memory resources, storage resources and network resources, wherein the user value score for each respective current user represents a degree of importance the each respective current user is to the service that the application corresponds to, wherein when the particular user is a prospective user, the customizing of the resource allocation to the application is further based on the likelihood that the prospective user will convert to the paying customer, wherein the resource usage of the application is selected from a group consisting of processor usage, memory usage, storage usage, and network usage, and further comprising:
• identifying, using the machine learning, a classification for the application based on the resource usage of the application and the features of the application, wherein the classification represents an application class, category or type corresponding to the application.