Abstract: Electric vehicle battery monitoring systems and methods are disclosed herein. An example method includes displaying a state of charge for a battery of an electric vehicle on a display screen of the electric vehicle, determining a frequency of how often a user views the state of charge, inferring that the user has anxiety over a range for the electric vehicle based on the frequency, and presenting the user with options to extend the state of charge.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: According to some embodiments, an automated provisioning system may receive a customer demand associated with an application to be executed in a cloud-based computing environment. The automated provisioning system may include a process allocator to communicate with Virtual Machine (“VM”) and container provisioners and determine cluster data. A machine learning based microservice setup platform, coupled to the automated provisioning system, may receive the cluster data and information about the customer demand. The machine learning based microservice setup platform may then execute policy rules based on the cluster data (and information about the customer demand) and generate a recommendation for the customer demand. The automated provisioning system may then assign the customer demand to one of a VM-based infrastructure and a container-based infrastructure in accordance with the recommendation generated by the machine learning based microservice setup platform.

Abstract: According to some embodiments, a centralized resource provisioning system may associated with a plurality of end-user applications in a cloud-based computing environment. The centralized resource provisioning system may include a policy decision maker that generates a centralized recommendation for a computing resource of a first end-user application. An application decision maker may be associated with the first end-user application and generate a decentralized recommendation for the computing resource of the first end-user application. A machine controller of the centralized resource provisioning system may then arrange to adjust the computing resource for the first end-user application when both the centralized recommendation and the decentralized recommendation indicate that the adjustment is appropriate.

Abstract: According to some embodiments, an automated provisioning system may receive a customer demand associated with an application to be executed in a cloud-based computing environment. The automated provisioning system may include a process allocator to communicate with Virtual Machine (“VM”) and container provisioners and determine cluster data. A machine learning based microservice setup platform, coupled to the automated provisioning system, may receive the cluster data and information about the customer demand. The machine learning based microservice setup platform may then execute policy rules based on the cluster data (and information about the customer demand) and generate a recommendation for the customer demand. The automated provisioning system may then assign the customer demand to one of a VM-based infrastructure and a container-based infrastructure in accordance with the recommendation generated by the machine learning based microservice setup platform.