Abstract: An asset stored on a storage device of a computer system is detected to be omitted from an asset-specific access policy. Using a utilization history of the asset, a criticality of the asset is scored, generating a first criticality score for the asset. Using the first criticality score, the asset is classified into a policy category. A selected access policy selected from a set of access policies assigned to a set of assets in the policy category is applied to the asset, the selected access policy specifying an access restriction of the asset.

Abstract: An asset stored on a storage device of a computer system is detected to be omitted from an asset-specific access policy. Using a utilization history of the asset, a criticality of the asset is scored, generating a first criticality score for the asset. Using the first criticality score, the asset is classified into a policy category. A selected access policy selected from a set of access policies assigned to a set of assets in the policy category is applied to the asset, the selected access policy specifying an access restriction of the asset.

Abstract: Natural language contractual geographic resource restrictions (CGRR) are converted by computer into a set of machine logic based rule(s), and then the set of machine logic based rule(s) is used to control access to a set of computing resources in accordance with the CGRR in the operative contract. In some embodiments, the CGRR restrict access based on the geography of the prospective user, or customer, of the computing resources.

Abstract: Determining microservice placement to minimize response time in a network is provided. A set of resources utilized by a microservice during operation is identified based on inspecting a configuration file and code of the microservice. A deployed location within the network of each resource is determined. A relative weight value is assigned to each resource indicating a relative significance of each resource to the microservice during operation. A weighted-average response time is determined for each data center in a set of possible data centers to deploy the microservice based on the relative weight value assigned to each resource and a determined distance value measured by response time between the deployed location of each resource and each possible data center in the set. A data center in the set of possible data centers is selected to deploy the microservice based on the data center having a lowest determined weighted-average response time.

Abstract: Determining microservice placement to minimize response time in a network is provided. A set of resources utilized by a microservice during operation is identified based on inspecting a configuration file and code of the microservice. A deployed location within the network of each resource is determined. A relative weight value is assigned to each resource indicating a relative significance of each resource to the microservice during operation. A weighted-average response time is determined for each data center in a set of possible data centers to deploy the microservice based on the relative weight value assigned to each resource and a determined distance value measured by response time between the deployed location of each resource and each possible data center in the set. A data center in the set of possible data centers is selected to deploy the microservice based on the data center having a lowest determined weighted-average response time.

Abstract: Natural language contractual geographic resource restrictions (CGRR) are converted by computer into a set of machine logic based rule(s), and then the set of machine logic based rule(s) is used to control access to a set of computing resources in accordance with the CGRR in the operative contract. In some embodiments, the CGRR restrict access based on the geography of the prospective user, or customer, of the computing resources.

Abstract: A method selects content based on a learning relevancy targeted to specific recipients. One or more processors extract semantic features, which provide meanings of concepts, from each content asset from a plurality of content assets. The processor(s) utilize a clustering algorithm to group entries in the content assets based on the semantic features in order to form hierarchical consolidated entries for the semantic features, where each hierarchical consolidated entry is associated with one of the semantic features. The processor(s) then provide a representation of each of the hierarchical consolidated entries based on a target audience criteria.